The Deep Learning with Keras Workshop: Learn how to define and train neural network models with just a few lines of code. Code

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Table of Contents
Preface
Chapter 1: Introduction to Machine Learning with Keras
	Introduction
	Data Representation
		Tables of Data
		Loading Data
		Exercise 1.01: Loading a Dataset from the UCI Machine Learning Repository
	Data Preprocessing
		Exercise 1.02: Cleaning the Data
		Appropriate Representation of the Data
		Exercise 1.03: Appropriate Representation of the Data
	Life Cycle of Model Creation
		Machine Learning Libraries
	scikit-learn
	Keras
		Advantages of Keras
			Disadvantages of Keras
		More than Building Models
	Model Training
		Classifiers and Regression Models
		Classification Tasks
		Regression Tasks
		Training Datasets and Test Datasets
		Model Evaluation Metrics
		Exercise 1.04: Creating a Simple Model
	Model Tuning
		Baseline Models
		Exercise 1.05: Determining a Baseline Model
		Regularization
		Cross-Validation
		Activity 1.01: Adding Regularization to the Model
	Summary
Chapter 2: Machine Learning versus Deep earning
	Introduction
		Advantages of ANNs over Traditional Machine Learning Algorithms
		Advantages of Traditional Machine Learning Algorithms over ANNs
		Hierarchical Data Representation
	Linear Transformations
		Scalars, Vectors, Matrices, and Tensors
		Tensor Addition
		Exercise 2.01: Performing Various Operations with Vectors, Matrices, and Tensors
		Reshaping
		Matrix Transposition
		Exercise 2.02: Matrix Reshaping and Transposition
		Matrix Multiplication
		Exercise 2.03: Matrix Multiplication
		Exercise 2.04: Tensor Multiplication
	Introduction to Keras
		Layer Types
		Activation Functions
		Model Fitting
		Activity 2.01: Creating a Logistic Regression Model Using Keras
	Summary
Chapter 3: Deep Learning with Keras
	Introduction
	Building Your First Neural Network
		Logistic Regression to a Deep Neural Network
		Activation Functions
		Forward Propagation for Making Predictions
		Loss Function
		Backpropagation for Computing Derivatives of Loss Function
		Gradient Descent for Learning Parameters
		Exercise 3.01: Neural Network Implementation with Keras
		Activity 3.01: Building a Single-Layer Neural Network for Performing Binary Classification
	Model Evaluation
		Evaluating a Trained Model with Keras
		Splitting Data into Training and Test Sets
		Underfitting and Overfitting
		Early Stopping
		Activity 3.02: Advanced Fibrosis Diagnosis with Neural Networks
	Summary
Chapter 4: Evaluating Your Model with Cross-Validation Using Keras Wrappers
	Introduction
	Cross-Validation
		Drawbacks of Splitting a Dataset Only Once
		K-Fold Cross-Validation
		Leave-One-Out Cross-Validation
		Comparing the K-Fold and LOO Methods
	Cross-Validation for Deep Learning Models
		Keras Wrapper with scikit-learn
		Exercise 4.01: Building the Keras Wrapper with scikit-learn for a Regression Problem
		Cross-Validation with scikit-learn
		Cross-Validation Iterators in scikit-learn
		Exercise 4.02: Evaluating Deep Neural Networks with Cross-Validation
		Activity 4.01: Model Evaluation Using Cross-Validation for an Advanced Fibrosis Diagnosis Classifier
	Model Selection with Cross-Validation
		Cross-Validation for Model Evaluation versus Model Selection
		Exercise 4.03: Writing User-Defined Functions to Implement Deep Learning Models with Cross-Validation
		Activity 4.02: Model Selection Using Cross-Validation for the Advanced Fibrosis Diagnosis Classifier
		Activity 4.03: Model Selection Using Cross-validation on a Traffic Volume Dataset
	Summary
Chapter 5: Improving Model Accuracy
	Introduction
	Regularization
		The Need for Regularization
		Reducing Overfitting with Regularization
	L1 and L2 Regularization
		L1 and L2 Regularization Formulation
		L1 and L2 Regularization Implementation in Keras
		Activity 5.01: Weight Regularization on an Avila Pattern Classifier
	Dropout Regularization
		Principles of Dropout Regularization
		Reducing Overfitting with Dropout
		Exercise 5.01: Dropout Implementation in Keras
		Activity 5.02: Dropout Regularization on the Traffic Volume Dataset
	Other Regularization Methods
		Early Stopping
		Exercise 5.02: Implementing Early Stopping in Keras
		Data Augmentation
		Adding Noise
	Hyperparameter Tuning with scikit-learn
		Grid Search with scikit-learn
		Randomized Search with scikit-learn
		Activity 5.03: Hyperparameter Tuning on the Avila Pattern Classifier
	Summary
Chapter 6: Model Evaluation
	Introduction
	Accuracy
		Exercise 6.01: Calculating Null Accuracy on a Pacific Hurricanes Dataset
		Advantages and Limitations of Accuracy
	Imbalanced Datasets
		Working with Imbalanced Datasets
	Confusion Matrix
		Metrics Computed from a Confusion Matrix
		Exercise 6.02: Computing Accuracy and Null Accuracy with APS Failure for Scania Trucks Data
		Activity 6.01: Computing the Accuracy and Null Accuracy of a Neural Network When We Change the Train/Test Split
		Exercise 6.03: Deriving and Computing Metrics Based on a Confusion Matrix
		Activity 6.02: Calculating the ROC Curve and AUC Score
	Summary
Chapter 7: Computer Vision with Convolutional Neural Networks
	Introduction
	Computer Vision
	Convolutional Neural Networks
	The Architecture of a CNN
		Input Image
		Convolution Layer
		The Pooling Layer
		Flattening
	Image Augmentation
		Advantages of Image Augmentation
		Exercise 7.01: Building a CNN and Identifying Images of Cars and Flowers
		Activity 7.01: Amending Our Model with Multiple Layers and the Use of softmax
		Exercise 7.02: Amending Our Model by Reverting to the Sigmoid Activation Function
		Exercise 7.03: Changing the Optimizer from Adam to SGD
		Exercise 7.04: Classifying a New Image
		Activity 7.02: Classifying a New Image
	Summary
Chapter 8: Transfer Learning andPre-Trained Models
	Introduction
	Pre-Trained Sets and Transfer Learning
		Feature Extraction
	Fine-Tuning a Pre-Trained Network
		The ImageNet Dataset
		Some Pre-Trained Networks in Keras
		Exercise 8.01: Identifying an Image Using the VGG16 Network
		Activity 8.01: Using the VGG16 Network to Train a Deep Learning Network to Identify Images
		Exercise 8.02: Classifying Images That Are Not Present in the ImageNet Database
		Exercise 8.03: Fine-Tuning the VGG16 Model
		Exercise 8.04: Image Classification with ResNet
		Activity 8.02: Image Classification with ResNet
	Summary
Chapter 9: Sequential Modeling with Recurrent Neural Networks
	Introduction
	Sequential Memory and Sequential Modeling
	Recurrent Neural Networks (RNNs)
		The Vanishing Gradient Problem
		A Brief Explanation of the Exploding Gradient Problem
	Long Short-Term Memory (LSTM)
		Exercise 9.01: Predicting the Trend of Alphabet\'s Stock Price Using an LSTM with 50 Units (Neurons)
		Activity 9.01: Predicting the Trend of Amazon\'s Stock Price Using an LSTM with 50 Units (Neurons)
		Exercise 9.02: Predicting the Trend of Alphabet\'s Stock Price Using an LSTM with 100 units
		Activity 9.02: Predicting Amazon\'s Stock Price with Added Regularization
		Activity 9.03: Predicting the Trend of Amazon\'s Stock Price Using an LSTM with an Increasing Number of LSTM Neurons (100 Units)
	Summary
Appendix
Index