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
Batches and Epochs
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- Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - Covers the theoretical and practical aspects of training deep neural networks, including batching, epochs, and various optimization algorithms.
model.fit(): Train a model with given data, Keras team, 2024 (Keras) - Official documentation for Keras'sfitmethod, detailing parameters likebatch_sizeandepochsand their usage.- Neural Networks Part 3: Learning and Evaluation, Stanford University CS231n: Convolutional Neural Networks for Visual Recognition, 2024 - A section from Stanford's renowned deep learning course, providing an in-depth explanation of mini-batch gradient descent, epochs, and their practical implications.
- On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima, Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang, 2017 ICLR 2017 DOI: 10.48550/arXiv.1609.04836 - Explores the impact of batch size on the generalization performance of deep neural networks, discussing the 'generalization gap' and the preference for flat vs. sharp minima.