It's my birthday today! In celebration, I'm delighted to be releasing the final video of my "Calculus for Machine Learning" YouTube course. The first video came out in May and now, ten months later, we're done! 🎂
We published a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday since May 6th, 2021. So happy that it's now complete for you to enjoy. Playlist is here.
More detail about my broader "ML Foundations" curriculum (which also covers subject areas like Linear Algebra, Probability, Statistics, Computer Science) and all of the associated open-source code is available in GitHub here.
Starting next Wednesday, we'll begin releasing videos for a new YouTube course of mine: "Probability for Machine Learning". Hope you're excited to get going on it :)
Filtering by Tag: calculus
Finding the Area Under the ROC Curve
In this week's tutorial, we use Python code to find the area under the curve of the receiver operating characteristic (the "ROC curve"). This is a machine learning-specific application of integral calculus.
We publish a new video from my "Calculus for Machine Learning" course to YouTube every Wednesday. Playlist is here.
This is the penultimate video in my Calculus course! After ten months of publishing it, the final video will be released next week :)
More detail about my broader "ML Foundations" curriculum and all of the associated open-source code is available in GitHub here.
Definite Integral Exercise
My recent videos have covered how to find Definite Integrals manually as well as how to find them computationally using Python code. This week's video is an exercise that tests comprehension of both approaches.
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.
Numeric Integration with Python
Having detailed how to integrate equations by hand over the past few weeks, this week's video tutorial uses Python code to introduce how to find Definite Integrals computationally — and therefore automatically.
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.
Definite Integrals
In recent weeks, my videos introduced Indefinite Integration. Today, we go a step further to calculate *Definite* Integrals. This allows us to find the area under a curve, which is essential for many machine learning 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.
Indefinite Integral Exercises
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.
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.
What Integral Calculus Is
What is Integral Calculus and why is it essential to Machine Learning? This week's video answers those questions while also explaining how integral calculus works at a high level and detailing its characteristic 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.
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.