If you watched last week's video on the integral calculus rules (or if you already feel confident about them!), you can use this week's video to test your comprehension of the topic.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is 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: ML Foundations
The Integral Calculus Rules — Topic 86 of Machine Learning Foundations
Integral Calculus Rocks! I mean, Integral Calculus Rules! I mean, this video covers the Integral Calculus Rules 😉... namely, the Power Rule, the Constant-Multiple Rule, and the Sum Rule.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
The Receiver-Operating Characteristic (ROC) Curve
In this video, we work through a simple example — with real numbers — to demonstrate how to calculate the ROC Curve, an enormously useful metric for quantifying the performance of a binary classification model.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Binary Classification
Last week, I kicked off a series of YouTube videos on Integral Calculus. To provide a real-world Machine Learning application to apply integral calculus to, today's video introduces what Binary Classification problems are.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Integral Calculus - The Final Segment of Calculus Videos in my ML Foundations Series
After several months of publishing videos on the Differential branch of Calculus, with today's video we turn our focus toward the *Integral* branch. As ever, applications of this math to Machine Learning remain central.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Exercise on Higher-Order Partial Derivatives
To cap off an epic four-month sequence of videos on Partial-Derivative Calculus, today's YouTube video features an exercise on Higher-Order Partial Derivatives. Next week, a new topic area begins: Integral Calculus!
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Higher-Order Partial Derivatives
This week's YouTube video introduces higher-order derivatives for multi-variable functions, with a particular focus on the second-order partial derivatives that abound in machine learning.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Backpropagation
This week's video explains the relationship between Partial-Derivative Calculus and the Backpropagation ("Backprop") approach used widely in training Artificial Neural Networks, including Deep Learning networks.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
The Gradient of Mean Squared Error
For this week's YouTube video, we manually derive the gradient of Mean Squared Error (popular in ML to quantify accuracy). We then use the Python library PyTorch to obtain the same result and to visualize ML in action.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Gradient Descent (Hands-on with PyTorch)
In my preceding YouTube videos, we detailed exactly what the gradient of cost is. With that understanding, today we dig into what it means to *descend* this gradient and fit a machine learning model.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
The Gradient of Quadratic Cost
In this week's video, we derive the Partial Derivatives of Quadratic Cost with respect to the parameters of a simple regression model. This derivation is essential to understanding how machines learn via Gradient Descent.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Linear Regression Fit Point by Point
With this new video, the past months of my "Calculus for Machine Learning" videos all start to come together, enabling us to apply the simplest ML model: a regression line fit to individual data points one by one by one.
This simple regression model will enable us, in next week's video, to derive the simplest-possible partial derivatives for calculating a machine learning gradient.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Exercises on the Multivariate Chain Rule
Last week's YouTube video detailed how we use the Chain Rule for Multivariate (Partial Derivative) Calculus. This week's video features three exercises to test your comprehension of the topic.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is 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 Partial Derivatives
For this week's YouTube video, we apply the Chain Rule (introduced earlier in the series in the single-variable case) to find the partial derivatives of multivariate functions — such as Machine Learning functions!
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Partial Derivative Notation
This week's YouTube video is a quick one on the common options for partial derivative notation.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
"Math for Machine Learning" course on Udemy complete!
After a year of filming and editing, my "Math for Machine Learning" course on Udemy is complete! To celebrate, we put together this epic video that overviews the 15-hour curriculum in under two minutes.
Over 83,000 students have registered for the course, which provides an introduction to all of the essential Linear Algebra and Calculus that one needs to be an expert Machine Learning practitioner. It's full of hundreds of hands-on code demos in the key Python tensor libraries — NumPy, TensorFlow, and PyTorch — to make learning fun and intuitive.
You can check out the course here.
Advanced Partial-Derivative Exercises
My "Machine Learning Foundations" video this week features fun geometrical examples in order to strengthen your command of the partial derivative theory that we covered in the preceding videos.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Advanced Partial Derivatives
This week's video builds on the preceding ones in my ML Foundations series to advance our understanding of partial derivatives by working through geometric examples. We use paper and pencil as well as Python code.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
O'Reilly + JK October & November ML Foundations LIVE Classes open for registration
We're halfway through my live 14-class "ML Foundations" curriculum, which I'm offering via O'Reilly Media. The first Probability class is on Wednesday with five of the seven remaining classes open for registration now:
• Oct 6 — Intro to Probability
• Oct 13 — Probability II and Information Theory
• Oct 27 — Intro to Statistics
• Nov 3 — Statistics II: Regression and Bayesian
• Nov 17 — Intro to Data Structures and Algorithms
The final two classes will be in December and are on Computer Science topics: Hashing, Trees, Graphs, and Optimization. Registration for them should open soon.
All of the training dates and registration links are provided at jonkrohn.com/talks.
A detailed curriculum and all of the code for my ML Foundations series is available open-source in GitHub here.
Calculating Partial Derivatives with PyTorch AutoDiff
My recent videos have detailed how to calculate partial derivatives by hand. In today's, I demo how we can compute them automatically using PyTorch, enabling us to easily differentiate complex equations like ML models.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.