The Deep Learning with Keras Workshop: An Interactive Approach to Understanding Deep Learning with Keras, 2nd Edition

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Cut through the noise and get real results with a step-by-step approach to understanding deep learning with Keras programming Key Features Ideal for those getting started with Keras for the first time A step-by-step Keras tutorial with exercises and activities that help build key skills Structured to let you progress at your own pace, on your own terms Use your physical print copy to redeem free access to the online interactive editionBook Description You already know that you want to learn Keras, and a smarter way to learn is to learn by doing. The Deep Learning with Keras Workshop focuses on building up your practical skills so that you can develop artificial intelligence applications or build machine learning models with Keras. You'll learn from real examples that lead to real results. Throughout The Deep Learning with Keras Workshop, you'll take an engaging step-by-step approach to understand Keras. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend tinkering with your own neural networks. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Deep Learning with Keras Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your book. Fast-paced and direct, The Deep Learning with Keras Workshop is the ideal companion for those who are just getting started with Keras. You'll build and iterate on your code like a software developer, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead. What you will learn Gain insight into the fundamental concepts of neural networks Learn to think like a data scientist and understand the difference between machine learning and deep learning Discover various techniques to evaluate, tweak, and improve your models Explore different techniques to manipulate your data Explore alternative techniques to verify the accuracy of your modelWho this book is for Our goal at Packt is to help you be successful, in whatever it is that you choose to do. The Deep Learning with Keras Workshop is an ideal tutorial for the programmer who is getting started with Keras and deep learning. Pick up a Workshop today and let Packt help you develop skills that stick with you for life. Table of Contents Introduction to Machine Learning with Keras Machine Learning versus Deep Learning Deep Learning with Keras Evaluating your Model with Cross-Validation Using Keras Wrappers Improving Model Accuracy Model Evaluation Computer Vision with Convolutional Neural Networks Transfer Learning and Pre-Trained Models Sequential Modeling with Recurrent Neural Networks Cover FM Copyright 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 Learning 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 and Pre-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