Neural Networks Deep Dive

Understand how neural networks work from perceptrons to deep architectures. Dive deep into neural networks.

Level: Intermediate · Category: Deep Learning · Estimated time: 9 hours

Prerequisites

The Perceptron & Single Neuron — The building block of all neural networks — weights, bias, activation.
Multi-Layer Networks & Approximation — Hidden layers, expressive power, and why depth matters.
Backpropagation & Computational Graphs — The algorithm that makes deep learning possible — chain rule in action.
Activation Functions — ReLU, Sigmoid, Tanh, GELU, Swish — properties and when to use each.
Weight Initialization — Xavier, He, and Kaiming initialization — avoiding vanishing/exploding gradients.
Optimizers: SGD to Adam — Momentum, RMSProp, Adam, AdamW, and learning rate scheduling.
Regularization: Dropout & Batch Norm — Preventing overfitting with dropout, batch normalization, and layer normalization.
Architecture Design Principles — Choosing depth vs width, skip connections, and common architecture patterns.

neural-networks, backpropagation, deep-learning, activation-functions, optimization