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
Backpropagation
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- Learning representations by back-propagating errors, David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams, 1986 Nature, Vol. 323 (Springer Nature) DOI: 10.1038/323533a0 - The seminal paper that popularized the backpropagation algorithm for training multi-layer neural networks.
- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - A comprehensive textbook with a detailed and rigorous mathematical treatment of backpropagation and its role in deep neural networks.
- Neural Networks and Deep Learning, Michael Nielsen, 2019 (Online) - An excellent, freely available online book providing an intuitive yet mathematically precise explanation of backpropagation, designed for self-study.
- CS231n: Deep Learning for Computer Vision, Lecture Notes on Backpropagation, Stanford University, 2023 (Stanford University) - Provides a clear, often visually supported, explanation of backpropagation from a computational graph perspective, which is practical for deep learning.