Automatic Differentiation (DL 26) | Highlights and Annotations by Gistr.

Zygote, a Julia library, enables automatic differentiation, crucial for large-scale machine learning. It computes gradients of complex functions, like the Fibonacci sequence, by constructing a computation graph during function execution. Reverse-mode automatic differentiation, unlike forward-mode, scales efficiently with the number of outputs (typically one), making it ideal for deep learning. This involves creating "gradient tape" objects storing intermediate values, parents, and functions, enabling backpropagation via the chain rule to compute gradients. Zygote, a Julia library, automatically computes the gradient of a function, showcasing its capabilities by calculating the gradient of a sample function and comparing it to the symbolic derivative. This segment highlights the ease and efficiency of automatic differentiation compared to manual methods, setting the stage for exploring more complex functions. This segment explains the core concept of reverse-mode automatic differentiation, emphasizing its efficiency in handling complex functions like the Fibonacci sequence. It contrasts reverse-mode with forward-mode, highlighting the scalability advantages of reverse-mode, particularly relevant for deep learning applications with numerous input parameters.