The TensorFlow Workshop: A hands-on guide to building deep learning models from scratch using real-world datasets
- Length: 650 pages
- Edition: 1
- Language: English
- Publisher: Packt Publishing
- Publication Date: 2022-01-11
- ISBN-10: 1800205252
- ISBN-13: 9781800205253
- Sales Rank: #1297652 (See Top 100 Books)
Get started with TensorFlow fundamentals to build and train deep learning models with real-world data, practical exercises, and challenging activities
Key Features
- Understand the fundamentals of tensors, neural networks, and deep learning
- Discover how to implement and fine-tune deep learning models for real-world datasets
- Build your experience and confidence with hands-on exercises and activities
Book Description
Getting to grips with tensors, deep learning, and neural networks can be intimidating and confusing for anyone, no matter their experience level. The breadth of information out there, often written at a very high level and aimed at advanced practitioners, can make getting started even more challenging.
If this sounds familiar to you, The TensorFlow Workshop is here to help. Combining clear explanations, realistic examples, and plenty of hands-on practice, it’ll quickly get you up and running.
You’ll start off with the basics – learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, you’ll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models.
Building on this solid foundation, you’ll learn how to tune models and work with different types of neural network, getting hands-on with real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing.
By the end of this deep learning book, you’ll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow.
What you will learn
- Get to grips with TensorFlow’s mathematical operations
- Pre-process a wide variety of tabular, sequential, and image data
- Understand the purpose and usage of different deep learning layers
- Perform hyperparameter-tuning to prevent overfitting of training data
- Use pre-trained models to speed up the development of learning models
- Generate new data based on existing patterns using generative models
Who This Book Is For
This TensorFlow book is for anyone who wants to develop their understanding of deep learning and get started building neural networks with TensorFlow. Basic knowledge of Python programming and its libraries, as well as a general understanding of the fundamentals of data science and machine learning, will help you grasp the topics covered in this book more easily.
The TensorFlow Workshop Preface About the Book About the Authors Who This Book Is For About the Chapters Conventions Code Presentation Minimum Hardware Requirements Downloading the Code Bundle Setting Up Your Environment Installing Anaconda on Your System Launching Jupyter Notebook Installing the tensorflow Virtual Environment Get in Touch Please Leave a Review 1. Introduction to Machine Learning with TensorFlow Introduction Implementing Artificial Neural Networks in TensorFlow Advantages of TensorFlow Disadvantages of TensorFlow The TensorFlow Library in Python Exercise 1.01: Verifying Your Version of TensorFlow Introduction to Tensors Scalars, Vectors, Matrices, and Tensors Exercise 1.02: Creating Scalars, Vectors, Matrices, and Tensors in TensorFlow Tensor Addition Exercise 1.03: Performing Tensor Addition in TensorFlow Activity 1.01: Performing Tensor Addition in TensorFlow Reshaping Tensor Transposition Exercise 1.04: Performing Tensor Reshaping and Transposition in TensorFlow Activity 1.02: Performing Tensor Reshaping and Transposition in TensorFlow Tensor Multiplication Exercise 1.05: Performing Tensor Multiplication in TensorFlow Optimization Forward Propagation Backpropagation Learning Optimal Parameters Optimizers in TensorFlow Activation functions Activity 1.03: Applying Activation Functions Summary 2. Loading and Processing Data Introduction Exploring Data Types Data Preprocessing Processing Tabular Data Exercise 2.01: Loading Tabular Data and Rescaling Numerical Fields Activity 2.01: Loading Tabular Data and Rescaling Numerical Fields with a MinMax Scaler Exercise 2.02: Preprocessing Non-Numerical Data Processing Image Data Exercise 2.03: Loading Image Data for Batch Processing Image Augmentation Activity 2.02: Loading Image Data for Batch Processing Text Processing Exercise 2.04: Loading Text Data for TensorFlow Models Audio Processing Exercise 2.05: Loading Audio Data for TensorFlow Models Activity 2.03: Loading Audio Data for Batch Processing Summary 3. TensorFlow Development Introduction TensorBoard Exercise 3.01: Using TensorBoard to Visualize Matrix Multiplication Activity 3.01: Using TensorBoard to Visualize Tensor Transformations Exercise 3.02: Using TensorBoard to Visualize Image Batches TensorFlow Hub Exercise 3.03: Downloading a Model from TensorFlow Hub Google Colab Advantages of Google Colab Disadvantages of Google Colab Development on Google Colab Exercise 3.04: Using Google Colab to Visualize Data Activity 3.02: Performing Word Embedding from a Pre-Trained Model from TensorFlow Hub Summary 4. Regression and Classification Models Introduction Sequential Models Keras Layers Exercise 4.01: Creating an ANN with TensorFlow Model Fitting The Loss Function Model Evaluation Exercise 4.02: Creating a Linear Regression Model as an ANN with TensorFlow Exercise 4.03: Creating a Multi-Layer ANN with TensorFlow Activity 4.01: Creating a Multi-Layer ANN with TensorFlow Classification Models Exercise 4.04: Creating a Logistic Regression Model as an ANN with TensorFlow Activity 4.02: Creating a Multi-Layer Classification ANN with TensorFlow Summary 5. Classification Models Introduction Binary Classification Logistic Regression Binary Cross-Entropy Binary Classification Architecture Exercise 5.01: Building a Logistic Regression Model Metrics for Classifiers Accuracy and Null Accuracy Precision, Recall, and the F1 Score Confusion Matrices Exercise 5.02: Classification Evaluation Metrics Multi-Class Classification The Softmax Function Categorical Cross-Entropy Multi-Class Classification Architecture Exercise 5.03: Building a Multi-Class Model Activity 5.01: Building a Character Recognition Model with TensorFlow Multi-Label Classification Activity 5.02: Building a Movie Genre Tagging a Model with TensorFlow Summary 6. Regularization and Hyperparameter Tuning Introduction Regularization Techniques L1 Regularization L2 Regularization Exercise 6.01: Predicting a Connect-4 Game Outcome Using the L2 Regularizer Dropout Regularization Exercise 6.02: Predicting a Connect-4 Game Outcome Using Dropout Early Stopping Activity 6.01: Predicting Income with L1 and L2 Regularizers Hyperparameter Tuning Keras Tuner Random Search Exercise 6.03: Predicting a Connect-4 Game Outcome Using Random Search from Keras Tuner Hyperband Exercise 6.04: Predicting a Connect-4 Game Outcome Using Hyperband from Keras Tuner Bayesian Optimization Activity 6.02: Predicting Income with Bayesian Optimization from Keras Tuner Summary 7. Convolutional Neural Networks Introduction CNNs Image Representation The Convolutional Layer Creating the Model Exercise 7.01: Creating the First Layer to Build a CNN Pooling Layer Max Pooling Average Pooling Exercise 7.02: Creating a Pooling Layer for a CNN Flattening Layer Exercise 7.03: Building a CNN Image Augmentation Batch Normalization Exercise 7.04: Building a CNN with Additional Convolutional Layers Binary Image Classification Object Classification Exercise 7.05: Building a CNN Activity 7.01: Building a CNN with More ANN Layers Summary 8. Pre-Trained Networks Introduction ImageNet Transfer Learning Exercise 8.01: Classifying Cats and Dogs with Transfer Learning Fine-Tuning Activity 8.01: Fruit Classification with Fine-Tuning TensorFlow Hub Feature Extraction Activity 8.02: Transfer Learning with TensorFlow Hub Summary 9. Recurrent Neural Networks Introduction Sequential Data Examples of Sequential Data Exercise 9.01: Training an ANN for Sequential Data – Nvidia Stock Prediction Recurrent Neural Networks RNN Architecture Vanishing Gradient Problem Long Short-Term Memory Network Exercise 9.02: Building an RNN with an LSTM Layer – Nvidia Stock Prediction Activity 9.01: Building an RNN with Multiple LSTM Layers to Predict Power Consumption Natural Language Processing Data Preprocessing Dataset Cleaning Generating a Sequence and Tokenization Padding Sequences Back Propagation Through Time (BPTT) Exercise 9.03: Building an RNN with an LSTM Layer for Natural Language Processing Activity 9.02: Building an RNN for Predicting Tweets' Sentiment Summary 10. Custom TensorFlow Components Introduction TensorFlow APIs Implementing Custom Loss Functions Building a Custom Loss Function with the Functional API Building a Custom Loss Function with the Subclassing API Exercise 10.01: Building a Custom Loss Function Implementing Custom Layers Introduction to ResNet Blocks Building Custom Layers with the Functional API Building Custom Layers with Subclassing Exercise 10.02: Building a Custom Layer Activity 10.01: Building a Model with Custom Layers and a Custom Loss Function Summary 11. Generative Models Introduction Text Generation Extending NLP Sequence Models to Generate Text Dataset Cleaning Generating a Sequence and Tokenization Generating a Sequence of n-gram Tokens Padding Sequences Exercise 11.01: Generating Text Generative Adversarial Networks The Generator Network The Discriminator Network The Adversarial Network Combining the Generative and Discriminative Models Generating Real Samples with Class Labels Creating Latent Points for the Generator Using the Generator to Generate Fake Samples and Class Labels Evaluating the Discriminator Model Training the Generator and Discriminator Creating the Latent Space, Generator, Discriminator, GAN, and Training Data Exercise 11.02: Generating Sequences with GANs Deep Convolutional Generative Adversarial Networks (DCGANs) Training a DCGAN Exercise 11.03: Generating Images with DCGAN Activity 11.01: Generating Images Using GANs Summary Appendix 1. Introduction to Machine Learning with TensorFlow Activity 1.01: Performing Tensor Addition in TensorFlow Activity 1.02: Performing Tensor Reshaping and Transposition in TensorFlow Activity 1.03: Applying Activation Functions 2. Loading and Processing Data Activity 2.01: Loading Tabular Data and Rescaling Numerical Fields with a MinMax Scaler Activity 2.02: Loading Image Data for Batch Processing Activity 2.03: Loading Audio Data for Batch Processing 3. TensorFlow Development Activity 3.01: Using TensorBoard to Visualize Tensor Transformations Activity 3.02: Performing Word Embedding from a Pre-Trained Model from TensorFlow Hub 4. Regression and Classification Models Activity 4.01: Creating a Multi-Layer ANN with TensorFlow Activity 4.02: Creating a Multi-Layer Classification ANN with TensorFlow 5. Classification Models Activity 5.01: Building a Character Recognition Model with TensorFlow Activity 5.02: Building a Movie Genre Tagging a Model with TensorFlow 6. Regularization and Hyperparameter Tuning Activity 6.01: Predicting Income with L1 and L2 Regularizers Activity 6.02: Predicting Income with Bayesian Optimization from Keras Tuner 7. Convolutional Neural Networks Activity 7.01: Building a CNN with More ANN Layers 8. Pre-Trained Networks Activity 8.01: Fruit Classification with Fine-Tuning Activity 8.02: Transfer Learning with TensorFlow Hub 9. Recurrent Neural Networks Activity 9.01: Building an RNN with Multiple LSTM Layers to Predict Power Consumption Activity 9.02: Building an RNN for Predicting Tweets' Sentiment 10. Custom TensorFlow Components Activity 10.01: Building a Model with Custom Layers and a Custom Loss Function 11. Generative Models Activity 11.01: Generating Images Using GANs Hey!
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