Let's Build a NEURAL NETWORK!

Welcome to the complete walkthrough of building a neural network library in Python using NumPy! This video combines all the parts of the series into one comprehensive guide, taking you from the basics of neural networks to building and testing a fully functional library from scratch. What we’ll cover: 1️⃣ Introduction to Neural Networks: Understanding the fundamentals of neural networks, activation functions, and the importance of building from scratch. 2️⃣ Mathematics Behind Neural Networks: - Matrix multiplication and its role in computations. - Chain rule and backpropagation using gradients. - Activation functions like ReLU, Sigmoid, Tanh, and Softmax. - Loss functions and their derivatives for optimization. - Gradient descent and the concept of batching in training. 3️⃣ Coding the Neural Network Library: - Setting up core classes for layers and layer lists. - Implementing activation and loss functions. - Coding the forward pass and testing with sample inputs. - Adding backpropagation and support for batch gradient descent. - Writing a training loop to abstract model training. 4️⃣ Testing the Library on MNIST: Using our library to classify the MNIST dataset of handwritten digits – the "Hello World" of neural networks! By the end of this video, you’ll have a solid understanding of how neural networks work, and a custom-built library that you can extend and use for various machine learning tasks. If you found this series valuable, don’t forget to like, share, and subscribe to the channel. Let me know your thoughts in the comments, and stay tuned for more exciting series in the future! GitHub Repo: https://github.com/Vineet-Vinod/NumpyNN