Upcoming Talks
May 8 — Linear Algebra for Machine Learning, Level III: Eigenvectors, O’Reilly
May 10-12 — Panel Member for A.I. Master Class for Board Members and C-Level Execs, New York University
May 17 — New York R Conference
May 22 — Calculus for Machine Learning: Intro, O’Reilly
June 5 — Calculus for Machine Learning, Level II: Automatic Differentiation, O’Reilly
June 26 — Calculus for Machine Learning, Level III: Partial Derivatives, O’Reilly
July 10 — Calculus for Machine Learning, Level IV: Gradients & Integrals, O’Reilly
Past Talks
2024
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 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 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)
