Math4ML Exercises: Calculus

In this video, W&B Deep Learning Educator Charles Frye and ML Engineer Scott Condron work the calculus exercises of the Math for Machine Learning course. To try out the exercises yourself, head here: https://github.com/wandb/edu/tree/main/math-for-ml For the Math4ML lecture on calculus, head here: http://wandb.me/m4ml-video-2 For an introduction to this exercise series, head here: https://www.youtube.com/watch?v=99QfjbX6uxg&list=PLD80i8An1OEGZ2tYimemzwC3xqkU0jKUg Check out the other Math4ML videos here: http://wandb.me/m4ml-videos Blogpost on calculating derivatives Frechet-style: https://charlesfrye.github.io/math/2018/03/06/frechet-derivative-introduction.html 00:00 - Teaser 00:15 - Intro 00:46 - Little-o notation 05:24 - Checking is_little_o in Python 07:16 - Doing math in Python with SymPy 10:19 - What does little-o mean? 14:50 - Exercise: is_little_o_x 25:07 - The gradient is a linear approximation 29:17 - Different meanings of "the" gradient 32:29 - Gradient of a constant function 34:15 - Exercise: Making a linear_approximation 38:29 - Gradients and optimization 40:39 - Exercise: Gradient descent 42:30 - Outro Get started with W&B: http://wandb.me/intro Follow us: Twitter: http://twitter.com/weights_biases Linkedin: https://www.linkedin.com/company/weig... Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected