Building a Machine Learning Library From Scratch with C++ | Part 1: Understanding the Basics In the first episode of this series, Gautam introduces the fundamental concepts necessary for building a deep learning library from scratch using C++. He provides a thorough explanation of the theory behind neural networks, including graph construction, gradients of primitive operations, and the mathematical operations integral to machine learning. Viewers will learn the basics required to undertake coding in the next lecture where implementation begins. Stay tuned for building complex models like multi-layer perceptrons and understanding the nuances of forward and backward propagation. 00:00 Introduction to Building a Deep Learning Library 00:41 Understanding Neural Networks and Loss Convergence 01:13 Agenda and Mathematical Foundations 01:51 Binary Operators and Derivatives 02:44 Graph Scenarios and Gradients 04:05 Chain Rule and Gradient Calculation 05:16 Multiplication as a Primitive Operation 05:21 Understanding Multiplication and Gradients 06:19 Exploring Subtraction and Its Gradients 07:03 Exponentiation and Calculus Basics 08:19 Division as Multiplication by Inverse 09:07 Practical Implementation of Operators 09:47 Forward and Backward Propagation Explained 10:48 Conclusion and Next Steps github: link to slides: