Let's Build a NEURAL NETWORK! | Math

Welcome to the first video in this Neural Network series! In this episode, we dive into the mathematical foundations of neural networks, exploring key concepts that power these models. What You'll Learn: 1️⃣ Matrix Multiplication: Understand how data and weights are represented in matrices and how matrix operations enable predictions. 2️⃣ Chain Rule: Learn how derivatives are used in neural networks and how the chain rule calculates gradients. 3️⃣ Neural Network Architecture: Discover the structure of neural networks. 4️⃣ Forward Pass: Understand how inputs, weights, biases, and activation functions work together to generate predictions. 5️⃣ Loss Functions: Explore the most common loss functions for regression and classification tasks. 6️⃣ Backpropagation: Get a clear explanation of how gradients are computed and propagated backward to update weights. 7️⃣ Batch Training: Learn how stochastic gradient descent works and why training in batches improves model performance. By the end of this video, you'll have a solid grasp of the mathematical underpinnings of neural networks, including gradient descent, activation functions, and the importance of backpropagation. 📌 Coming Next: In the next video, we’ll start implementing a neural network library step by step, putting these concepts into action. GitHub Repo: https://github.com/Vineet-Vinod/NumpyNN