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Introduction to Neural Networks (Lecture 5)

Here is a YouTube description for your fifth lecture, modeled after the format you provided: Welcome to the fifth lecture of my Deep Learning series! 🐍 In this video, we transition from mathematical theory to actual Python code. We start building the heart of our Micrograd library: the Value class. This is the foundational data structure that will allow us to build complex Neural Networks from scratch. We begin by establishing the prerequisites, discussing Python Classes and Object-Oriented Programming (OOP). We then implement the Value object, a wrapper around standard integers and floats. By using "Dunder" (Double Underscore) methods, we unlock the ability to perform arithmetic operations like addition and multiplication directly on our custom objects. Crucially, we learn how to track the "lineage" of these operations—storing the children and the operators—to construct a Computational Graph. Finally, we visualize this graph to see exactly how data flows through a mathematical expression, setting the stage for the Forward Pass of a neuron (wx + b) In this video, we cover: ✅ Python OOP Refresher: Understanding Classes, Objects, and the _init_ constructor. ✅ Dunder Methods: Implementing _repr_ for nice string representation. ✅ Operator Overloading: Using _add_ and _mul_ to define how our Value objects handle + and *. ✅ Building the Graph: Storing pointers to previous nodes (_children) and operations (_op) to create a connected tree. ✅ Graph Visualization: Using Graphviz (via a helper function) to visually inspect the computational graph we built. ✅ The Forward Pass: simulating the structure of a single neuron by computing ab + c Resources: 🔗 GitHub Repository (Code & Notes): https://github.com/gautamgoel962/Yout... 🔗 Follow me on Instagram:   / gautamgoel978   Prerequisites: Previous Lecture (Multivariate Calculus & Backprop Intuition) Basic Python Syntax Understanding of "Self" and Classes in Python (Helpful but explained briefly) Subscribe to continue the journey. In the next lecture, we will implement the Backward Pass to calculate gradients automatically! 🧠💻 #DeepLearning #Python #OOP #Micrograd #NeuralNetworks #ForwardPass #Coding #Hindi #AI #Graphviz