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
From Machine Learning to Deep Learning
Was this section helpful?
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A comprehensive textbook that covers the theoretical foundations, algorithms, and applications of deep learning, including the distinction from traditional machine learning and the concept of representation learning.
- Neural Networks and Deep Learning, Michael Nielsen, 2015 (Determination Press) - An accessible online book that introduces the core concepts of neural networks and deep learning from first principles, explaining the motivation behind automated feature learning.
- CS230: Deep Learning (Fall 2018 Lecture Notes), Andrew Ng and Kian Katanforoosh, 2018 (Stanford University) - Lecture notes from a foundational Stanford course that introduces deep learning concepts, including the distinction between traditional machine learning and the role of automated feature learning in deep neural networks.
- Representation Learning: A Review and New Perspectives, Yoshua Bengio, Aaron Courville, and Pascal Vincent, 2013 IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35 (IEEE) DOI: 10.1109/TPAMI.2013.29 - A seminal review paper that formally introduces and discusses representation learning, highlighting its importance in deep learning and how it addresses limitations of hand-engineered features in traditional machine learning.