This is the FINAL (of nine) videos in my Machine Learning Foundations series on the Derivative Rules. It merges together the Power Rule and the Chain Rule into a single easy step.
Next begins a chunk of long, meaty videos on Automatic Differentiation — i.e., using the PyTorch and TensorFlow libraries to, well, automatically differentiate equations (e.g., ML models) instead of needing to do it painstakingly by hand.
Because these forthcoming videos are so meaty, we're moving from a twice-weekly publishing schedule to a weekly one: Starting next week, we'll publish a new video to YouTube every Wednesday.
My growing "Calculus for ML" course available on YouTube here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Filtering by Category: Calculus
Advanced Exercises on Derivative Rules — Topic 60 of Machine Learning Foundations
Having now covered the product rule, quotient rule, and chain rule, we're well-prepared for advanced exercises that confirm your comprehension of all of the derivative rules in my Machine Learning Foundations series.
There’s just one quick derivative rule left after this — one that conveniently combines together two of the rules we’ve already covered — and then we’re ready to move on to the next segment of videos on Automatic Differentiation with PyTorch and TensorFlow.
New videos are published every Monday and Thursday to my "Calculus for ML" course, which is available on YouTube here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
The Chain Rule for Derivatives — Topic 59 of Machine Learning Foundations
Today's video introduces the Chain Rule — arguably the single most important differentiation rule for ML. It facilitates several of the most ubiquitous ML algorithms, such as gradient descent and backpropagation.
Gradient descent and backprop will be covered in great detail later in my "Machine Learning Foundations" video series. This video is critical for understanding those applications.
New videos are published every Monday and Thursday to my "Calculus for ML" course, which is available on YouTube.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub.
The History of Calculus
Y'all seem to love these "History of..." episodes, so for Five-Minute Friday this week, here's another one. It's on the History of Calculus! Enjoy 😄
(Leibniz and Newton, who independently devised modern calculus around the same time, are pictured.)
Listen or watch here.
The Quotient Rule for Derivatives — Topic 58 of Machine Learning Foundations
This is the penultimate Derivative Rule and then we're moving onward to AutoDiff with TensorFlow and PyTorch! The Quotient Rule is analogous to the Product Rule introduced on Monday but is for division instead of multiplication.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
The Product Rule for Derivatives
Today's video is on the Product Rule, a relatively advanced Derivative Rule. Only a couple such rules remain and then we move onward to Automatic Differentiation with PyTorch and TensorFlow.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
machinelearning,datascience,calculus,mathematics,python
Exercises on Derivative Rules — Topic 56 of Machine Learning Foundations
Today's YouTube video uses five fun exercises to test your understanding of the derivative rules we’ve covered so far: the Constant Rule, Power Rule, Constant-Multiple Rule, and Sum Rule.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
If you’d happen to like a detailed walkthrough of the solutions to all the exercises in this video, you can check out my Udemy course called Mathematical Foundations of Machine Learning. See jonkrohn.com/udemy
The Sum Rule for Derivatives
Thus far in this set of videos on Differentiation Rules, we’ve covered the Constant, Power, and Constant-Multiple rules. Today's video is on the Sum Rule. On Thursday, we'll have comprehension exercises on all four key rules!
The Constant Multiple Rule for Derivatives
Continuing my short series on Differentiation Rules, today’s video covers the Constant Multiple Rule. This rule is often used in conjunction with the Power Rule, which was covered in the preceding video, released on Monday.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
Performance Marketing Analytics
My guest this week is Kris Tait, who fills us in on how data and machine learning have transformed — and will continue to transform — marketing, enabling even small firms to effectively target customers and grow their revenue.
In this episode of the SuperDataScience show, we cover:
• What performance marketing is
• The rapidly shifting digital marketing ecosystem, as well as how data and ML can mitigate the risks associated with these changes
• The sweet spot for augmenting human marketers' skills with machines
• How any firm should define metrics to maximize return on marketing investment, thereby ensuring broader commercial success
• The most useful modern data science tools for global digital marketing
Kris is the managing director for the US at Croud - Performance Marketing Agency of the Year, an innovative marketing agency that is driven by data analytics and machine learning algorithms.
Listen or watch here.
The Power Rule for Derivatives
On Thursday, I published a video on the Constant Rule, the first video in a series on Differentiation Rules. Today, we continue the series with the Power Rule, arguably the most common and most important of all the rules.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
The Derivative of a Constant
This and the next several videos will provide you with clear and colorful examples of all of the most important differentiation rules. We kick these rules off with the Constant Rule.
The derivative rules are critical to machine learning as they allow us to find the derivatives of cost functions. These cost-function derivatives are concatenated into the "gradient" that we descend to allow ML models to learn.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
Derivative Notation
In today's YouTube video, we detail all of the most common notation for derivatives. This lays the foundation for a fun, immediately forthcoming series of videos covering all of the major differentiation rules. Enjoy!
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
How Derivatives Arise from Limits
In today's video, we use hands-on code demos in Python to find the slopes of curves with the Delta Method. While finding these slopes, we derive together — from first principles — the most important Differential Calculus formula.
This video is part of a thematic segment of videos on Differentiation. In the forthcoming videos, we’ll cover derivative notation and a series of useful rules for differentiation.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
The Delta Method
In today's video, we use a Python code demo to develop a working understanding of the Delta Method, a centuries-old technique that enables us to determine the slope of a curve.
This video is the first from a thematic segment of videos on Differentiation. In Thursday's video, we'll build on what we covered today to derive — and deeply understand — the most common, most important equation in differential calculus.
New videos are published every Monday and Thursday. The playlist for my "Calculus for ML" course is here.
More detail about my broader "ML Foundations" series and all of the associated open-source code is available in GitHub here.
Derivatives and Differentiation — Segment 2 of Subject 3, "Limits & Derivative
Today marks the beginning of a new thematic segment of videos in my ML Foundations series. This segment builds on the Limits content already covered to clearly illustrate how Differentiation works and how we find Derivatives.
Through a combination of color-coded equations, paper-and-pencil exercises, and hands-on Python code demos, the videos in this segment instill a deep understanding of how differentiation allows us to find derivatives.
More specifically, the videos cover:
• The Delta Method
• The Differentiation Equation
• Differentiation Notation
• Rules that enable us to quickly calculate the derivatives of a wide range of functions, including those found throughout machine learning
New videos are published every Monday and Thursday. The playlist for my Calculus for ML course is here.
More detail about my broader ML Foundations series and all of the associated open-source code is available in GitHub here.
Exercises on Limits
Final YouTube video from my thematic segment on Limits out today! It's a handful of comprehension exercises. Starting Thursday, we'll begin releasing videos from a new Calculus segment, on derivatives and differentiation.
We release new videos from my "Calculus for Machine Learning" course on YouTube every Monday and Thursday. The playlist is here.
The Machine Learning House
In last week’s Five-Minute Friday, I discussed how, in the data science field, the learning never stops. But there’s one big counterpoint: The foundational subjects that underlie the field barely change at all, decade after decade.
These subjects — linear algebra, calculus, probability, statistics, data structures, and algorithms — build a strong foundation for your “Machine Learning House”. Today's Five-Minute Friday articulates my perspective that investing time in studying these foundational subjects will reap great dividends throughout your data science career.
You can listen or watch here.
Calculating Limits
Today's video introduces Limits, a key stepping stone toward understanding Differential Calculus. This one has lots of interactive Python code demos and paper-and-pencil exercises to ensure learning the subject is both engaging and fun.
We release new videos from my "Calculus for Machine Learning" course on YouTube every Monday and Thursday. The playlist is here.
Calculus Applications
New YouTube video out today! In this one, I provide specific examples of how calculus is applied in the real world, with an emphasis on applications to machine learning.
The YouTube playlist for my "Calculus for Machine Learning" course is here.