Deep Learning Fundamentals with Keras
Chapter 1: Introduction to Deep Learning Concepts
What is Deep Learning?
Artificial Neurons and Network Structure
Supervised Learning with Neural Networks
Introduction to Keras
Setting Up Your Keras Environment
Hands-on Practical: Environment Verification
Chapter 2: Building Neural Networks with Keras
The Sequential API
Common Layer Types: Dense
Activation Functions
Specifying Input Shape
The Functional API
Model Summary and Visualization
Practice: Building Your First Network with Keras
Quiz for Chapter 2
Chapter 3: Training Deep Neural Networks
The Compilation Step
Understanding Loss Functions
Optimization Algorithms
Backpropagation
The Training Loop: fit() Method
Batches and Epochs
Validation Data and Monitoring Performance
Model Evaluation: evaluate() Method
Hands-on Practical: Training a Simple Classifier
Quiz for Chapter 3
Chapter 4: Convolutional Neural Networks (CNNs)
Introduction to Convolutional Networks
Convolutional Layers (Conv2D)
Pooling Layers (MaxPooling2D)
Building a Simple CNN Architecture
Flattening and Dense Layers in CNNs
Working with Image Data in Keras
Understanding Feature Maps
Practice: Implementing a CNN for Image Classification
Quiz for Chapter 4
Chapter 5: Recurrent Neural Networks (RNNs)
Introduction to Sequence Data
Recurrent Neural Network Concepts
SimpleRNN Layer in Keras
The Vanishing Gradient Problem
Long Short-Term Memory (LSTM) Networks
LSTM Layer in Keras
Gated Recurrent Units (GRUs)
Preparing Sequence Data for RNNs
Practice: Building an RNN/LSTM for Text Classification
Quiz for Chapter 5
Chapter 6: Improving Model Performance and Workflow
Understanding Overfitting and Underfitting
Regularization Techniques: L1, L2, Dropout
Data Augmentation
Using Callbacks
Saving and Loading Models
Introduction to TensorBoard
Hyperparameter Tuning Concepts
Hands-on Practical: Applying Improvement Techniques
Quiz for Chapter 6
Optimization Algorithms
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Comprehensive coverage of optimization algorithms, including gradient descent variants, momentum, and adaptive methods like Adam and RMSprop.
- Adam: A Method for Stochastic Optimization, Diederik P. Kingma, Jimmy Ba, 2015 International Conference for Learning Representations DOI: 10.48550/arXiv.1412.6980 - The original paper introducing the Adam optimization algorithm, outlining its formulation and empirical advantages.
- Keras Optimizers, Keras team, 2024 - Official Keras API documentation for optimization algorithms, with practical usage examples and parameter configurations.
- Optimizers and Learning Rate Scheduling, Stanford University CS231n team, 2023 - Provides detailed explanations of various optimization algorithms, including RMSprop, momentum, and different learning rate strategies.