we will implement an automatic differentiation engine from scratch In PyTorch, one can compute arbitrary gradients easily, such as: import torch a = torch.tensor(3., requires_grad=True) print(torch.autograd.grad(a**(3*a-4), a)) # prints (tensor(1205.8884),) You never needed to tell torch what the derivative of x^(3x-4) is; torch figures it out itself, using something called automatic differentiation. Automatic differentiation allows for differentiating arbitrary functions, which forms the basis of Machine learning frameworks, such as PyTorch or JAX. In this class, you will learn how automatic differentiation works, and implement it from scratch in vanilla python. Hopefully, after this class, you will better understand how your favorite ML framework works under the hood, and the performance implications of those frameworks.

Learn how to make and host a public discord bot using python and replit.com

It is an illustrated tour of the structures and patterns we call “math”. The only numbers in this course are the course numbers. Math Without Numbers is a vivid and conversational guide to the main branches of abstract math—topology and category theory. This course upends the conventional approach to math, inviting you to think creatively about shape and dimension, the infinite and infinitesimal, symmetries, proofs, and how these concepts all fit together. What awaits you is a freewheeling tour of the inimitable joys and unsolved mysteries of this curiously powerful subject. (This course is inspired by the book MATH WITHOUT NUMBERS by Milo Beckman)