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.

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