How Neural Networks Actually Learn | Training From First Principles

How does a neural network go from a pile of random numbers to a model that can actually make useful predictions? In this video, we build the neural network training process from first principles and visualize what is actually happening underneath the code. We start with randomly initialized weights and meaningless predictions, then gradually build the machinery that allows a neural network to learn. You’ll understand: • Why neural networks start with random weights • What a loss function actually represents • Mean Squared Error and why we reduce model performance to a single number • What a loss surface is • Why gradient descent moves the model downhill • What the learning rate controls • How the chain rule makes backpropagation possible • How gradients flow through an entire neural network • What automatic differentiation is doing for us • Why vanishing gradients happen • How optimizers such as RMSProp adapt the update step • Why we train using mini-batches • What local minima really mean • The complete neural network training loop The goal is not just to know that we call: forward pass → loss → backward pass → optimizer step The goal is to understand why each of those steps exists. By the end of the video, the training loop should feel less like a few mysterious lines of PyTorch code and more like a logical process for turning random parameters into a working model. AI Beyond the Tutorials. Understand the system. Build better AI. — Ankit Wahane #NeuralNetworks #DeepLearning #MachineLearning #AI #GradientDescent #Backpropagation #PyTorch #GenAI #MLEngineering#GenAI#India#GenAIInIndia