| Description |
1 online resource |
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text file |
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PDF |
| Contents |
Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Preface -- Chapter 1: TensorFlow Jump Start -- What Is TensorFlow 2.0? -- TensorFlow 2.x Platform -- Training -- Data Preparation -- Designing Model -- Distribution Strategy -- Analysis -- Model Saving -- Deployment -- What TensorFlow 2.x Offers? -- The tf.keras in TensorFlow -- Eager Execution -- Distribution -- TensorBoard -- Vision Kit -- Voice Kit -- Edge TPU -- Pre-trained Models for AIY Kits -- Data Pipelines -- Installation -- Installation -- Docker Installation -- No Installation |
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Testing -- Summary -- Chapter 2: A Closer Look at TensorFlow -- A Trivial Machine Learning Application -- Creating Colab Notebook -- Imports -- Importing TensorFlow 2.x -- Importing numpy -- Setting Up Data -- Defining Neural Network -- Compiling Model -- Training Network -- Examining Training Output -- Predicting -- Full Source Code -- Binary Classification in TensorFlow -- Setting Up Project -- Imports -- Mounting Google Drive -- Loading Data -- Shuffling Data -- Examining Data -- Data Preprocessing -- Checking Nulls -- Selecting Features and Labels -- Encoding Categorical Columns |
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Scaling Numerical Values -- Creating Training and Testing Datasets -- Defining ANN -- Compiling Model -- Model Training -- Performance Evaluation -- Predicting on Test Data -- Confusion Matrix -- Predicting on Unseen Data -- Full Source Code -- Summary -- Chapter 3: Deep Dive in tf.keras -- Getting Started -- Functional API for Model Building -- Sequential Models -- Model Subclassing -- Predefined Layers -- Custom Layers -- Saving Models -- Whole-Model Saving -- Export to SavedModel Format -- Saving Architecture -- Saving Weights -- Saving to JSON -- Convolutional Neural Networks |
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Image Classification with CNN -- Creating Project -- Image Dataset -- Loading Dataset -- Creating Training/Testing Datasets -- Preparing Data for Model Training -- Creating Validation Dataset -- Augmenting Data -- Model Development -- Train/Evaluate/Display Function -- Predict Function -- Defining Models -- A Model with 2 Convolutional Layers -- Model_2 with 4 Convolutional Layers -- Third Model: 6 Convolutional layers with 32, 64 and 128 filters respectively -- Fourth Model: Addition of dropout layer -- Model 5 -- Saving Model -- Predicting Unseen Images -- Summary |
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Chapter 4: Transfer Learning -- Knowledge Transfer -- TensorFlow Hub -- Pre-trained Modules -- Using Modules -- ImageNet Classifier -- Setting Up Project -- Classifier URL -- Creating Model -- Preparing Images -- Loading Label Mappings -- Displaying Prediction -- Listing All Classes -- Result Discussions -- Dog Breed Classifier -- Project Description -- Creating Project -- Loading Data -- Setting Up Images and Labels -- Preprocessing Images -- Processing Image -- Associating Labels to Images -- Creating Data Batches -- Display Function for Images -- Selecting Pre-trained Model -- Defining Model |
| Note |
Includes index. |
| Summary |
Develop machine learning models across various domains. This book offers a single source that provides comprehensive coverage of the capabilities of TensorFlow 2 through the use of realistic, scenario-based projects. After learning what's new in TensorFlow 2, you'll dive right into developing machine learning models through applicable projects. This book covers a wide variety of ANN architectures-starting from working with a simple sequential network to advanced CNN, RNN, LSTM, DCGAN, and so on. A full chapter is devoted to each kind of network and each chapter consists of a full project describing the network architecture used, the theory behind that architecture, what data set is used, the pre-processing of data, model training, testing and performance optimizations, and analysis. This practical approach can either be used from the beginning through to the end or, if you're already familiar with basic ML models, you can dive right into the application that interests you. Line-by-line explanations on major code segments help to fill in the details as you work and the entire project source is available to you online for learning and further experimentation. With Artificial Neural Networks with TensorFlow 2 you'll see just how wide the range of TensorFlow's capabilities are. What You'll Learn Develop Machine Learning Applications Translate languages using neural networks Compose images with style transfer Who This Book Is For Beginners, practitioners, and hard-cored developers who want to master machine and deep learning with TensorFlow 2. The reader should have working concepts of ML basics and terminologies. |
| Subject |
TensorFlow.
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Neural networks (Computer science)
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Machine learning.
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Neural Networks, Computer |
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Machine Learning |
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Réseaux neuronaux (Informatique) |
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Apprentissage automatique. |
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Neural networks (Computer science) |
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Machine learning |
| Other Form: |
Print version: Sarang, P. G. (Poornachandra G.). Artificial neural networks with TensorFlow 2. [Place of publication not identified] : Apress, 2021 1484261496 9781484261491 (OCoLC)1158472604 |
| ISBN |
9781484261507 (electronic bk.) |
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148426150X (electronic bk.) |
| Standard No. |
10.1007/978-1-4842-6150-7 doi |
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