TensorFlow 2 Pocket Reference

This easy-to-use reference for Tensorflow 2 pattern designs in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself. When and why would...

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Detalles Bibliográficos
Autor principal: Tung, KC
Formato: Electrónico eBook
Idioma:Indeterminado
Publicado: [S.l.] : O'Reilly Media, Inc., 2021.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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520 |a This easy-to-use reference for Tensorflow 2 pattern designs in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself. When and why would you feed training data as NumPy or a streaming dataset? How would you set up cross validations in the training process? How do you leverage a pretrained model using transfer learning? How do you perform hyperparameter tuning? Pick up this pocket reference and reduce the time you spend searching through options for your TensorFlow use cases. Understand best practices in Tensorflow model patterns and ML workflows Use code snippets as templates in building TensorFlow models and workflows Save development time by integrating pre-built models in TensorFlow Hub Make informed design choices about data ingestion, training paradigms, model saving, and inferencing Address common scenarios such as model design style, data ingestion workflow, model training, and tuning. 
505 0 |a Cover -- Copyright -- Table of Contents -- Preface -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Chapter 1. Introduction to TensorFlow 2 -- Improvements in TensorFlow 2 -- Keras API -- Reusable Models in TensorFlow -- Making Commonly Used Operations Easy -- Open Source Data -- Working with Distributed Datasets -- Data Streaming -- Data Engineering -- Transfer Learning -- Model Styles -- Monitoring the Training Process -- Distributed Training -- Serving Your TensorFlow Model -- Improving the Training Experience 
505 8 |a Wrapping Up -- Chapter 2. Data Storage and Ingestion -- Streaming Data with Python Generators -- Streaming File Content with a Generator -- JSON Data Structures -- Setting Up a Pattern for Filenames -- Splitting a Single CSV File into Multiple CSV Files -- Creating a File Pattern Object Using tf.io -- Creating a Streaming Dataset Object -- Streaming a CSV Dataset -- Organizing Image Data -- Using TensorFlow Image Generator -- Streaming Cross-Validation Images -- Inspecting Resized Images -- Wrapping Up -- Chapter 3. Data Preprocessing -- Preparing Tabular Data for Training -- Marking Columns 
505 8 |a Encoding Column Interactions as Possible Features -- Creating a Cross-Validation Dataset -- Starting the Model Training Process -- Summary -- Preparing Image Data for Processing -- Transforming Images to a Fixed Specification -- Training the Model -- Summary -- Preparing Text Data for Processing -- Tokenizing Text -- Creating a Dictionary and Reverse Dictionary -- Wrapping Up -- Chapter 4. Reusable Model Elements -- The Basic TensorFlow Hub Workflow -- Image Classification by Transfer Learning -- Model Requirements -- Data Transformation and Input Processing 
505 8 |a Model Implementation with TensorFlow Hub -- Defining the Output -- Mapping Output to Plain-Text Format -- Evaluation: Creating a Confusion Matrix -- Summary -- Using the tf.keras.applications Module for Pretrained Models -- Model Implementation with tf.keras.applications -- Fine-Tuning Models from tf.keras.applications -- Wrapping Up -- Chapter 5. Data Pipelines for Streaming Ingestion -- Streaming Text Files with the text_dataset_from_directory Function -- Downloading Text Data and Setting Up Directories -- Creating the Data Pipeline -- Inspecting the Dataset -- Summary 
505 8 |a Streaming Images with a File List Using the flow_from_dataframe Method -- Downloading Images and Setting Up Directories -- Creating the Data Ingestion Pipeline -- Inspecting the Dataset -- Building and Training the tf.keras Model -- Streaming a NumPy Array with the from_tensor_slices Method -- Loading Example Data and Libraries -- Inspecting the NumPy Array -- Building the Input Pipeline for NumPy Data -- Wrapping Up -- Chapter 6. Model Creation Styles -- Using the Symbolic API -- Loading the CIFAR-10 Images -- Inspecting Label Distribution -- Inspecting Images -- Building a Data Pipeline 
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