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
Using Callbacks
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- Keras Callbacks API, Keras Team, 2024 (TensorFlow) - The official documentation for Keras callbacks, detailing their usage, available methods, and parameters for all built-in callbacks discussed.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Aurélien Géron, 2022 (O'Reilly Media) - A practical guide offering comprehensive coverage of Keras, including how to effectively use callbacks like ModelCheckpoint, EarlyStopping, and ReduceLROnPlateau for optimizing deep learning training and workflow.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A foundational textbook covering the theoretical principles behind deep learning, including optimization algorithms, regularization, and strategies to prevent overfitting, which callbacks help to manage in practice.
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (CS230 Lecture Notes), Andrew Ng, 2018 (deeplearning.ai) - Provides detailed explanations of techniques for improving neural network performance and training workflow, including regularization methods, optimization strategies, and hyperparameter tuning, which callbacks help implement.