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
Choosing an Optimization Algorithm
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- Adam: A Method for Stochastic Optimization, Diederik P. Kingma and Jimmy Ba, 2014 International Conference on Learning Representations (ICLR 2015) DOI: 10.48550/arXiv.1412.6980 - Introduces the Adam optimization algorithm, combining adaptive learning rates and momentum.
- Decoupled Weight Decay Regularization, Ilya Loshchilov and Frank Hutter, 2019 International Conference on Learning Representations (ICLR 2019) DOI: 10.48550/arXiv.1711.05101 - Proposes AdamW, a variant of Adam that decouples weight decay from the adaptive learning rate mechanism, improving regularization.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Comprehensive textbook covering various optimization algorithms, including SGD, Momentum, RMSprop, and Adam, along with their theoretical foundations and practical considerations.
- The Marginal Value of Adaptive Gradient Methods in Machine Learning, Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern, Nathan Srebro, Benjamin Recht, 2017 Advances in Neural Information Processing Systems 30 (NeurIPS 2017) DOI: 10.48550/arXiv.1705.08292 - Critically analyzes the generalization performance of adaptive gradient methods (like Adam) compared to SGD, suggesting that SGD can find flatter minima.