Introduction to Deep Learning
Chapter 1: Neural Network Foundations
From Machine Learning to Deep Learning
Biological Inspiration: The Neuron
The Artificial Neuron: A Mathematical Model
The Perceptron: The Simplest Neural Network
Limitations of Single-Layer Perceptrons
Multi-Layer Perceptrons (MLPs): Adding Depth
Hands-on Practical: Building a Simple Perceptron Model
Quiz for Chapter 1
Chapter 2: Activation Functions and Network Architecture
The Role of Activation Functions
Sigmoid Activation
Hyperbolic Tangent (Tanh) Activation
Rectified Linear Unit (ReLU)
Variants of ReLU (Leaky ReLU, PReLU, ELU)
Choosing the Right Activation Function
Understanding Network Layers: Input, Hidden, Output
Designing Feedforward Network Architectures
Hands-on Practical: Implementing Different Activations
Quiz for Chapter 2
Chapter 3: Training Neural Networks: Loss and Optimization
Measuring Performance: Loss Functions
Common Loss Functions for Regression (MSE, MAE)
Common Loss Functions for Classification (Cross-Entropy)
Optimization: Finding the Best Weights
Gradient Descent Algorithm
Learning Rate
Stochastic Gradient Descent (SGD)
Challenges with Gradient Descent
Hands-on Practical: Visualizing Gradient Descent
Quiz for Chapter 3
Chapter 4: Backpropagation and Advanced Optimization
Calculating Gradients: The Chain Rule
Computational Graphs
The Backpropagation Algorithm Explained
Forward Pass vs. Backward Pass
Gradient Descent with Momentum
RMSprop Optimizer
Adam Optimizer
Choosing an Optimization Algorithm
Hands-on Practical: Backpropagation Step-by-Step
Quiz for Chapter 4
Chapter 5: Building and Training Deep Neural Networks
Introduction to Deep Learning Frameworks (TensorFlow/Keras, PyTorch)
Setting up the Development Environment
Preparing Data for Neural Networks
Defining a Feedforward Network Model
Weight Initialization Strategies
Compiling the Model: Loss and Optimizer Selection
Training the Model: The fit Method
Monitoring Training Progress (Loss and Metrics)
Evaluating Model Performance
Hands-on Practical: Training a Classifier on MNIST
Quiz for Chapter 5
Chapter 6: Regularization and Improving Performance
The Problem of Overfitting
Regularization Techniques Overview
L1 and L2 Regularization
Dropout Regularization
Early Stopping
Batch Normalization
Hyperparameter Tuning Fundamentals
Strategies for Hyperparameter Search (Grid Search, Random Search)
Hands-on Practical: Applying Dropout and Early Stopping
Quiz for Chapter 6
Chapter 7: Introduction to Specialized Architectures
Limitations of Feedforward Networks
Convolutional Neural Networks (CNNs): Motivation
Core CNN Operations: Convolution
Core CNN Operations: Pooling
Typical CNN Architecture
Recurrent Neural Networks (RNNs): Motivation
The Concept of Recurrence and Hidden State
Basic RNN Architecture
Challenges with Simple RNNs (Vanishing/Exploding Gradients)
Overview: LSTMs and GRUs
Quiz for Chapter 7
Limitations of Feedforward Networks
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A comprehensive textbook on deep learning that thoroughly covers feedforward networks, their practical limitations, and the architectural solutions provided by convolutional and recurrent networks.
- Gradient-Based Learning Applied to Document Recognition, Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner, 1998 Proceedings of the IEEE, Vol. 86 (IEEE) DOI: 10.1109/5.726791 - A seminal paper introducing convolutional neural networks (LeNet-5), detailing their architectural benefits for image recognition by exploiting local correlations and translation invariance, thereby overcoming limitations of fully connected layers.
- Long Short-Term Memory, Sepp Hochreiter, Jürgen Schmidhuber, 1997 Neural Computation, Vol. 9 (MIT Press) DOI: 10.1162/neco.1997.9.8.1735 - This foundational paper introduces Long Short-Term Memory (LSTM) networks, an architecture highly effective for enabling recurrent networks to learn and remember long-term dependencies in sequential data, addressing a major weakness of simpler feedforward or recurrent models.
- Convolutional Neural Networks for Visual Recognition (CS231n), Stanford University, 2024 (Stanford University) - Official course notes offering a clear explanation of convolutional neural networks, including an analysis of why feedforward networks are insufficient for image data and how CNNs address these challenges through architectural design. (Spring 2023 version)