Neural Network Fundamentals

Understand the core building blocks of neural networks, including perceptrons, multilayer networks, forward and backward propagation, activation functions, and loss functions.

📄️ Perceptron

Understanding the building block of Deep Learning: Weights, Bias, and Step Functions.

📄️ MLP

Exploring Feedforward Neural Networks, Hidden Layers, and how stacking neurons solves non-linear problems.

📄️ Forward Propagation

Understanding how data flows from the input layer to the output layer to generate a prediction.

📄️ Backpropagation

Demystifying the heart of neural network training: The Chain Rule, Gradients, and Error Attribution.

📄️ Activation Functions

Why we need non-linearity and a deep dive into Sigmoid, Tanh, ReLU, and Softmax.

📄️ Loss Functions

Understanding how models quantify mistakes using MSE, Binary Cross-Entropy, and Categorical Cross-Entropy.