Upcoming Talks

• April 17 — Agentic AI: Don’t Panic! …But Be Prepared, Tulane University, New Orleans

• April 21 — AI Catalyst, O’Reilly

• May 11-14 — Web Summit, Vancouver

• October 20 — AI Catalyst, O’Reilly

Past Talks

2026

• February 13 — The Moltbook Phenomenon: OpenClaw Unleashed, SuperDataScience Podcast #966

• February 10 — From PhD Side Project to $500M ARR: Will Falcon’s PyTorch Lightning Story, SuperDataScience Podcast #965

• February 6 — In Case You Missed It in January 2026, SuperDataScience Podcast #964

• February 3 — Reinforcement Learning for Agents, with Amazon AGI Labs’ Antje Barth, SuperDataScience Podcast #963

• January 30 — Wharton Prof Ethan Mollick on Why Your AI Strategy Is Already Obsolete, SuperDataScience Podcast #962

• January 27 — Lightning AI Fireside Chat feat. Will Falcon and Kyunghyun Cho, Global AI Frontier Lab, Brooklyn NY

• January 27 — Distributed Artificial Superintelligence, with Dr. Vijoy Pandey, SuperDataScience Podcast #961

• January 24 — In Case You Missed It in December 2025, SuperDataScience Podcast #960

• January 20 — Building Agents 101: Design Patterns, Evals and Optimization (with Sinan Ozdemir), SuperDataScience Podcast #959

• January 16 — Without Trusted Context, Agents are Stupid (featuring Salesforce’s Rahul Auradkar), SuperDataScience Podcast #958

• January 13 — How AI Agents Are Automating Enterprise Data Operations, with Ashwin Rajeeva, SuperDataScience Podcast #957

• January 9 — From Agent Demo to Enterprise Product (with Ease!) feat. Salesforce’s Tyler Carlson, SuperDataScience Podcast #956

• January 6 — Nested Learning, Spatial Intelligence and the AI Trends of 2026, with Sadie St. Lawrence, SuperDataScience Podcast #955

• January 2 — Recap of 2025 and Wishing You a Wonderful 2026, SuperDataScience Podcast #954

2025

• December 30 — Beyond “Agent Washing”: AI Systems That Actually Deliver ROI, with Dell’s Global CTO John Roese, SuperDataScience Podcast #953

• December 26 — How to Avoid Burnout and Get Promoted, with “The Fit Data Scientist” Penelope Lafeuille, SuperDataScience Podcast #952

• December 23 — Context Engineering, Multiplayer AI and Effective Search, with Dropbox’s Josh Clemm, SuperDataScience Podcast #951

• December 19 — Happy Holidays from All of Us at the SuperDataScience Podcast, SuperDataScience Podcast #950

• December 16 — Why AI Keeps Failing Society, with Stanford professor Alex “Sandy” Pentland, SuperDataScience Podcast #949

• December 12 — In Case You Missed It in November 2025, SuperDataScience Podcast #948

• December 9 — How to Get Hired at Top Firms like Netflix and Spotify, with Jeff Li, SuperDataScience Podcast #947

• December 5 — How Robotaxis Are Transforming Cities, SuperDataScience Podcast #946

• December 2 — AI is a Joke, with Joel Beasley, SuperDataScience Podcast #945

• November 28 — Gemini 3 Pro: Google’s Back on Top, SuperDataScience Podcast #944

• November 25 — Why 95% of AI Projects Fail and How to Be the 5% with Jon Krohn, Signal To Noise Podcast Ep. #7

• November 25 — Creative Machines: AI in Music and Art, with Prof. Maya Ackerman, SuperDataScience Podcast #943

• November 21 — NYU AI Masterclass, New York

• November 21— Odds of AGI by 2040? LEAP Expert Forecasts and Workforce Implications, SuperDataScience Podcast #942

• November 19 — AI Collective NYC Demo Night, New York

• November 18 — Multi-Agent Human Societies, with Dr. Vijoy Pandey , SuperDataScience Podcast #941

• November 18 — AI Catalyst: Enterprise Agent Deployments, O’Reilly

• November 14 — In Case You Missed It in October 2025, SuperDataScience Podcast #940

• November 13 — Mentoring startups on effective scaling, Web Summit, Lisbon

• November 12 — Intro to Agentic AI for Software Developers, Web Summit, Lisbon [slides]

• November 11 (morning) — Hosting "Beyond the Stage" Meetup for founders and execs on successful Agentic A.I. projects, Web Summit, Lisbon

• November 11 (afternoon) — Judging Startup Pitch Competition, Web Summit, Lisbon

• November 11 — Mixture-of-Experts and State-Space Models on Edge Devices, with Tyler Cox and Shirish Gupta, SuperDataScience Podcast #939

• November 7 — Frontier AI Agents for Data Science, with Sphinx’s Rohan Kodialam, SuperDataScience Podcast #938

• November 4 — How to Design AI-First Products, with Marc Dupuis, SuperDataScience Podcast #937

• October 31 — LLMs Are Delighted to Help Phishing Scams, SuperDataScience Podcast #936

• October 28 — Global Issues Accelerated by AI (with Solutions), feat. Stephanie Hare, SuperDataScience Podcast #935

• October 24 — Is AI Replacing Junior Workers?, SuperDataScience Podcast #934

• October 23 — Unlocking Data with AI, Bessemer Venture Partners, New York

• October 21 — Future-Proofing Your Career in the AI Era, feat. Sheamus McGovern, SuperDataScience Podcast #933

• October 17 — Should You Build or Buy Your AI Solution? With Larissa Schneider, SuperDataScience Podcast #932

• October 14 — Boost Your Profits with Mathematical Optimization, feat. Jerry Yurchisin, SuperDataScience Podcast #931

• October 10 — In Case You Missed It in September 2025, SuperDataScience Podcast #930

• October 8 — Guest Co-Host of Last Week in A.I., Episode #222

• October 8 — SEMICON West, Phoenix, Arizona

• October 7 — Dragon Hatchling: The Missing Link Between Transformers and the Brain, with Adrian Kosowski, SuperDataScience Podcast #929

• October 7 — ScaleUp:AI, New York

• October 3 — The “Lethal Trifecta”: Can AI Agents Ever Be Safe?, SuperDataScience Podcast #928

• September 30 — Automating Code Review with AI, feat. CodeRabbit’s David Loker, SuperDataScience Podcast #927

• September 26 — AI is Disrupting the Legal Industry: Are Paralegals Doomed?, SuperDataScience Podcast #926

• September 23 — AI, Automation and the Future of Work, with Oxford’s Prof. Carl Benedikt Frey, SuperDataScience Podcast #925

• September 19 — 95% of Enterprise AI Projects Fail (Per MIT Research), SuperDataScience Podcast #924

• September 16 — Graph Algorithms, GraphRAG and Causal Graphs, with Graph Guru Amy Hodler, SuperDataScience Podcast #923

• September 12 — AI for Manufacturing and Industry, with Hugo Dozois-Caouette, SuperDataScience Podcast #922

• September 12 — LLMs, Agentic AI & Blackmail with Jon Krohn, Modern CTO Podcast #873

• September 9 — NPUs vs GPUs vs CPUs for Local AI Workloads, with Dell’s Ish Shah and Shirish Gupta, SuperDataScience Podcast #921

• September 5 — In Case You Missed It in August 2025, SuperDataScience Podcast #920

• September 2 — Hopes and Fears of AGI, with All-Time Bestselling ML Author Aurélien Géron, SuperDataScience Podcast #919

• August 29 — Multi-Agent Systems with CrewAI, SuperDataScience Podcast #918

• August 26 — 8 Steps to Becoming an AI Engineer, with Kirill Eremenko, SuperDataScience Podcast #917

• August 22 — The 5 Key GPT-5 Takeaways, SuperDataScience Podcast #916

• August 19 — How to Jailbreak LLMs (and How to Prevent It), with Michelle Yi, SuperDataScience Podcast #915

• August 15 — Data Lakes 101 (and Why They’re Key for AI Models), with Oz Katz, SuperDataScience Podcast #914

• August 13 — Intro to Agentic AI and MCP, University of Auckland, New Zealand [slides]

• August 12 — LLM Pre-Training and Post-Training 101, with Julien Launay, SuperDataScience Podcast #913

• August 8 — In Case You Missed It in July 2025, SuperDataScience Podcast #912

• August 5 — The Future of Python Notebooks is Here, with Marimo’s Dr. Akshay Agrawal, SuperDataScience Podcast #911

• August 1 — AI is Disrupting Journalism: The Good, The Bad and The Opportunity, SuperDataScience Podcast #910

• July 29 — Intro to Model Context Protocol (MCP), Lightning AI Office, San Francisco [slides]

• July 29 — Causal AI, with Dr. Robert Usazuwa Ness, SuperDataScience Podcast #909

• July 25 — AI Agents Blackmail Humans 96% of the Time (Agentic Misalignment), SuperDataScience Podcast #908

• July 24 — Intro to Model Context Protocol (MCP), Lightning AI Office, New York [slides]

• July 23 — Guest Co-Host of Last Week in A.I., Episode #217

• July 22 — Neuroscience, AI and the Limitations of LLMs, with Dr. Zohar Bronfman, SuperDataScience Podcast #907

• July 18 — How Prof. Jason Corso Solved Computer Vision’s Data Problem, SuperDataScience Podcast #906

• July 15 — Why RAG Makes LLMs Less Safe (And How to Fix It), with Bloomberg’s Dr. Sebastian Gehrmann, SuperDataScience Podcast #905

• July 11 — A.I. is Disrupting the Entire Advertising Industry, SuperDataScience Podcast #904

• July 8 — LLM Benchmarks Are Lying to You (And What to Do Instead), with Sinan Ozdemir, SuperDataScience Podcast #903

• July 4 — In Case You Missed It in June 2025, SuperDataScience Podcast #902

• July 1 — Automating Legal Work with Data-Centric ML (feat. Lilith Bat-Leah), SuperDataScience Podcast #901

• June 27 — 95-Year-Old Annie on How to Stay Healthy and Happy, SuperDataScience Podcast #900

• June 24 — Landing $200k+ AI Roles: Real Cases from the SuperDataScience Community, with Kirill Eremenko, SuperDataScience Podcast #899

• June 20 — My Four-Hour Agentic AI Workshop is Live and 100% Free, SuperDataScience Podcast #898

• June 16 — How to Enable Enterprise AI Transformation, with Strategy Consultant Diane H, SuperDataScience Podcast #897

• June 13 — AI (Probably) Isn’t Taking Your Job (At Least Anytime Soon), SuperDataScience Podcast #896

• June 10 — The Future of Enterprise AI: Investor Shaun Johnson Reveals What Actually Works, SuperDataScience Podcast #895

• June 6 — In Case You Missed It in May 2025, SuperDataScience Podcast #894

• June 3 — Emceeing “AI Agents Hands-on Workshop”, Lightning AI Headquarters, New York

• June 3 — How to Jumpstart Your Data Career (by Applying Like a Scientist), with Avery Smith, SuperDataScience Podcast #893

• May 30 — We’re In The AI “Trough of Disillusionment” (and that’s Great!), SuperDataScience Podcast #892

• May 27 — Conversational AI is Overhauling Data Analytics, with Martin Brunthaler, SuperDataScience Podcast #891

• May 23 — The “State of AI” Report 2025, SuperDataScience Podcast #890

• May 20 — AI-Powered Virtual Reality: The Future of Education and Entertainment, with Mary Spio, SuperDataScience Podcast #889

• May 16 — Teams of Agents: The Next Frontier in AI Collaboration, with Mike Pell, SuperDataScience Podcast #888

• May 14 — Agentic AI in Action: Build Autonomous, Multi-Agent Systems Hands-On in Python (Full-Day Training with Ed Donner), Open Data Science Conference (ODSC) East, Boston [code] [slides]

• May 13 — Multi-Agent Teams, Quantum Computing and the Future of Work, with Dell’s Global CTO John Roese, SuperDataScience Podcast #887

• May 9 — In Case You Missed It in April 2025, SuperDataScience Podcast #886

• May 6 — Python Polars: The Definitive Guide, with Jeroen Janssens and Thijs Nieuwdorp, SuperDataScience Podcast #885

• May 2 — Model Context Protocol (MCP) and Why Everyone’s Talking About It, SuperDataScience Podcast #884

• April 29 — Blackwell GPUs Are Now Available at Your Desk, with Sama Bali and Logan Lawler, SuperDataScience Podcast #883

• April 26 — Panel Host, AI Masterclass, New York University

• April 25 — 40x Hotter Than the Sun: The ASML Machines That Make AI Chips, SuperDataScience Podcast #882

• April 24 — Emceeing & Podcast Episode with guest Mike Pell, Artist and the Machine “AI + Creativity” Summit, New York

• April 22 — Beyond GPUs: The Power of Custom AI Accelerators, with Emily Webber, SuperDataScience Podcast #881

• April 19 — Saving Yourself (and The World) with A.I., New York University’s Stern School of Business [slides]

• April 18 — Manus, DeepSeek and China’s AI Boom, SuperDataScience Podcast #880

• April 15 — Serverless, Parallel, and AI-Assisted: The Future of Data Science is Here, with Zerve’s Dr. Greg Michaelson, SuperDataScience Podcast #879

• April 11 — In Case You Missed It in March 2025, SuperDataScience Podcast #878

• April 9 — The Neural Processing Units Bringing AI to PCs, with Shirish Gupta, SuperDataScience Podcast #877

• April 7 — Hugging Face’s smolagents: Agentic AI in Python Made Easy, SuperDataScience Podcast #876

• April 4 — The AI Agent Market: Investors Weigh In, CrewAI Agent Summit, New York

• April 1 — How Semiconductors Are Made (And Fuel the AI Boom), with Kai Beckmann, SuperDataScience Podcast #875

• March 28 — How AI is Transforming Baseball (with Lessons For All of Us), SuperDataScience Podcast #874

• March 25 — Become Your Best Self Through AI Augmentation — feat. Natalie Monbiot, SuperDataScience Podcast #873

• March 21 — Microsoft’s “Majorana 1” Chip Brings Quantum ML Closer, SuperDataScience Podcast #872

• March 19 — Saving Yourself (and The World) with A.I., rvatech Data + AI Summit, Richmond, Virginia [slides] [video]

• March 18 — NoSQL Is Ideal for AI Applications, with MongoDB’s Richmond Alake, SuperDataScience Podcast #871

• March 14 — OpenAI’s “Deep Research”: Get Days of Human Work Done in Minutes, SuperDataScience Podcast #870

• March 11 — AI Should Make Humans Wiser (But It Isn’t), with Varun Godbole, SuperDataScience Podcast #869

• March 7 — In Case You Missed It in February 2025, SuperDataScience Podcast #868

• March 4 — LLMs and Agents Are Overhyped, with Dr. Andriy Burkov, SuperDataScience Podcast #867

• February 28 — Bringing Back Extinct Animals like the Wooly Mammoth and Dodo Bird, SuperDataScience Podcast #866

• February 25 — How to Grow (and Sell) a Data Science Consultancy, with Cal Al-Dhubaib, SuperDataScience Podcast #865

• February 22 — OpenAI’s o3-mini: SOTA reasoning and exponentially cheaper, SuperDataScience Podcast #864

• February 18 — TabPFN: Deep Learning for Tabular Data (That Actually Works!), with Prof. Frank Hutter, SuperDataScience Podcast #863

• February 14 — In Case You Missed It in January 2025, SuperDataScience Podcast #862

• February 11 — From Pro Athlete to Data Engineer: Colleen Fotsch’s Inspiring Journey, SuperDataScience Podcast #861

• February 7 — DeepSeek R1: SOTA Reasoning at 1% of the Cost, SuperDataScience Podcast #860

• February 4 — BAML: The Programming Language for AI, with Vaibhav Gupta, SuperDataScience Podcast #859

• January 31 — Are You The Account Executive We’re Looking For?, SuperDataScience Podcast #858

• January 28 — How to Ensure AI Agents Are Accurate and Reliable, with Brooke Hopkins, SuperDataScience Podcast #857

• January 24 — The Fastest-Growing Jobs Are AI Jobs, SuperDataScience Podcast #856

• January 21 — Exponential Views on AI and Humanity’s Greatest Challenges, with Azeem Azhar, SuperDataScience Podcast #855

• January 17 — The Six Epochs of Intelligence Evolution, SuperDataScience Podcast #854

• January 14 — Generative AI for Business, with Kirill Eremenko and Hadelin de Ponteves, SuperDataScience Podcast #853

• January 10 — In Case You Missed It in December 2024, SuperDataScience Podcast #852

• January 7 — Quantum ML: Real-World Applications Today, with Dr. Florian Neukart, SuperDataScience Podcast #851

• January 3 — Continuous Calendar for 2025, SuperDataScience Podcast #850

2024

• December 31 — 2025 AI and Data Science Predictions, with Sadie St. Lawrence, SuperDataScience Podcast #849

• December 27 — Happy Holidays from the SuperDataScience Podcast, SuperDataScience Podcast #848

• December 24 — AI Engineering 101, with Ed Donner, SuperDataScience Podcast #847

• December 20 — Making Enterprise Data Ready for AI, with Anu Jain and Mahesh Kumar, SuperDataScience Podcast #846

• December 17 — Tech is Our New Religion And It Needs Reformation, with Greg Epstein, SuperDataScience Podcast #845

• December 13 — In Case You Missed It in November 2024, SuperDataScience Podcast #844

• December 10 — Safe, Fast and Efficient AI, with Protopia’s Dr. Eiman Ebrahimi, SuperDataScience Podcast #843

• December 6 — Flexible AI Deployments Are Critical, with Chris Bennett and Joseph Balsamo, SuperDataScience Podcast #842

• December 4 — Agentic Artificial Intelligence: Exploring AI Agents that Plan, Reason, and Act, O’Reilly

• December 3 — Andrew Ng on AI Vision, Agents and Business Value, SuperDataScience Podcast #841

• November 29 — Delicate Viticultural Robotics, SuperDataScience Podcast #840

• November 27 — Double Your Data Salary in 11 Months, with Jess Ramos, SuperDataScience Podcast #839

• November 25 — Getting Value from A.I., Graduate Data Science Course, NYU Stern [slides]

• November 22 — Consciousness and Machines, with Jennifer K. Hill, SuperDataScience Podcast #838

• November 20 — Optimization, O’Reilly [slides] [code]

• November 19 — Career Success in the AI Era, with Deepali Vyas, SuperDataScience Podcast #837

• November 16 — How to Become Happier, with Dr. Nat Ware, SuperDataScience Podcast #836

• November 14 — Hosting the “Developer Summit” stage, Web Summit, Lisbon, Portugal [video]

• November 12 — Live interview with Andrew Ng, ScaleUp: AI, New York

• November 12 — AI Systems as Productivity Engines, with You.com’s Bryan McCann, SuperDataScience Podcast #835

• November 8 — In Case You Missed It in October 2024, SuperDataScience Podcast #834

• November 5 — The 10 Reasons AI Projects Fail, with Dr. Martin Goodson, SuperDataScience Podcast #833

• November 1 — The Anthropic CEO’s Techno-Utopia, SuperDataScience Podcast #832

• October 29 — PyTorch Lightning, Lit-Serve and Lightning Studios, with Dr. Luca Antiga, SuperDataScience Podcast #831

• October 25 — The “A.I.” Nobel Prizes (in Physics and Chemistry??), SuperDataScience Podcast #830

• October 23 — Data Structures and Algorithms, Level II: Hashing, Trees, Graphs, O’Reilly [code] [slides]

• October 22 — Neuroscience Fueled by ML, with Prof. Bradley Voytek, SuperDataScience Podcast #829

• October 18 — Are “Citizen Data Scientists” A Myth? With Keith McCormick, SuperDataScience Podcast #828

• October 15 — Polars: Past, Present and Future, with Polars Creator Ritchie Vink, SuperDataScience Podcast #827

• October 11 — In Case You Missed It in September 2024, SuperDataScience Podcast #826

• October 8 — Data Contracts: The Key to Data Quality, with Chad Sanderson, SuperDataScience Podcast #825

• October 4 — Llama 3.2: Open-Source Edge and Multimodal LLMs, SuperDataScience Podcast #824

• October 1 — Virtual Humans and AI Clones, with Natalie Monbiot, SuperDataScience Podcast #823

• September 27 —NotebookLM: Jaw-Dropping Podcast Episodes Generated About Your Documents, SuperDataScience Podcast #822

• September 25 — Intro to Data Structures and Algorithms, O’Reilly [code] [slides]

• September 24 — The Skills You Need to Be an Effective Data Scientist, with Marck Vaisman, SuperDataScience Podcast #821

• September 20 — OpenAI's o1 "Strawberry" Models, SuperDataScience Podcast #820

• September 17 — PyTorch: From Zero to Hero, with Luka Anicin, SuperDataScience Podcast #819

• September 13 — In Case You Missed It in August 2024, SuperDataScience Podcast #818

• September 11 — Statistics II: Regression and Bayesian, O’Reilly [code] [slides]

• September 10 — The Positron IDE, Tidy NLP and MLOps with Dr. Julia Silge, SuperDataScience Podcast #817

• September 6 — Explaining AGI to a 94-Year-Old, SuperDataScience Podcast #816

• September 3 — DataFrame Operations 100x Faster than Pandas, with Marco Gorelli, SuperDataScience Podcast #815

• August 30 — Summer Reflections, SuperDataScience Podcast #814

• August 20 — Solving Business Problems Optimally with Data, with Jerry Yurchisin, SuperDataScience Podcast #813

• August 23 — The AI Scientist: Towards Fully Automated, Open-Ended Scientific Discovery, SuperDataScience Podcast #812

• August 21 — Intro to Statistics, O’Reilly [code] [slides]

• August 20 — Scaling Data Science Teams Effectively, with Nick Elprin, SuperDataScience Podcast #811

• August 16 — The Five Levels of Self-Driving Cars, SuperDataScience Podcast #810

• August 13 — Agentic AI, with Shingai Manjengwa, SuperDataScience Podcast #809

• August 11 — Guest Co-Host of Last Week in A.I., Episode #177

• August 9 — In Case You Missed It in July 2024, SuperDataScience Podcast #808

• August 7 — Probability II and Information Theory, O’Reilly [slides] [code]

• August 6 — Superintelligence and the Six Singularities, with Dr. Daniel Hulme, SuperDataScience Podcast #807

• August 2 — Llama 3.1 405B: The First Open-Source Frontier LLM, SuperDataScience Podcast #806

• July 30 — How to Be a Supercommunicator, with Charles Duhigg, SuperDataScience Podcast #805

• July 26 — AI x Solar Power = Abundant Energy, SuperDataScience Podcast #804

• July 23 — How to Thrive in Your (Data Science) Career, with Daliana Liu, SuperDataScience Podcast #803

• July 23 — Intro to Probability, O’Reilly [slides] [code]

• July 19 — In Case You Missed It in June 2024, SuperDataScience Podcast #802

• July 17 — Getting a Return on A.I. Investment, “Data Science & Machine Learning” course, Columbia University [slides]

• July 16 — Merged LLMs Are Smaller And More Capable, with Arcee AI’s Mark McQuade and Charles Goddard, SuperDataScience Podcast #801

• July 12 — A Transformative Century of Technological Progress, with Annie P., SuperDataScience Podcast #800

• July 10 — Calculus for Machine Learning, Level IV: Gradients & Integrals, O’Reilly [slides] [code]

• July 9 — AGI Could Be Near: Dystopian and Utopian Implications, with Dr. Andrey Kurenkov, SuperDataScience Podcast #799

• July 5 — Claude 3.5 Sonnet: Frontier Capabilities & Slick New “Artifacts” UI, SuperDataScience Podcast #798

• July 2 — Deep Learning Classics and Trends, with Dr. Rosanne Liu, SuperDataScience Podcast #797

• June 28 — Earth’s Coming Population Collapse and How AI Can Help, with Simon Kuestenmacher, SuperDataScience Podcast #796

• June 26 — Calculus for Machine Learning, Level III: Partial Derivatives, O’Reilly [slides] [code]

• June 25 — Fast-Evolving Data and AI Regulatory Frameworks, with Dr. Gina Guillaume-Joseph, SuperDataScience Podcast #795

• June 21 — Exciting (and Frightening!) Trends in Open-Source AI, SuperDataScience Podcast #794

• June 20 — Content Creation Meets the Singularity, Keynote at Collision, Toronto [slides]

• June 18 — Bayesian Methods and Applications, with Alexandre Andorra, SuperDataScience Podcast #793

• June 14 — In Case You Missed It in May 2024, SuperDataScience Podcast #792

• June 11 — Reinforcement Learning from Human Feedback (RLHF), with Dr. Nathan Lambert, SuperDataScience Podcast #791

• June 7 — Open-Source Libraries for Data Science at the New York R Conference, SuperDataScience Podcast #790

• June 5 — Calculus for Machine Learning, Level II: Automatic Differentiation, O’Reilly [slides] [code]

• June 4 — ML for Wind-Powered Energy Generation, with Dr. Jason Yosinski, SuperDataScience Podcast #789

• May 31 — Multi-Agent Systems: How Teams of LLMs Excel at Complex Tasks, SuperDataScience Podcast #788

• May 28 — MLOps: The Job and The Key Tools, with Demetrios Brinkmann, SuperDataScience Podcast #787

• May 24 — The Six Keys to Data Scientists’ Success, with Kirill Eremenko, SuperDataScience Podcast #786

• May 23 — Training and Deploying Open-Source LLMs, ODSC Ai X Podcast #48

• May 22 — Calculus for Machine Learning: Intro, O’Reilly [slides] [code]

• May 21 — Math, Quantum ML and Language Embeddings, with Dr. Luis Serrano, SuperDataScience Podcast #785

• May 17 — Host of the 10-Year Retrospective Panel with guests Drew Conway, Emily Zabor, JD Long and Jared Lander, New York R Conference

• May 17 — Aligning Large Language Models, with Sinan Ozdemir, SuperDataScience Podcast #784

• May 14 — Generative A.I. for Solar Power Installation, with Navdeep Martin, SuperDataScience Podcast #783

• May 11 — Panel Member for A.I. Master Class for Board Members and C-Level Execs, New York University

• May 10 — In Case You Missed It in April 2024, SuperDataScience Podcast #782

• May 8 — Linear Algebra for Machine Learning, Level III: Eigenvectors, O’Reilly [slides] [code]

• May 7 — Ensuring Successful Enterprise AI Deployments, with Sol Rashidi, SuperDataScience Podcast #781

• May 4 — How to Become a Data Scientist, with Dr. Adam Ross Nelson, SuperDataScience Podcast #780

• April 30 — The Tidyverse of Essential R Libraries and their Python Analogues, with Dr. Hadley Wickham, SuperDataScience Podcast #779

• April 26 — Mixtral 8x22B: SOTA Open-Source LLM Capabilities at a Fraction of the Compute, SuperDataScience Podcast #778

• April 24 — NLP with Open-Source LLMs: From Training to Deployment with Hugging Face and PyTorch Lightning, Open Data Science Conference East, Boston [slides] [code]

• April 23 — Deep Learning with PyTorch and TensorFlow, Open Data Science Conference East, Boston [slides] [code] [handout]

• April 23 — Generative AI in Practice, with Bernard Marr, SuperDataScience Podcast #777

• April 19 — Deep Utopia: AI Could Solve All Human Problems in Our Lifetime, SuperDataScience Podcast #776

• April 17 — Linear Algebra for Machine Learning, Level II: Matrix Tensors, O’Reilly [code] [slides 1] [slides 2]

• April 16 — What will humans do when machines are vastly more intelligent? With Aleksa Gordić, SuperDataScience Podcast #775

• April 14 — RFM-1 Gives Robots Human-like Reasoning and Conversation Abilities, SuperDataScience Podcast #774

• April 10 — “Generative A.I. with LLMs” Keynote, Data Universe, New York [slides]

• April 9 — Deep Reinforcement Learning for Maximizing Profits, with Prof. Barrett Thomas, SuperDataScience Podcast #773

• April 5 — In Case You Missed It in March 2024, SuperDataScience Podcast #772

• April 3 — Linear Algebra for Machine Learning: Intro, O’Reilly [slides] [code]

• April 2 — Gradient Boosting: XGBoost, LightGBM and CatBoost, with Kirill Eremenko, SuperDataScience Podcast #771

• March 30 — The Neuroscientific Guide to Confidence, SuperDataScience Podcast #770

• March 26 — Generative AI for Medicine, with Prof. Zack Lipton, SuperDataScience Podcast #769

• March 22 — Is Claude 3 Better than GPT-4?, SuperDataScience Podcast #768

• March 21 — Generative A.I. with Large Language Models, The University of Iowa [slides] [code]

• March 19 — Open-Source LLM Libraries and Techniques, with Dr. Sebastian Raschka, SuperDataScience Podcast #767

• March 15 — Vonnegut's Player Piano (1952): An Eerie Novel on the Current AI Revolution, SuperDataScience Podcast #766

• March 13 — Multiplying HR’s Impact with AI: What’s Possible and How to Start, Transform, Las Vegas

• March 12 — NumPy, SciPy and the Economics of Open-Source, with Dr. Travis Oliphant, SuperDataScience Podcast #765

• March 8 — The Top 10 Episodes of 2023, SuperDataScience Podcast #764

• March 5 — The Best A.I. Startup Opportunities, with venture capitalist Rudina Seseri, SuperDataScience Podcast #763

• March 1 — Gemini 1.5 Pro, the Million-Token-Context LLM, SuperDataScience Podcast #762

• February 27 — Gemini Ultra: How to Release an A.I. Product for Billions of Users, with Google’s Lisa Cohen, SuperDataScience Podcast #761

• February 23 — Humans Love A.I.-Crafted Beer, SuperDataScience Podcast #760

• February 20 — Full Encoder-Decoder Transformers Fully Explained, with Kirill Eremenko, SuperDataScience Podcast #759

• February 16 — The Mamba Architecture: Superior to Transformers in LLMs, SuperDataScience Podcast #758

• February 13 — How to Speak so You Blow Listeners’ Minds, with Cole Nussbaumer Knaflic, SuperDataScience Podcast #757

• February 9 — AlphaGeometry: AI is Suddenly as Capable as the Brightest Math Minds, SuperDataScience Podcast #756

• February 8 — Launch of Krohn&Borg A.I.-Generated Beer, Species X Brewery, Columbus, Ohio

• February 6 — Brewing Beer with A.I., with Beau Warren, SuperDataScience Podcast #755

• February 1 — A Code-Specialized LLM Will Realize AGI, with Jason Warner, SuperDataScience Podcast #754

• January 30 — Blend Any Programming Languages in Your ML Workflows, with Dr. Greg Michaelson, SuperDataScience Podcast #753

• January 26 — AI is Disadvantaging Job Applicants, But You Can Fight Back, SuperDataScience Podcast #752

• January 26 — Bloomberg Media Studios’ Creativity Speaker Series, New York [slides]

• January 24 — A.I. and the Future of Work, AI4Talent Conference

• January 23 — How to Found and Fund Your Own A.I. Startup, with Dr. Rasmus Rothe, SuperDataScience Podcast #751

• January 23 — Generative A.I. with Large Language Models, Numerical Analysis and Scientific Computing Seminar, University of Waterloo [slides]

• January 19 — How A.I. is Transforming Science, SuperDataScience Podcast #750

• January 16 — Data Science for Clean Energy, with Emily Pastewka, SuperDataScience Podcast #749

• January 12 — The Five Levels of AGI, SuperDataScience Podcast #748

• January 12 — Generative A.I. Myths & Reality in Talent Acquisition, Shally’s Alley

• January 9 — Technical Intro to Transformers and LLMs, with Kirill Eremenko, SuperDataScience Podcast #747

• January 9 — What Will Recruiters Do After AI Automates Everything, Tech Recruiter Podcast S03-Ep19

• January 5 — A Continuous Calendar for 2024, SuperDataScience Podcast #746

• January 2 — 2024 Data Science Trend Predictions, SuperDataScience Podcast #745

2023

• December 29 — To a Peaceful 2024, SuperDataScience Podcast #744

• December 26 — How to Integrate Generative A.I. Into Your Business, with Piotr Grudzień, SuperDataScience Podcast #743

• December 22 — Happy Holidays from All of Us, SuperDataScience Podcast #742

• December 19 — How to Visualize Data Effectively, with Prof. Alberto Cairo, SuperDataScience Podcast #741

• December 15 — Q*: OpenAI’s Rumored AGI Breakthrough, SuperDataScience Podcast #740

• December 12 — AI is Eating Biology and Chemistry, with Dr. Ingmar Schuster, SuperDataScience Podcast #739

• December 8 — Reinforcement Learning from Human Feedback for Generative A.I., Reinforcement Learning (ELEN E6885), Columbia University, New York [slides] [epsilon code] [all other code]

• December 8 — Engineering Biomaterials with Generative AI, with Dr. Pierre Salvy, SuperDataScience Podcast #738

• December 5 — Scikit-learn’s Past, Present and Future, with scikit-learn co-founder Dr. Gaël Varoquaux, SuperDataScience Podcast #737

• December 1 — How to Officially Certify your AI Model, with Jan Zawadzki, SuperDataScience Podcast #736

• November 28 —A.I. Product Management, with Google DeepMind's Head of Product, Mehdi Ghissassi, SuperDataScience Podcast #735

• November 27 — Getting Value from A.I., Data Science for Business course, New York University Stern MBA program [slides]

• November 23 —Humanoid Robot Soccer, with the Dutch RoboCup Team, SuperDataScience Podcast #734

• November 22 — Will A.I. Replace Recruiters and Automate Talent Acquisition?, The Resilient Recruiter Podcast #198

• November 21 — OpenAssistant: The Open-Source ChatGPT Alternative, with Dr. Yannic Kilcher, SuperDataScience Podcast #733

• November 17 — Data Science for Astronomy, with Dr. Daniela Huppenkothen, SuperDataScience Podcast #732

• November 14 — A.I. Agents Will Develop Their Own Distinct Culture, with Nell Watson, SuperDataScience Podcast #731

• November 10 — How GitHub Operationalizes AI for Teamwide Collaboration and Productivity, with GitHub COO Kyle Daigle, SuperDataScience Podcast #730

• November 8 — Building Commercially Successful LLM Applications, O’Reilly Conference [my slides] [Vin’s slides] [Caterina’s slides] [Krishnaram’s slides]

• November 7 — Universal Principles of Intelligence (Across Humans and Machines), with Prof. Blake Richards, SuperDataScience Podcast #729

• November 3 — Use Contrastive Search to get Human-Quality LLM Outputs, SuperDataScience Podcast #728

• October 31 — Unmasking A.I. Injustice, with Dr. Joy Buolamwini, SuperDataScience Podcast #727

• October 27 — Seven Factors for Successful Data Leadership, SuperDataScience Podcast #726

• October 27 — Interview with GitHub COO Kyle Daigle at ScaleUp:AI, New York

• October 24 —Neuroscience + Machine Learning, with Google DeepMind’s Dr. Kim Stachenfeld, SuperDataScience Podcast #725

• October 20 — Intro to Deep Learning, Reinforcement Learning (ELEN E6885), Columbia University, New York [slides] [handout] [code]

• October 20 — Decoding Speech from Raw Brain Activity, with Dr. David Moses, SuperDataScience Podcast #724

• October 17 — Mathematical Optimization, with Jerry Yurchisin, SuperDataScience Podcast #723

• October 13 —AI Emits Far Less Carbon Than Humans (Doing the Same Task), SuperDataScience Podcast #722

• October 10 — Quantum Machine Learning, with Dr. Amira Abbas, SuperDataScience Podcast #721

• October 6 — OpenAI’s DALL-E 3, Image Chat and Web Search, SuperDataScience Podcast #720

• October 3 — DALLE-3, YouAgent, Gemini, NExT-GPT, AI book labeling, Last Week in A.I. Podcast #138

• October 3 — Computational Mathematics and Fluid Dynamics, with Prof. Margot Gerritsen, SuperDataScience Podcast #719

• September 29 — ChatGPT Custom Instructions: A Major, Easy Hack for Data Scientists, SuperDataScience Podcast #718

• September 26 — Overcoming Adversaries with A.I. for Cybersecurity, with Dr. Dan Shiebler, SuperDataScience Podcast #717

• September 22 — Happiness and Life-Fulfillment Hacks, SuperDataScience Podcast #716

• September 19 — Make Better Decisions with Data, with Dr. Allen Downey, SuperDataScience Podcast #715

• September 20 — How He Became the Biggest Podcaster in Data Science (Jon Krohn), Ken's Nearest Neighbors #168

• September 15 — Using A.I. to Overcome Blindness and Thrive as a Data Scientist, SuperDataScience Podcast #714

• September 12 — Llama 2, Toolformer and BLOOM: Open-Source LLMs with Meta’s Dr. Thomas Scialom, SuperDataScience Podcast #713

• September 8 — Code Llama, SuperDataScience Podcast #712

• September 5 — Generative AI – A Deep Dive, The Recruiting Future Podcast #547

• September 5 — Image, Video and 3D-Model Generation from Natural Language, with Dr. Ajay Jain, SuperDataScience Podcast #711

• September 1 — LangChain: Create LLM Applications Easily in Python, SuperDataScience Podcast #710

• August 31 — Zortify Summer School: The Future of Work [slides]

• August 29 — Big A.I. R&D Risks Reap Big Societal Rewards, with Meta’s Dr. Laurens van der Maaten, SuperDataScience Podcast #709

• August 25 — ChatGPT Code Interpreter: 5 Hacks for Data Scientists, SuperDataScience Podcast #708

• August 24 —The Network Effect: What's Driving the New Golden Age of Transportation and Logistics?, The Network Effect

• August 22 — Vicuña, Gorilla, Chatbot Arena and Socially Beneficial LLMs, with Prof. Joey Gonzalez, SuperDataScience Podcast #707

• August 21 —What's Driving the New Golden Age of Transportation and Logistics?, The Network Effect

• August 18 — Large Language Model Leaderboards and Benchmarks, SuperDataScience Podcast #706

• August 15 — Feeding the World with ML-Powered Precision Agriculture, SuperDataScience Podcast #705

• August 11 — Jon’s “Generative A.I. with LLMs” Hands-on Training, SuperDataScience Podcast #704

• August 8 — How Data Happened: A History, with Columbia Prof. Chris Wiggins, SuperDataScience Podcast #703

• August 4 — LLaMA 2 — It’s Time to Upgrade your Open-Source LLM, SuperDataScience Podcast #702

• August 1 — Generative A.I. without the Privacy Risks (with Prof. Raluca Ada Popa), SuperDataScience Podcast #701

• July 28 — “The Dream of Life” by Alan Watts, SuperDataScience Podcast #700

• July 25 — Llama 2, Elon Musk’s xAI, WormGPT, LongLLaMA, AI apocalypse, actors on strike, Last Week in A.I. Podcast #130

• July 25 — The Modern Data Stack, with Harry Glaser, SuperDataScience Podcast #699

• July 21 — How Firms Can Actually Adopt A.I., with Rehgan Avon, SuperDataScience Podcast #698

• July 18 — The (Short) Path to Artificial General Intelligence, with Dr. Ben Goertzel, SuperDataScience Podcast #697

• July 14 — Brain-Computer Interfaces and Neural Decoding, with Prof. Bob Knight, SuperDataScience Podcast #696

• July 14 — SuperDataScience Live, New York R Conference

• July 11 — NLP with Transformers, feat. Hugging Face’s Lewis Tunstall, SuperDataScience Podcast #695

• July 7 — CatBoost: Powerful, efficient ML for large tabular datasets, SuperDataScience Podcast #694

• July 4 — YOLO-NAS: The State of the Art in Machine Vision, with Harpreet Sahota, SuperDataScience Podcast #693

• June 30 — Lossless LLM Weight Compression: Run Huge Models on a Single GPU, SuperDataScience Podcast #692

• June 27 — A.I. Accelerators: Hardware Specialized for Deep Learning, SuperDataScience Podcast #691

• June 23 — How to Catch and Fix Harmful Generative A.I. Output, SuperDataScience Podcast #690

• June 22 —Telehealth, VR and the Future of Healthcare, The Network Effect

• June 22 —Venturing into AI: Safe Solutions for Health, Education, and Climate, UNICEF Venture Fund Showcase

• June 20 — Observing LLMs in Production to Automatically Catch Issues, SuperDataScience Podcast #689

• June 16 — Six Reasons Why Building LLM Products Is Tricky, SuperDataScience Podcast #688

• June 13 — Generative Deep Learning, with David Foster, SuperDataScience Podcast #687

• June 9 — Open-Source “Responsible A.I.” Tools, with Ruth Yakubu, SuperDataScience Podcast #686

• June 6 — Tools for Building Real-Time Machine Learning Applications, with Richmond Alake, SuperDataScience Podcast #685

• June 2 — Get More Language Context out of your LLM, SuperDataScience Podcast #684

• May 31 — Contextual A.I. for Adapting to Adversaries, with Dr. Matar Haller, SuperDataScience Podcast #683

• May 26 — Business Intelligence Tools, with Mico Yuk, SuperDataScience Podcast #682

• May 23 — XGBoost: The Ultimate Classifier, with Matt Harrison, SuperDataScience Podcast #681

• May 19 — Automating Industrial Machines with Data Science and the Internet of Things (IoT), SuperDataScience Podcast #680

• May 16 — The A.I. and Machine Learning Landscape, with investor George Mathew, SuperDataScience Podcast #679

• May 2 — StableLM: Open-source “ChatGPT”-like LLMs you can fit on one GPU, SuperDataScience Podcast #678

• May 10 — “NLP with GPT-4 and other LLMs: From Training to Deployment with Hugging Face and PyTorch Lightning” Half-Day Training, ODSC East, Boston [slides] [code]

• May 9 — “Deep Learning with PyTorch and TensorFlow” Half-Day Training, ODSC East, Boston [slides] [code] [handout]

• May 9 — Digital Analytics with Avinash Kaushik, SuperDataScience Podcast #677

• May 5 — The Chinchilla Scaling Laws, SuperDataScience Podcast #676

• May 4 — “Unlocking the Full Potential of A.I.: How to Use ChatGPT in our Daily Lives”, St. Gallen Symposium, Switzerland

• May 3 — “Adapting Enterprise Leadership to the New A.I. Paradigm”, St. Gallen Symposium, Switzerland

• May 2 — Pandas for Data Analysis and Visualization, SuperDataScience Podcast #675

• April 28 — Parameter-Efficient Fine-Tuning of LLMs using LoRA (Low-Rank Adaptation), SuperDataScience Podcast #674

• April 25 — Taipy, the open-source Python application builder, SuperDataScience Podcast #673

• April 22 — “Getting Value from A.I.”, Data Science for Business course, New York University Stern MBA program [slides]

• April 21 — Open-source “ChatGPT”: Alpaca, Vicuña, GPT4All-J, and Dolly 2.0, SuperDataScience Podcast #672

• April 18 — Cloud Machine Learning, SuperDataScience Podcast #671

• April 14 — LLaMA: GPT-3 performance, 10x smaller, SuperDataScience Podcast #670

• April 11 — Streaming, reactive, real-time machine learning, SuperDataScience Podcast #669

• April 7 — GPT-4: Apocalyptic stepping stone?, SuperDataScience Podcast #668

• April 4 — Harnessing GPT-4 for your Commercial Advantage, SuperDataScience Podcast #667

• March 31 — GPT-4, SuperDataScience Podcast #666

• March 28 — How to be both socially impactful and financially successful in your data career, SuperDataScience Podcast #665

• March 24 —MIT Study: ChatGPT Dramatically Increases Productivity, SuperDataScience Podcast #664

• March 21 — Astonishing CICERO negotiates and builds trust with humans using natural language, SuperDataScience Podcast #663

• March 17 — The Most Popular SuperDataScience Podcast Episodes of 2022, SuperDataScience Podcast #662

• March 14 — Designing Machine Learning Systems, SuperDataScience Podcast #661

• March 10 — Five Ways to Use ChatGPT for Data Science, SuperDataScience Podcast #660

• March 7 — Open-Source Tools for Natural Language Processing, SuperDataScience Podcast #659

• March 3 — How to Build Data and ML Products Users Love, SuperDataScience Podcast #658

• March 2 — Getting Value from A.I., Pace University, New York [slides]

• March 1 — NLP with ChatGPT (and other Large Language Models), O’Reilly “A.I. Catalyst” Conference [video]

• February 27 — How to Learn Data Engineering, SuperDataScience Podcast #657

• February 24 — A.I. Talent and the Red-Hot A.I. Skills, SuperDataScience Podcast #656

• February 21 — AI ROI: How to get a profitable return on an AI-project investment, SuperDataScience Podcast #655

• February 17 — Mike Wimmer: The 14-Year-Old A.I. Entrepreneur, SuperDataScience Podcast #654

• February 14 — Efficiently Glean-ing Insights from Vast Data Warehouses, SuperDataScience Podcast #653

• February 10 — A.I. Speech for the Speechless, SuperDataScience Podcast #652

• February 8 — “Getting Value from A.I.” keynote at Hg Capital’s “Digital Forum”, London [slides]

• February 7 — The Intentional Use of Color in Data Communication, SuperDataScience Podcast #651

• February 3 — SparseGPT: Remove 100 Billion Parameters but Retain 100% Accuracy, SuperDataScience Podcast #650

• January 31 — Introduction to Machine Learning, SuperDataScience Podcast #649

• January 27 — VALL-E: Uncannily Realistic Voice Imitation from a 3-Second Clip, SuperDataScience Podcast #648

• January 24 — Is Data Science Still Sexy?, SuperDataScience Podcast #647

• January 20 — Getting Value from ChatGPT, SuperDataScience Podcast #646

• January 17 — Machine Learning for Video Games, SuperDataScience Podcast #645

• January 12 — A Framework for Big Life Decisions, SuperDataScience Podcast #644

• January 10 — A.I. for Medicine, SuperDataScience Podcast #643

• January 6 — Continuous Calendar for 2023, SuperDataScience Podcast #642

• January 3 — Data Science Trends for 2023, SuperDataScience Podcast #641

2022

• December 30 — What I Learned in 2022, SuperDataScience Podcast #640

• December 27 — Simplifying Machine Learning, SuperDataScience Podcast #639

• December 23 — ChatGPT Holiday Greeting, SuperDataScience Podcast #638

• December 20 — How to Influence Others with Your Data, SuperDataScience Podcast #637

• December 16 — The Equality Machine, SuperDataScience Podcast #636

• December 13 — The Perils of Manually Labeling Data for Machine Learning Models, SuperDataScience Podcast #635

• December 9 — Model Error Analysis, SuperDataScience Podcast #634

• December 6 — Responsible Decentralized Intelligence, SuperDataScience Podcast #633

• December 2 — Liquid Neural Networks, SuperDataScience Podcast #632

• November 29 — Data Analytics Career Orientation, SuperDataScience Podcast #631

• November 25 — Resilient Machine Learning, SuperDataScience Podcast #630

• November 22 — Software for Efficient Data Science, SuperDataScience Podcast #629

• November 21 — Data Science for Business, New York University Stern School [slides]

• November 18 — The Critical Human Element of Successful A.I. Deployments, SuperDataScience Podcast #628

• November 15 — AutoML: Automated Machine Learning, SuperDataScience Podcast #627

• November 10 — Subword Tokenization with Byte-Pair Encoding, SuperDataScience Podcast #626

• November 8 — Analyzing Blockchain Data and Cryptocurrencies, SuperDataScience Podcast #625

• November 4 — Imagen Video: Incredible Text-to-Video Generation, SuperDataScience Podcast #624

• November 3 — Deep Learning Illustrated book signing, ODSC West, San Francisco

• November 2 — The SuperDataScience Podcast live with Prof. Dawn Song, ODSC West, San Francisco

• November 1 — Data Analyst, Data Scientist, and Data Engineer Career Paths, SuperDataScience Podcast #623

• October 28 — Burnout: Causes and Solutions, SuperDataScience Podcast #622

• October 25 — Blockchains and Cryptocurrencies: Analytics and Data Applications, SuperDataScience Podcast #621

• October 21 — OpenAI Whisper: General-Purpose Speech Recognition, SuperDataScience Podcast #620

• October 18 — Tools for Deploying Data Models into Production, SuperDataScience Podcast #619

• October 14 — The Joy of Atelic Activities, SuperDataScience Podcast #618

• October 11 — Causality in Sequential Data, SuperDataScience Podcast #617

• October 7 — The Four Requirements of Expertise, SuperDataScience Podcast #616

• October 4 — How to Ace Your Data Science Interview, SuperDataScience Podcast #615

• September 30 — Thriving on Information Overload, SuperDataScience Podcast #614

• September 27 — Causal Machine Learning, SuperDataScience Podcast #613

• September 23 — More Guests on Fridays, SuperDataScience Podcast #612

• September 20 — Open-Ended A.I.: Practical Applications for Humans and Machines, SuperDataScience Podcast #611

• September 16 — Who Dares Wins, SuperDataScience Podcast #610

• September 13 — Data Mesh, SuperDataScience Podcast #609

• September 9 — Daily Habit #11: Assigning Deliverables, SuperDataScience Podcast #608

• September 6 — Inferring Causality, SuperDataScience Podcast #607

• September 2 — Four Thousand Weeks, SuperDataScience Podcast #606

• August 30 — Upskilling in Data Science and Machine Learning, SuperDataScience Podcast #605

• August 26 — Nuclear Fusion Ignition, SuperDataScience Podcast #604

• August 23 — Geospatial Data and Unconventional Routes into Data Careers, SuperDataScience Podcast #603

• August 19 — We Are Living in Ancient Times, SuperDataScience Podcast #602

• August 15 — Mimicking the Voice of Dead Relatives- The Future of Voice Cloning and A.I., The Evan Solomon Show

• August 16 — Venture Capital for Data Science, SuperDataScience Podcast #601

• August 12 — Yoga Nidra Practice, SuperDataScience Podcast #600

• August 9 — MLOps: Machine Learning Operations, SuperDataScience Podcast #599

• August 5 — Getting Kids Excited about STEM Subjects, SuperDataScience Podcast #598

• August 2 — A.I. Policy at OpenAI, SuperDataScience Podcast #597

• July 29 — The A.I. Platforms of the Future, SuperDataScience Podcast #596

• July 26 — Data Engineering 101, SuperDataScience Podcast #595

• July 22 — Why CEOs Care About A.I. More than Other Technologies, SuperDataScience Podcast #594

• July 19 — The Real-World Impact of Cross-Disciplinary Data Science Collaboration, SuperDataScience Podcast #593

• July 15 — How to Sell a Multimillion Dollar A.I. Contract, SuperDataScience Podcast #592

• July 13 — Career Panel on Deep Learning, Neuromatch Academy

• July 12 — Simulations and Synthetic Data for Machine Learning, SuperDataScience Podcast #591

• July 8 — Artificial General Intelligence is Not Nigh (Part 2 of 2), SuperDataScience Podcast #590

• July 5 — Narrative A.I. with Hilary Mason, SuperDataScience Podcast #589

• July 1 — Artificial General Intelligence is Not Nigh, SuperDataScience Podcast #588

• June 28 — Data Engineering for Data Scientists, SuperDataScience Podcast #587

• June 24 — Daily Habit #10: Limit Social Media Use, SuperDataScience Podcast #586

• June 22 — Daily Habit #10: Limit Social Media Use, SuperDataScience Podcast #586

• June 21 — Could artificial intelligence one day become sentient?, The Evan Solomon Show

• June 17 — OpenAI Codex, SuperDataScience Podcast #584

• June 14 — The State of Natural Language Processing, SuperDataScience Podcast #583

• June 10 — Model Speed vs Model Accuracy, SuperDataScience Podcast #582

• June 10 — SuperDataScience Live with Hilary Mason, New York R Conf

• June 7 — Bayesian, Frequentist, and Fiducial Statistics in Data Science, SuperDataScience Podcast #581

• June 3 — Collecting Valuable Data, SuperDataScience Podcast #580

• May 27 — Transforming Dentistry with A.I., SuperDataScience Podcast #579

• May 27 — Identifying Commercial ML Problems, SuperDataScience Podcast #578

• May 24 — Scaling A.I. Startups Globally, SuperDataScience Podcast #577

• May 21 — TEDx DrexelU, Philadelphia [video]

• May 20 — Tech Startup Dramas, SuperDataScience Podcast #576

• May 17 — Optimizing Computer Hardware with Deep Learning, SuperDataScience Podcast #575

• May 13 — Music for Deep Work, SuperDataScience Podcast #574

• May 10 — Automating ML Model Deployment, SuperDataScience Podcast #573

• May 6 — Daily Habit #9: Avoiding Messages Until a Set Time Each Day, SuperDataScience Podcast #572

• May 3 — Collaborative, No-Code Machine Learning, SuperDataScience Podcast #571

• April 29 — DALL-E 2: Stunning Photorealism from Any Text Prompt, SuperDataScience Podcast #570

• April 26 — A.I. For Crushing Humans at Poker and Board Games, SuperDataScience Podcast #569

• April 22 — PaLM: Google's Breakthrough Natural Language Model, SuperDataScience Podcast #568

• April 19 — Open-Access Publishing, SuperDataScience Podcast #567

• April 16 — Deep Machine Learning, “Data Science for Business” course at NYU Stern [slides]

• April 15 — The Best Time to Plant a Tree, SuperDataScience Podcast #566

• April 13 — PyTorch and Beyond, Ai+ Deep Learning Bootcamp (VI of VI)

• April 12 — AGI: The Apocalypse Machine, SuperDataScience Podcast #565

• April 8 — Clem Delangue on Hugging Face and Transformers, SuperDataScience Podcast #564

• April 7 — Moderator for Panel on “Open-Source Machine Learning”, ScaleUp:AI New York

• April 4 — How to Rock at Data Science — with Tina Huang, SuperDataScience Podcast #563

• April 1 — Daily Habit #8: Math or Computer Science Exercise, SuperDataScience Podcast #562

• March 31 — SuperDataScience Live at MLconf NYC

• March 30 — Deep Reinforcement Learning and A.I., Ai+ Deep Learning Bootcamp (V of VI) [slides]

• March 29 — Engineering Data APIs, SuperDataScience Podcast #561

• March 25 — Daily Habit #7: Read Two Pages, SuperDataScience Podcast #560

• March 23 — First video from Subject 5 of my ML Foundations series, Probability & Information Theory, released on YouTube [blog post]

• March 22 — GPT-3 for Natural Language Processing, SuperDataScience Podcast #559

• March 18 — Jon’s Answers to Questions on Machine Learning, SuperDataScience Podcast #558

• March 16 — Natural Language Processing, Ai+ Deep Learning Bootcamp (IV of VI) [slides]

• March 15 — Effective Pandas, SuperDataScience Podcast #557

• March 11 — Jon’s Machine Learning Courses, SuperDataScience Podcast #556

• March 8 — Sports Analytics and 66 Days of Data with Ken Jee, SuperDataScience Podcast #555

• March 4 — Jon’s Deep Learning Courses, SuperDataScience Podcast #554

• March 2 — Machine Vision and Creativity, Ai+ Deep Learning Bootcamp (III of VI) [slides A] [slides B]

• March 1 — The Statistics and Machine Learning Quests of Dr. Josh Starmer, SuperDataScience Podcast #553

• February 22 — The Most Popular Episodes of 2021, SuperDataScience Podcast #552

• February 22 — Deep Reinforcement Learning, SuperDataScience Podcast #551

• February 18 — Engineering Natural Language Models, SuperDataScience Podcast #550

• February 16 — Building and Training a Deep Learning Network, Ai+ Deep Learning Bootcamp (II of VI)

• February 15 — Engineering Natural Language Models, SuperDataScience Podcast #549

• February 11 — Daily Habit #5: Meditate, SuperDataScience Podcast #548

• February 8 — How Genes Influence Behavior, SuperDataScience Podcast #547

• February 4 — Daily Habit #4: Alternate-Nostril Breathing, SuperDataScience Podcast #546

• February 2 — How Deep Learning Works, Ai+ Deep Learning Bootcamp (I of VI) [slides] [handout]

• February 1 — Scaling Data-Intensive Real-Time Applications, SuperDataScience Podcast #545

• January 28 — Daily Habit #3: Make Your Bed, SuperDataScience Podcast #544

• January 25 — Sparking A.I. Innovation, SuperDataScience Podcast #543

• January 21 — Continuous Calendar for 2022, SuperDataScience Podcast #542

• January 18 — Data Observability, SuperDataScience Podcast #541

• January 14 — Daily Habit #2: Start the Day with a Glass of Water, SuperDataScience Podcast #540

• January 11 — Interpretable Machine Learning, SuperDataScience Podcast #539

• January 7 — Daily Habit #1: Track Your Habits, SuperDataScience Podcast #538

• January 4 — Data Science Trends for 2022, SuperDataScience Podcast #537

2021

• December 31 — What I Learned in 2021, SuperDataScience Podcast #536

• December 28 — How to Found, Grow, and Sell a Data Science Start-up, SuperDataScience Podcast #535

• December 24 — A Holiday Greeting, SuperDataScience Podcast #534

• December 21 — Fusion Energy, Cancer Proteomics, and Massive-Scale Machine Vision, SuperDataScience Podcast #533

• December 17 — Mutable vs Immutable Conditions, SuperDataScience Podcast #532

• December 16 — Deep Learning’s Neurobiology Origins, DATAcated Holiday Book Party [video] [slides]

• December 14 — Data Science at the Command Line, SuperDataScience Podcast #531

• December 10 — Ten A.I. Thought Leaders to Follow (on Twitter), SuperDataScience Podcast #530

• December 8 — Optimization (Machine Learning Foundations), O’Reilly [slides]

• December 7 — A.I. Robotics at Home, SuperDataScience Podcast #529

• December 3 — The Normal Anxiety of Content Creation, SuperDataScience Podcast #528

• December 1 — DSA II: Hashing, Trees, and Graphs (Machine Learning Foundations), O’Reilly

• November 30 — Automating Data Analytics, SuperDataScience Podcast #527

• November 26 — The Highest-Paying Data Frameworks, SuperDataScience Podcast #526

• November 23 — Hurdling Over Data Career Obstacles, SuperDataScience Podcast #525

• November 19 — The Highest-Paying Data Tools, SuperDataScience Podcast #524

• November 17 — Intro to Data Structures and Algorithms (Machine Learning Foundations), O’Reilly [slides]

• November 16 — Open-Source Analytical Computing (pandas, Apache Arrow), SuperDataScience Podcast #523

• November 12 — Data Tools vs. Data Platforms, SuperDataScience Podcast #522

• November 9 — Skyrocket Your Career by Sharing Your Writing, SuperDataScience Podcast #521

• November 8 — Deep Machine Learning, “Data Science for Business” course at NYU Stern [slides]

• November 5 — The Highest-Paying Programming Languages for Data Scientists, SuperDataScience Podcast #520

• November 3 — Statistics II: Regression and Bayesian (Machine Learning Foundations), O’Reilly

• November 2 — A.I. for Good, SuperDataScience Podcast #519

• October 29 — Fail More, SuperDataScience Podcast #518

• October 29 — Reinforcement Learning (ELEN E6885), Columbia University [slides] [code]

• October 27 — Intro to Statistics (Machine Learning Foundations), O’Reilly [slides]

• October 26 — Courses in Data Science and Machine Learning, SuperDataScience Podcast #517

• October 22 — Does Caffeine Hurt Productivity? (Part 3: Scientific Literature), SuperDataScience Podcast #516

• October 19 — Accelerating Impact through Community, SuperDataScience Podcast #515

• October 15 — Does Caffeine Hurt Productivity? (Part 2), SuperDataScience Podcast #514

• October 13 — Probability II and Information Theory (Machine Learning Foundations), O’Reilly

• October 12 — Transformers for Natural Language Processing, SuperDataScience Podcast #513

• October 8 — Does Caffeine Hurt Productivity? (Part 1), SuperDataScience Podcast #512

• October 6 — Intro to Probability (Machine Learning Foundations), O’Reilly [slides]

• October 5 — Data Science for Private Investing, SuperDataScience Podcast #511

• October 1 — Deep Reinforcement Learning, SuperDataScience Podcast #510

• September 28 — Accelerating Start-up Growth with A.I. Specialists, SuperDataScience Podcast #509

• September 24 — Building Your Ant Hill, SuperDataScience Podcast #508

• September 22 — Calculus IV: Gradients and Integrals (Machine Learning Foundations), O’Reilly

• September 21 — Bayesian Statistics, SuperDataScience Podcast #507

• September 17 — Supervised vs Unsupervised Learning, SuperDataScience Podcast #506

• September 15 — Calculus III: Partial Derivatives (Machine Learning Foundations), O’Reilly [slides]

• September 14 — From Data Science to Cinema, SuperDataScience Podcast #505

• September 10 — Classification vs Regression, SuperDataScience Podcast #504

• September 10 — Filming SuperDataScience Live with guest Drew Conway, R Conference New York

• September 8 — First video from Subject 4 of my ML Foundations series, Calculus II: Partial Derivatives & Integrals, released on YouTube [blog post]

• September 7 — Deep Reinforcement Learning for Robotics, SuperDataScience Podcast #503

• September 3 — Managing Imposter Syndrome, SuperDataScience Podcast #502

• August 31 — Statistical Programming with Friends, SuperDataScience Podcast #501

• August 27 — Yoga Nidra, SuperDataScience Podcast #500

• August 24 — Data Meshes and Data Reliability, SuperDataScience Podcast #499

• August 20 — How Only Beginners Know Everything, SuperDataScience Podcast #498

• August 18 — Calculus II: Automatic Differentiation (Machine Learning Foundations), O’Reilly [slides]

• August 17 — Maximizing the Global Impact of Your Career, SuperDataScience Podcast #497

• August 13 — A Brain-Computer Interface Story, SuperDataScience Podcast #496

• August 12 — Intro to Calculus (Machine Learning Foundations), O’Reilly [slides]

• August 10 — Successful AI Projects and AI Startups, SuperDataScience Podcast #495

• August 6 — How to Instantly Appreciate Being Alive, SuperDataScience Podcast #494

• August 3 — Bringing Data to the People, SuperDataScience Podcast #493

• July 30 — The World is Awful (and it’s Never Been Better), SuperDataScience Podcast #492

• July 28 — Linear Algebra III: Eigenvectors (Machine Learning Foundations), O’Reilly [slides]

• July 27 — R in Production, SuperDataScience Podcast #491

• July 24 — Deep Learning Battle: TensorFlow vs PyTorch, DataScienceGO Virtual [video] [slides]

• July 23 — Say No to Pie Charts, SuperDataScience Podcast #490

• July 21 — Linear Algebra II: Matrix Tensors (Machine Learning Foundations), O’Reilly [solutions]

• July 20 — Monetizing Machine Learning, SuperDataScience Podcast #489

• July 16 — The Price of Your Attention, SuperDataScience Podcast #488

• July 14 — Intro to Linear Algebra (Machine Learning Foundations), O’Reilly [slides]

• July 13 — Fixing Dirty Data, SuperDataScience Podcast #487

• July 9 — The History of Calculus, SuperDataScience Podcast #486

• July 6 — Financial Data Engineering, SuperDataScience Podcast #485

• July 2 — Algorithm Aversion, SuperDataScience Podcast #484

• June 29 — Setting Yourself Apart in Data Science Interviews, SuperDataScience Podcast #483

• June 25 — Continuous Calendars, SuperDataScience Podcast #482

• June 24 — Computer Science for Machine Learning (Part III of III), Ai+ Training [slides]

• June 22 — Performance Marketing Analytics, SuperDataScience Podcast #481

• June 18 — Top Resume Tips, SuperDataScience Podcast #480

• June 15 — Knowledge Graphs, SuperDataScience Podcast #479

• June 11 — Five Keys to Success, SuperDataScience Podcast #478

• June 10 — Computer Science for Machine Learning (Part II of III), Ai+ Training

• June 8 — How to Thrive as an Early-Career Data Scientist, SuperDataScience Podcast #477

• June 4 — Peer-Driven Learning, SuperDataScience Podcast #476

• June 1 — The 20% of Analytics Driving 80% of ROI, SuperDataScience Podcast #475

• May 27 — Computer Science for Machine Learning (Part I of III), Ai+ Training [slides]

• May 27 — The Machine Learning House, SuperDataScience Podcast #474

• May 25 — Machine Learning at NVIDIA, SuperDataScience Podcast #473

• May 21-23 — Filming Data Structures, Algorithms, and Machine Learning Optimization for Pearson [video]

• May 21 — The Learning Never Stops (so Relax), SuperDataScience Podcast #472

• May 17 — 99 Days to Your First Data Science Job, SuperDataScience Podcast #471

• May 13 — My Favorite Books, SuperDataScience Podcast #470

• May 11 — Learning Deep Learning Together, SuperDataScience Podcast #469

• May 7 — The History of Data, SuperDataScience Podcast #468

• May 6 — Probability and Statistics for Machine Learning (Part IV of IV), Ai+ Training

• May 6 — First video from Subject 3 of my ML Foundations series, Calculus 1: Limits & Derivatives, released on YouTube [blog post]

• May 4 — High-Impact Data Science Made Easy, SuperDataScience Podcast #467

• May 1 — Deep Machine Learning, Data Science for Business course at NYU Stern [slides]

• April 30 — Good vs. Great Data Scientists, SuperDataScience Podcast #466

• April 27 — Analytics for Commercial and Personal Success, SuperDataScience Podcast #465

• April 23 — A.I. vs Machine Learning vs Deep Learning, SuperDataScience Podcast #464

• April 22 — Probability and Statistics for Machine Learning (Part III of IV), Ai+ Training [slides]

• April 20 — Time Series Analysis, SuperDataScience Podcast #463

• April 20 — Human Capital Management HCAT1-GC2015: Intelligent Automation, New York University [slides]

• April 16 — It Could Be Even Better, SuperDataScience Podcast #462

• April 14 — MLOps for Renewable Energy, SuperDataScience Podcast #461

• April 13 — Final eight videos on linear algebra from my ML Foundations series are released on YouTube and Udemy [blog post]

• April 12 — Engineering 501: Deep and Reinforcement Learning, University of British Columbia [code]

• April 8 — Probability and Statistics for Machine Learning (Part II of IV), Ai+ Training

• April 7 — The History of Algebra, SuperDataScience Podcast #460

• April 7 — Tackling Climate Change with ML, SuperDataScience Podcast #459

• April 2 — Behind the Scenes, SuperDataScience Podcast #458

• March 31 — Landing Your Data Science Dream Job, SuperDataScience Podcast #457

• March 30 — Linear Algebra, Calculus, and Probability: The Math ML Experts Master, ODSC East [slides]

• March 26 — The Pomodoro Technique, SuperDataScience Podcast #456

• March 25 — Probability and Statistics for Machine Learning (Part I of IV), Ai+ Training [slides]

• March 24 — Legal Tech, Powered by Machine Learning, SuperDataScience Podcast #455

• March 19 — The Staggering Pace of Progress Part 2, SuperDataScience Podcast #454

• March 18-21 — Filming Probability and Statistics for Machine Learning for Pearson [blog post / photos] [video]

• March 17 — Big Global Problems Worth Solving with Machine Learning, SuperDataScience Podcast #453

• March 17 — Six more videos from Subject 2 of my ML Foundations series, Linear Algebra II: Matrix Operations, released on YouTube and Udemy [blog post]

• March 12 — The Staggering Pace of Progress, SuperDataScience Podcast #452

• March 10 — Translating PhD Research into ML Applications, SuperDataScience Podcast #451

• March 5 — Yoga Nidra, SuperDataScience Podcast #450

• March 3 — Fairness in A.I., SuperDataScience Podcast #449

• February 26 — How to Be a Data Science Leader, SuperDataScience Podcast #448

• February 24 — Calculus for Machine Learning (Part IV of IV), Ai+ Training

• February 24 — Commercial ML Opportunities Lie Everywhere, SuperDataScience Podcast #447

• February 23 — Panel on Continual Learning in Practice, Ai+ Webinar

• February 22 — First four videos from Subject 2 of my ML Foundations series, Linear Algebra II: Matrix Operations, released on YouTube [blog post]

• February 22 — Master of Science in Management Analytics: Applications of A.I., Wilfrid Laurier University, Canada [slides]

• February 19 – Getting Started in Machine Learning, SuperDataScience Podcast #446

• February 17 – Conversational A.I., SuperDataScience Podcast #445

• February 12 – Future-Proofing Your Career, SuperDataScience Podcast #444

• February 10 – The End of Jobs, SuperDataScience Podcast #443

• February 10 — Calculus for Machine Learning (Part III of IV), Ai+ Training [slides]

• February 5 – Data Science as an Atomic Habit, SuperDataScience Podcast #442

• February 3 – Communicating Data Effectively, SuperDataScience Podcast #441

• February 1 – Three bonus videos from first subject of my ML Foundations series, Intro to Linear Algebra, released on YouTube [blog post]

• January 28 — MuZero: Learning Without Rules, SuperDataScience Podcast #440

• January 27 — Deep Learning for Machine Vision, SuperDataScience Podcast #439

• January 27 — Calculus for Machine Learning (Part II of IV), Ai+ Training

• January 22 — Artificial General Intelligence, SuperDataScience Podcast #438

• January 20 — Data Science at a World-Leading Hedge Fund, SuperDataScience Podcast #437

• January 20 — Perspectives in Psychology, Neuroscience & Behaviour, McMaster University, Canada [slides]

• January 15 — Attention-Sharpening Tools Part 2, SuperDataScience Podcast #436

• January 13 — Scaling Up Machine Learning, SuperDataScience Podcast #435

• January 13 — Calculus for Machine Learning (Part I of IV), Ai+ Training [slides]

• January 8 — Attention-Sharpening Tools Part 1, SuperDataScience Podcast #434

• January 6 — Data Science Trends for 2021, SuperDataScience Podcast #433

• January 1 — Hello from Jon and Welcome to 2021, SuperDataScience Podcast #432

2020

• December 23 — 2020’s Biggest Data Science Breakthroughs, SuperDataScience Podcast #429

• December 20 — AI Recruitment Technology & Deep Learning, Engineered-Mind Podcast #27

• December 17 — Linear Algebra for Machine Learning (Part III of III), Ai+ Training [solutions]

• December 16 — Impacting Through Technology, SuperDataScience Podcast #427

• December 10 — Linear Algebra for Machine Learning (Part II of III), Ai+ Training [slides]

• December 3 — Linear Algebra for Machine Learning (Part I of III), Ai+ Training [slides]

• December 2 — Why the Best Data Scientists have Mastered Algebra, Calculus, and Probability, Data Science Labs [slides]

• November 20-22 — Filming Calculus for Machine Learning for Pearson [blog post / photos] [video]

• November 9 — Deep Machine Learning, NYU Data Mining course [slides]

• November 6 — TensorFlow 2 vs PyTorch, MLconf [slides] [video]

• October 30 — E6885 (Reinforcement Learning), Columbia University [slides] [code]

• October 29 — Deep Learning (with TensorFlow 2), Open Data Science Conference West [slides] [code]

• October 28 — Launch of Machine Learning and Data Science Foundations Masterclass Udemy course

• October 23 — ODSC West Warm-Up Webinar [slides] [recording]

• October 16-18 — Filming Linear Algebra for Machine Learning for Pearson [blog post / photos]

• October 15 — All remaining videos from the first subject of my ML Foundations series, Intro to Linear Algebra, released on YouTube [blog post]

• October 8 — ML Foundations videos on Matrix Multiplication (#18-21) released

• September 22 — ML Foundations videos on Solving Linear Systems (#16-17) released [blog post]

• September 10 — ML Foundations videos on Common Tensor Operations (#11-15) released [blog post]

• September 2 — Machine Learning Foundations: Optimization, O’Reilly [code]

• August 26 — Exercises on Algebra Data Structures — ML Foundations #10 [video]

• August 26 — Generic Tensor Notation — ML Foundations #9 [video]

• August 26 — Matrix Tensors — ML Foundations #8 [video]

• August 14 — Deep Learning Battle: TensorFlow 2 vs PyTorch, New York R Conference [slides] [video]

• August 13 — Scalars — ML Foundations #7 [video]

• August 13 — Norms and Unit Vectors — ML Foundations #6 [video]

• August 13 — Vectors and Vector Transposition — ML Foundations #5 [video]

• August 12 — Machine Learning Foundations: Algorithms & Data Structures, O’Reilly [code]

• August 5 — A4N Episode 4: Automated Cancer Detection & Self–Driving Cars with Dr. Rasmus Rothe

• July 29 — Deep Learning (with TensorFlow 2 and PyTorch), Open Data Science Ai+ Platform [video] [code] [slides] [handout]

• July 23 — Machine Learning Foundations: Intro to Statistics, O’Reilly [code]

• July 22 — Scalars — ML Foundations #4 [video]

• July 22 — Tensors — ML Foundations #3 [video]

• July 22 — Linear Algebra Exercise — ML Foundations #2 [video]

• July 9 — What Linear Algebra Is — ML Foundations #1 [video]

• July 9 — Machine Learning Foundations: Welcome to the Journey [video]

• July 8 — Machine Learning Foundations: Probability & Information Theory, O’Reilly [code]

• June 25 — Machine Learning Foundations: Calculus II: Partial Derivatives & Integrals, O’Reilly [code]

• June 23 — Deep Learning Illustrated with Jon Krohn, Hidden in Plain Sight (a podcast series by Mission.org)

• June 21 — Deep Learning Model Architectures for Natural Language Processing, DataScienceGO Conference [slides] [code]

• June 15 — Complete Artificial Intelligence Series: Deep Learning for Natural Language Processing [slides] [code]

• June 11 — Machine Learning Foundations: Calculus I: Limits & Derivatives, O’Reilly [slides]

• June 4 — Machine Learning Foundations: Linear Algebra II: Matrix Operations, O’Reilly [code]

• May 28 — Machine Learning Foundations: Intro to Linear Algebra, O’Reilly [code]

• May 26 — A4N Episode 3: Scaling a Global Data Business with Kirill Eremenko

• May 13 — Deep Learning Models for Recruitment, SuperDataScience Podcast

• May 12 — Announcing the Machine Learning Foundations Tutorial Series [video] [code]

• May 6 — Deep Learning + HR, GQR Webinar Series [slides] [video]

• April 30 — A.I. Recruiting Tightrope, The Chad & Cheese Podcast

• April 27 — Deep Machine Learning, NYU Machine Learning course [slides]

• April 15 PM — Meet the Expert: Jon Krohn, Open Data Science Conference East

• April 15 AM — Deep Learning (with TensorFlow 2) Workshop, Open Data Science Conference East [preview video] [slides] [code] [handout] [guitar/vocals of Little Feat cover]

• April 14 — release of Machine Vision, GANs, and Deep Reinforcement Learning — six hours of hands-on video tutorials

• April 2 — A4N Episode 2: Tackling Coronaviruses with Machine Learning, feat. Ben Taylor

• March 16 — The Central Limit Theorem [video] [code]

• March 5 — Deep Learning for Natural Language Processing: Complete Artificial Intelligence Series

• February 28 — release of Deep Learning for Natural Language Processing, 2nd Ed. — five hours of hands-on video tutorials

• February 20 — Introduction to Deep Learning with PyTorch: Complete Artificial Intelligence Series

• February 15 — release of Deep Learning with TensorFlow, Keras, and PyTorch — seven hours of hands-on video tutorials

• January 28 — Deep Reinforcement Learning: Complete Artificial Intelligence Series

• January 23 — Introduction to Deep Learning (with TensorFlow 2.0): Complete Artificial Intelligence Series

• January 20 — Big O Notation [video] [code] [slides]

• January 18 — Episode 1: Intrusive Face Detection, Kaggle Cheaters, AlphaFold, and Becoming an A.I. Researcher, A4N: The Artificial Neural Network News Network [blog post]

• January 17 — A Conversation with Jon Krohn, Chief Data Scientist of untapt, The Pulse of AI Podcast

• January 13 — Deep Learning, Featuring Applications to NLP, Blackstone, New York [slides] [code]

• January 8 — ODSC East Webinar Warm-Up [slides] [code]

2019

• December 11th — Gentle, Code-Free Intro to Deep Learning and Artificial Intelligence

• December 10th — Deep Machine Learning, NYU Data Mining course [slides]

• December 7th PM — Unit 10: Deep Reinforcement Learning, NYC Data Science Academy [slides] [slides of student, Edward Tong]

• December 7th AM — Unit 9: Generative Adversarial Networks, NYC Data Science Academy [slides]

• December 4th — Deep Learning for Natural Language Processing (NLP): Complete Artificial Intelligence Series

• November 23rd PM — Unit 8: PyTorch, NYC Data Science Academy

• November 23rd AM — Unit 7: Advanced TensorFlow, NYC Data Science Academy [slides]

• November 21st — Conversation on Artificial Intelligence and the Brain with neuroscientist Heather Berlin, Magdalen College Alumni in New York

• November 19th — Deep Learning for Machine Vision: Complete Artificial Intelligence Series

• November 16th — Units 5 and 6: Natural Language Processing, NYC Data Science Academy [slides]

• November 12th — Introduction to Deep Learning (with TensorFlow 2.0): Complete Artificial Intelligence Series

• November 5th — Deep learning is easier when it is illustrated on the Data Science at Home podcast

• November 4th — Deep Learning ‘Spoken’ with Dr. Jon Krohn on the Data Science Imposters podcast

• October 31st - November 6th — filming on NLP, Machine Vision, GANs, and Deep Reinforcement Learning

• October 30th — Deep Learning with TensorFlow 2.0, Open Data Science Conference West, San Francisco [slides]

• October 25th — “A Conversation with Jon Krohn” on the DataBytes podcast

• October 25th PM — Unit 4: Machine Vision, NYC Data Science Academy [slides]

• October 25th AM — Unit 3: Building and Training a Deep Learning Network, NYC Data Science Academy [slides]

• October 22nd — “Illustrating The Landscape And Applications Of Deep Learning” on Podcast.__init__

• October 19th PM — Unit 2: How Deep Learning Works, NYC Data Science Academy [slides]

• October 19th AM — Unit 1: The Unreasonable Effectiveness of Deep Learning, NYC Data Science Academy [video interview] [code] [slides]

• October 16th — Deep Learning Study Group XVII: Capsule Nets and BERT

• October 9th — Deep Learning for Natural Language Processing

• October 8th — Deep Learning: TensorFlow 2.0 vs PyTorch, Metis New York [slides]

• October 2nd — Deep Learning Illustrated Book Signing and Course Demo, NYC Data Science Academy [slides] [video]

• October 1st — Deep Learning with PyTorch

• September 26th-27th — Deep Learning Part II, Washington D.C.

• September 25th — Deep Learning with TensorFlow

• September 18th — Deep Learning Fundamentals

• September 19th-20th — Deep Learning Part I, Washington D.C.

• September 6th — “Explaining AI in Recruiting” Recruiting Future podcast

• August 22nd-24th — filming “Deep Learning with TensorFlow, Keras and PyTorch”

• August 8th — “Money Matters Top Tips with Adam Torres” podcast

• July 25th — Deep Learning for Natural Language Processing

• July 18th — Deep Reinforcement Learning

• June 28th — Deep Learning with TensorFlow 2.0, New York Open Data Science Conference [slides] [code]

• June 20th — Deep Learning with PyTorch

• June 12th — Deep Learning with TensorFlow [code]

• May 16th — Deep Learning for Natural Language Processing (NLP)

• May 7th — Deep Learning Fundamentals

• April 17th — Deep Learning with TensorFlow

• April 13th PM — Unit 10: Deep Reinforcement Learning, NYC Data Science Academy [slides] [video presentation of final project from student, Zach Do]

• April 13th AM — Unit 9: Generative Adversarial Networks, NYC Data Science Academy [slides]

• April 6th — Units 7 and 8: Deep Learning with TensorFlow, NYC Data Science Academy [slides]

• April 4th — Deep Learning for Machine Vision

• March 30th — Units 5 and 6: Natural Language Processing, NYC Data Science Academy [slides]

• March 23rd PM — Unit 4: Machine Vision, NYC Data Science Academy [slides]

• March 23rd AM — Unit 3: Building and Training a Deep Learning Network, NYC Data Science Academy [slides]

• March 16th PM — Unit 2: How Deep Learning Works, NYC Data Science Academy [slides]

• March 16th AM — Unit 1: The Unreasonable Effectiveness of Deep Learning, NYC Data Science Academy [video interview] [code] [slides]

• March 12th — Deep Reinforcement Learning

• March 6th — Deep Learning Illustrated: A Course Demo, NYC Data Science Academy [slides] [slides of former student, Zach McCormick]

• March 5th — Deep Learning Fundamentals

• February 14th — Deep Learning with TensorFlow

• February 6th — Deep Learning for Natural Language Processing (NLP)

• January 17th — Deep Learning Fundamentals

2018

• December 12th — Deep Learning with TensorFlow

• December 4th — Deep Learning for Machine Vision

• December 1st PM -- Unit 10: Deep Reinforcement Learning, NYC Data Science Academy [slides]

• December 1st AM -- Unit 9: Generative Adversarial Networks, NYC Data Science Academy [slides]

• November 27th — Artificial Intelligence Panel, VIOCON, Nasdaq Marketplace, New York

• November 17th -- Units 7 and 8: Deep Learning with TensorFlow, NYC Data Science Academy [slides]

• November 14th — Deep Reinforcement Learning

• November 6th — Deep Learning Fundamentals

• November 3rd -- Units 5 and 6: Natural Language Processing, NYC Data Science Academy [slides]

• October 27th PM -- Unit 4: Machine Vision, NYC Data Science Academy [slides]

• October 27th AM -- Unit 3: Building and Training a Deep Learning Network, NYC Data Science Academy [slides]

• October 26th — E6885 (Reinforcement Learning) Guest Lecture, Columbia University [slides]

• October 20th PM -- Unit 2: How Deep Learning Works, NYC Data Science Academy [slides]

• October 20th AM -- Unit 1: The Unreasonable Effectiveness of Deep Learning, NYC Data Science Academy [video interview] [code] [slides]

• October 10th — The Origins of Deep Learning’s Unreasonable Effectiveness, NYC Open Data [slides]

• October 3rd — Deep Learning with TensorFlow

• October 1st — Deep Learning for Natural Language Processing (NLP)

• September 20th — Deep Learning for Machine Vision

• September 12th -- Deep Machine Learning and its Neuroscience Origins, Pintô, New York [slides]

• September 12th — Deep Learning Fundamentals

• August 18th PM -- Unit 10: Deep Reinforcement Learning, NYC Data Science Academy [slides]

• August 18th AM -- Unit 9: Generative Adversarial Networks, NYC Data Science Academy [slides]

• August 16th — Deep Reinforcement Learning

• August 11th -- Units 7 and 8: Deep Learning with TensorFlow, NYC Data Science Academy [slides]

• August 8th — Deep Learning for Natural Language Processing (NLP)

• August 7th -- Talent Intelligence: A Think Tank on AI in HR by untapt, Rise New York [slides] [press release]

• August 4th -- Units 5 and 6: Natural Language Processing, NYC Data Science Academy [slides]

• July 28th PM -- Unit 4: Machine Vision, NYC Data Science Academy [slides]

• July 28th AM -- Unit 3: Building and Training a Deep Learning Network, NYC Data Science Academy [slides]

• July 21st PM -- Unit 2: How Deep Learning Works, NYC Data Science Academy [slides]

• July 21st AM -- Unit 1: The Unreasonable Effectiveness of Deep Learning, NYC Data Science Academy [blog post] [video interview] [code] [slides]

• July 18th — Deep Reinforcement Learning

• July 10th — Deep Learning Fundamentals

• July 9th -- Deep Learning with Artificial Neural Networks, Columbia University College of Dental Medicine [slides]

• June 6th — Deep Reinforcement Learning

• June 4th — Deep Learning for Natural Language Processing (NLP)

• May 30th -- untapt Resume Clinic, NYC Data Science Academy [slides]

• May 10th — Deep Reinforcement Learning

• May 8th — Deep Learning Fundamentals

• April 7th PM -- Unit 10: Deep Reinforcement Learning, NYC Data Science Academy [slides]

• April 7th AM -- Unit 9: Generative Adversarial Networks, NYC Data Science Academy [slides]

• April 6th — Deep Learning for Natural Language Processing

• April 4th — Deep Reinforcement Learning

• March 24th -- Units 7 and 8: Deep Learning with TensorFlow, NYC Data Science Academy [slides]

• March 17th -- Units 5 and 6: Natural Language Processing, NYC Data Science Academy [slides]

• March 13th — Deep Learning Fundamentals

• March 10th PM -- Unit 4: Machine Vision, NYC Data Science Academy [slides]

• March 10th AM -- Unit 3: Building and Training a Deep Learning Network, NYC Data Science Academy [slides]

• March 6th — Deep Learning for Natural Language Processing

• March 3rd PM -- Unit 2: How Deep Learning Works, NYC Data Science Academy [slides]

• March 3rd AM -- Unit 1: The Unreasonable Effectiveness of Deep Learning, NYC Data Science Academy [blog post] [video interview] [code] [slides]

• February 20th -- Deep Learning Course Demo, NYC Open Data [slides]

• February 17th -- hosted Deep Reinforcement Learning II, untapt (NY) [notes] [blog post]

• February 13th — Deep Learning for NLP

• February 6th — Deep Learning Fundamentals

• January 19th to 21st -- filming Deep Reinforcement Learning and Generative Adversarial Network LiveLessons for Safari [code] [blog post] [summary post] [video]

• January 11th — Deep Learning Fundamentals

• January 9th —Deep Learning for NLP

2017

• December 16th PM -- Unit 10: Deep Reinforcement Learning, NYC Data Science Academy [slides]

• December 16th AM -- Unit 9: Generative Adversarial Networks, NYC Data Science Academy [slides]

• December 9th -- hosted Deep Reinforcement Learning, untapt (NY) [notes] [blog post]

• December 8th — Deep Learning for NLP

• December 4th — Deep Learning Fundamentals

• December 2nd -- Units 7 and 8: Deep Learning with TensorFlow, NYC Data Science Academy [slides]

• November 21st -- Fundamentals of Deep Learning, untapt (NY) [slides]

• November 18th -- Units 5 and 6: Natural Language Processing, NYC Data Science Academy [slides]

• November 15th — Deep Learning Fundamentals

• November 8th — Deep Learning for NLP

• November 3rd -- E6885 (Reinforcement Learning) Guest Lecture, Columbia University (NY) [slides]

• October 28th PM -- Unit 4: Machine Vision, NYC Data Science Academy [slides]

• October 28th AM -- Unit 3: Building and Training a Deep Learning Network, NYC Data Science Academy [slides]

• October 24th — Deep Learning Fundamentals

• October 17th -- Reinforcement Learning, untapt (NY) [blog post]

• October 14th PM -- Unit 2: How Deep Learning Works, NYC Data Science Academy [slides]

• October 14th AM -- Unit 1: The Unreasonable Effectiveness of Deep Learning, NYC Data Science Academy [blog post] [video interview] [code] [slides]

• September 27th -- Deep Learning Course Demo, NYC Open Data [slides]

• September 25th — Deep Learning Fundamentals

• September 15th to 18th -- filming Deep Learning for Natural Language Processing LiveLessons for Safari [code] [blog post]

• August 21st — Deep Learning Fundamentals

• August 5th -- Model Architectures for Answering Questions and Overcoming NLP Limits, untapt (NY) [notes]

• July 1st -- Models with Attention, untapt (NY) [blog post] [notes]

• June 8th -- Match Making for Tech Jobs, International Society for Business and Industrial Statistics (IBM TJ Watson Research Center, Yorktown Heights, NY)

• June 1st to 3rd -- filming Deep Learning with TensorFlow LiveLessons for Safari [code] [blog post] [free preview]

• May 5th -- Panel Discussion on "Becoming a Champion" of Diversity, HackFemme Conference (NY)

• May 1st -- Metis Data Science Bootcamp (NY) [slides]

• April 21st -- NYU Analytics Conference, New York University (NY)

• April 19th -- Recurrent Neural Networks, including Gate Recurrent Units and Long Short-Term Memory units, untapt (NY) [blog post] [notes]

• March 27th -- Building Deep Learning Models for Natural Language Processing, untapt (NY) [blog post] [notes]

• March 6th -- Word Vectors and Vector-Space Embeddings, untapt (NY) [blog post] [notes]

• February 28th -- Fundamentals of Deep Learning, with Applications, NYC Open Data (NY) [video] [slides]

• February 7th -- Unsupervised Learning, untapt (NY) [blog post] [notes]

• January 30th -- The Fundamentals of Deep Learning, with Applications, Open Statistical Progamming Meetup (NY) [slides] [summary]

• January 17th -- Metis Data Science Bootcamp (NY) [slides]

• January 12th -- Implementing Convolutional Nets, untapt (NY)

• January 4th -- Deep Learning with Artificial Neural Networks, Wilfrid Laurier University (Waterloo) [press release] [video] [slides] [summary]

2016

• A History of Biological and Machine Vision, untapt (NY)

• How Deep Convolutional Neural Networks Work and How to Improve Them, untapt (NY)

• Fundamentals of Deep Learning with Neural Networks, Data Science + FinTech (Jersey City, NJ)

• Proofs of Key Deep Neural Network Properties, untapt (NY)

• Improving Deep Neural Networks, untapt (NY)

• The Backpropagation Algorithm, untapt (NY)

• Perceptrons and Sigmoid Neurons, untapt (NY)

• Predicting Job Application Success with Two-Stage, Bayesian Modeling of Features Extracted from Candidate-Role Pairs. Joint Statistical Meetings (Chicago)

• Modeling the Success of Software Developer Job Applications. Women in Machine Learning and Data Science (NY)

• Modeling the Success of Software Developer Job Applications. Metis Data Science Bootcamp, Winter Cohort (NY)

• R and Python Bootcamp. Columbia University / Hunter College PhD Career Transitions (NY)

• Mock Interviews for Data Scientists. Metis Data Science Bootcamp, Spring Cohort (NY)

• Mock Interviews for Technology Careers. General Assembly (NY)

2015

• Winner of Data Science Hackathon at I-COM Global Summit (San Sebastián, Spain)

• Winner of Data Science Hackathon at Google-Omnicom Emerge (NY)

• Data Science: Applications, Trends and Technologies. Metis Data Science Bootcamp, Fall Cohort (NY)

• Data Science Career Panel. Columbia University / Hunter College PhD Career Transitions (NY)

• Data-Driven Healthcare Marketing. Omnicom Emerge+ Healthcare (London)

2014

• Data Science in the Online Advertising Ecosystem. New York Computer Science and Economics Day [blog post]

• Data Science at Omnicom. General Assembly (NY)

2013

• Genes Contributing to Variation in Fear-Related Behaviour. Wellcome Trust Centre for Human Genetics (Oxford) [dissertation] [review]

2011

• Fine-Mapping QTL and Inferring Causal Pathways that Underlie Sixty Murine Phenotypes. Mouse Genetics (Washington, DC)

• Early-onset mood and anxiety problems: the role of early life adversities, epigenetic mechanisms and continuing brain development [Seminar Chair]. Genetics of Mood (Oxford)

• Pharmatics: Machine Learning for Identifying Causal Relationships with Genomic Data. Kairos Global Summit (NY)

• Pharmatics: Machine Learning for Identifying Causal Relationships with Genomic Data. Venture Capital Investment Competition, European Final (Oxford)

2010

• Gene-by-Environment Interactions Underlying Anxiety Across Six Murine Experiments. Complex Trait Community Annual Meeting (Chicago)

• Sex-by-Gene Interactions in 100 Murine Phenotypes Investigated by Resample Model Averaging. European Mathematical Genetics Meeting (Oxford)