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
Understanding Feature Maps
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Explains the theoretical foundation for deep learning, covering convolutional networks, filters, and feature maps, and their role in feature extraction across layers.
- ImageNet Classification with Deep Convolutional Neural Networks, Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, 2012 Advances in Neural Information Processing Systems 25 (NIPS 2012), Vol. 25 (Curran Associates, Inc., Red Hook, NY) - This influential paper showcased deep CNNs for image classification, demonstrating learned features across multiple layers.
- tf.keras.layers.Conv2D, Keras team, 2024 (TensorFlow team) - Official documentation for the
tf.keras.layers.Conv2Dlayer, describing its parameters, input/output shapes, and how thefiltersargument determines the number of output feature maps.