Neural Networks Fundamentals
Master the foundations of neural networks from mathematical principles to practical implementations. 8 comprehensive chapters covering feedforward networks, backpropagation, activation functions, CNNs, RNNs, and LSTMs.
Course Overview
What You Will Build Toward
- Navigate the Neural Networks Fundamentals learning path across 8 chapters.
- Choose the right chapter based on your current goal and prerequisites.
- Move from overview material into the canonical chapter experience.
Chapter Path
Start With Any Chapter
Before You Start
Recommended Background
- Working knowledge of the course category.
- Willingness to work through examples and short checks.
Neural Networks Fundamentals
Master the foundations of neural networks from mathematical principles to practical implementations. 8 comprehensive chapters covering feedforward networks, backpropagation, activation functions, CNNs, RNNs, and LSTMs with detailed explanations, formulas, and code examples.
Chapter 1: Introduction to Neural Networks
From Biological Neurons to Artificial Networks
- Biological inspiration and neuron model
- Perceptron and linear classification
- Multi-layer perceptrons (MLPs)
- Universal approximation theorem
- Neural network architecture basics
Chapter 2: Feedforward Networks & Forward Propagation
Understanding Information Flow
- Layer-by-layer computation
- Matrix operations in neural networks
- Weight initialization strategies
- Bias terms and their role
- Forward propagation implementation
Chapter 3: Activation Functions
Non-Linearity and Network Capacity
- Sigmoid, Tanh, ReLU, and variants
- Activation function properties
- Vanishing gradient problem
- Choosing the right activation
- Leaky ReLU, ELU, Swish functions
Chapter 4: Backpropagation Algorithm
The Learning Mechanism
- Chain rule and gradient computation
- Backward pass step-by-step
- Computational graph understanding
- Gradient flow and vanishing/exploding gradients
- Backpropagation implementation from scratch
Chapter 5: Optimization & Training
Learning from Data
- Loss functions (MSE, Cross-entropy)
- Gradient descent variants (SGD, Adam, RMSprop)
- Learning rate scheduling
- Batch normalization
- Regularization techniques (L1, L2, Dropout)
Chapter 6: Convolutional Neural Networks (CNNs)
Processing Spatial Data
- Convolution operation and filters
- Pooling layers (Max, Average)
- CNN architecture patterns
- Image classification examples
- Transfer learning with CNNs
Chapter 7: Recurrent Neural Networks (RNNs)
Processing Sequential Data
- RNN architecture and hidden states
- Unfolding RNNs through time
- Backpropagation through time (BPTT)
- Vanishing gradient in RNNs
- RNN applications and limitations
Chapter 8: Long Short-Term Memory (LSTM)
Solving the Vanishing Gradient Problem
- LSTM cell architecture
- Forget, input, and output gates
- Cell state and hidden state
- LSTM vs RNN comparison
- GRU (Gated Recurrent Unit) introduction