book

The Deep Learning with Keras Workshop

by Matthew Moocarme, Mahla Abdolahnejad, Ritesh Bhagwat

July 2020

Intermediate to advanced

496 pages

9h 10m

English

Packt Publishing

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About the BookAudienceAbout the ChaptersConventionsCode PresentationSetting up Your EnvironmentInstalling AnacondaInstalling LibrariesRunning Jupyter Notebook Accessing the Code Files
IntroductionData RepresentationTables of DataLoading DataExercise 1.01: Loading a Dataset from the UCI Machine Learning RepositoryData PreprocessingExercise 1.02: Cleaning the DataAppropriate Representation of the DataExercise 1.03: Appropriate Representation of the DataLife Cycle of Model CreationMachine Learning Librariesscikit-learnKerasAdvantages of KerasDisadvantages of KerasMore Than Building ModelsModel TrainingClassifiers and Regression ModelsClassification TasksRegression TasksTraining Datasets and Test DatasetsModel Evaluation MetricsExercise 1.04: Creating a Simple ModelModel TuningBaseline ModelsExercise 1.05: Determining a Baseline ModelRegularizationCross-ValidationActivity 1.01: Adding Regularization to the ModelSummary
IntroductionAdvantages of ANNs over Traditional Machine Learning AlgorithmsAdvantages of Traditional Machine Learning Algorithms over ANNsHierarchical Data RepresentationLinear TransformationsScalars, Vectors, Matrices, and TensorsTensor AdditionExercise 2.01: Performing Various Operations with Vectors, Matrices, and TensorsReshapingMatrix TranspositionExercise 2.02: Matrix Reshaping and TranspositionMatrix MultiplicationExercise 2.03: Matrix MultiplicationExercise 2.04: Tensor MultiplicationIntroduction to KerasLayer TypesActivation FunctionsModel FittingActivity 2.01: Creating a Logistic Regression Model Using KerasSummary
IntroductionBuilding Your First Neural NetworkLogistic Regression to a Deep Neural NetworkActivation FunctionsForward Propagation for Making PredictionsLoss FunctionBackpropagation for Computing Derivatives of Loss FunctionGradient Descent for Learning ParametersExercise 3.01: Neural Network Implementation with KerasActivity 3.01: Building a Single-Layer Neural Network for Performing Binary ClassificationModel EvaluationEvaluating a Trained Model with KerasSplitting Data into Training and Test SetsUnderfitting and OverfittingEarly StoppingActivity 3.02: Advanced Fibrosis Diagnosis with Neural NetworksSummary
IntroductionCross-ValidationDrawbacks of Splitting a Dataset Only OnceK-Fold Cross-ValidationLeave-One-Out Cross-ValidationComparing the K-Fold and LOO MethodsCross-Validation for Deep Learning ModelsKeras Wrapper with scikit-learnExercise 4.01: Building the Keras Wrapper with scikit-learn for a Regression ProblemCross-Validation with scikit-learnCross-Validation Iterators in scikit-learnExercise 4.02: Evaluating Deep Neural Networks with Cross-ValidationActivity 4.01: Model Evaluation Using Cross-Validation for an Advanced Fibrosis Diagnosis ClassifierModel Selection with Cross-ValidationCross-Validation for Model Evaluation versus Model SelectionExercise 4.03: Writing User-Defined Functions to Implement Deep Learning Models with Cross-ValidationActivity 4.02: Model Selection Using Cross-Validation for the Advanced Fibrosis Diagnosis ClassifierActivity 4.03: Model Selection Using Cross-validation on a Traffic Volume DatasetSummary
IntroductionRegularizationThe Need for RegularizationReducing Overfitting with RegularizationL1 and L2 RegularizationL1 and L2 Regularization FormulationL1 and L2 Regularization Implementation in KerasActivity 5.01: Weight Regularization on an Avila Pattern ClassifierDropout RegularizationPrinciples of Dropout RegularizationReducing Overfitting with DropoutExercise 5.01: Dropout Implementation in KerasActivity 5.02: Dropout Regularization on the Traffic Volume DatasetOther Regularization MethodsEarly StoppingExercise 5.02: Implementing Early Stopping in KerasData AugmentationAdding NoiseHyperparameter Tuning with scikit-learnGrid Search with scikit-learnRandomized Search with scikit-learnActivity 5.03: Hyperparameter Tuning on the Avila Pattern ClassifierSummary
IntroductionAccuracyExercise 6.01: Calculating Null Accuracy on a Pacific Hurricanes DatasetAdvantages and Limitations of AccuracyImbalanced DatasetsWorking with Imbalanced DatasetsConfusion MatrixMetrics Computed from a Confusion MatrixExercise 6.02: Computing Accuracy and Null Accuracy with APS Failure for Scania Trucks DataActivity 6.01: Computing the Accuracy and Null Accuracy of a Neural Network When We Change the Train/Test SplitExercise 6.03: Deriving and Computing Metrics Based on a Confusion MatrixActivity 6.02: Calculating the ROC Curve and AUC ScoreSummary
IntroductionComputer VisionConvolutional Neural NetworksThe Architecture of a CNNInput ImageConvolution LayerThe Pooling LayerFlatteningImage AugmentationAdvantages of Image AugmentationExercise 7.01: Building a CNN and Identifying Images of Cars and FlowersActivity 7.01: Amending Our Model with Multiple Layers and the Use of softmaxExercise 7.02: Amending Our Model by Reverting to the Sigmoid Activation FunctionExercise 7.03: Changing the Optimizer from Adam to SGDExercise 7.04: Classifying a New ImageActivity 7.02: Classifying a New ImageSummary
IntroductionPre-Trained Sets and Transfer LearningFeature ExtractionFine-Tuning a Pre-Trained Network The ImageNet DatasetSome Pre-Trained Networks in KerasExercise 8.01: Identifying an Image Using the VGG16 NetworkActivity 8.01: Using the VGG16 Network to Train a Deep Learning Network to Identify ImagesExercise 8.02: Classifying Images That Are Not Present in the ImageNet DatabaseExercise 8.03: Fine-Tuning the VGG16 ModelExercise 8.04: Image Classification with ResNetActivity 8.02: Image Classification with ResNet Summary

IntroductionSequential Memory and Sequential Modeling Recurrent Neural Networks (RNNs)The Vanishing Gradient ProblemA Brief Explanation of the Exploding Gradient ProblemLong 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 unitsActivity 9.02: Predicting Amazon's Stock Price with Added RegularizationActivity 9.03: Predicting the Trend of Amazon's Stock Price Using an LSTM with an Increasing Number of LSTM Neurons (100 Units)Summary
1. Introduction to Machine Learning with KerasActivity 1.01: Adding Regularization to the Model2. Machine Learning versus Deep LearningActivity 2.01: Creating a Logistic Regression Model Using Keras3. Deep Learning with KerasActivity 3.01: Building a Single-Layer Neural Network for Performing Binary ClassificationActivity 3.02: Advanced Fibrosis Diagnosis with Neural Networks4. Evaluating Your Model with Cross-Validation Using Keras WrappersActivity 4.01: Model Evaluation Using Cross-Validation for an Advanced Fibrosis Diagnosis ClassifierActivity 4.02: Model Selection Using Cross-Validation for the Advanced Fibrosis Diagnosis ClassifierActivity 4.03: Model Selection Using Cross-validation on a Traffic Volume Dataset5. Improving Model AccuracyActivity 5.01: Weight Regularization on an Avila Pattern ClassifierActivity 5.02: Dropout Regularization on the Traffic Volume DatasetActivity 5.03: Hyperparameter Tuning on the Avila Pattern Classifier6. Model EvaluationActivity 6.01: Computing the Accuracy and Null Accuracy of a Neural Network When We Change the Train/Test SplitActivity 6.02: Calculating the ROC Curve and AUC Score7. Computer Vision with Convolutional Neural NetworksActivity 7.01: Amending Our Model with Multiple Layers and the Use of softmaxActivity 7.02: Classifying a New Image8. Transfer Learning and Pre-Trained ModelsActivity 8.01: Using the VGG16 Network to Train a Deep Learning Network to Identify ImagesActivity 8.02: Image Classification with ResNet 9. Sequential Modeling with Recurrent Neural NetworksActivity 9.01: Predicting the Trend of Amazon's Stock Price Using an LSTM with 50 Units (Neurons)Activity 9.02: Predicting Amazon's Stock Price with Added RegularizationActivity 9.03: Predicting the Trend of Amazon's Stock Price Using an LSTM with an Increasing Number of LSTM Neurons (100 Units)

Overview

The Deep Learning with Keras Workshop is a comprehensive guide for beginners to understand and apply deep learning concepts using the Keras framework. Starting from the fundamentals of data preprocessing and progressing through to building, training, and optimizing neural networks, this book offers a hands-on learning experience.

What this Book will help me do

Gain a strong foundation in neural network principles and their applications.
Learn to build and train models using the popular Keras framework effectively.
Understand and implement advanced techniques like convolutional and recurrent neural networks.
Master the art of tuning and evaluating deep learning models using cross-validation and other metrics.
Develop the skills to solve real-world data problems through practical exercises.

Author(s)

Matthew Moocarme, Mahla Abdolahnejad, and Ritesh Bhagwat bring together their expertise in machine learning and neural network frameworks to create a book that is both practical and engaging. With years of experience in teaching and applying artificial intelligence, their practical approach ensures readers can understand and apply deep learning concepts.

Who is it for?

This book is ideal for data science practitioners and programmers who have a basic understanding of Python and wish to advance their knowledge in machine learning and deep learning. It is well-suited for learners who are comfortable with foundational topics like statistics and regression and now seek to gain practical expertise in neural networks and Keras. If you aim to expand your skill set to include state-of-the-art model training techniques, this book is an excellent choice.