Learn TensorFlow 2.0 : implement machine learning and deep learning models with Python /

Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It a...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Singh, Pramod
Otros Autores: Manure, Avinash
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berkeley, CA : Apress L.P., ©2020.
Temas:
TensorFlow.
Machine learning.
Apprentissage automatique.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Intro
  • Table of Contents
  • About the Authors
  • About the Technical Reviewer
  • Acknowledgments
  • Introduction
  • Chapter 1: Introduction to TensorFlow 2.0
  • Tensor + Flow = TensorFlow
  • Components and Basis Vectors
  • Tensor
  • Rank
  • Shape
  • Flow
  • TensorFlow 1.0 vs. TensorFlow 2.0
  • Usability-Related Changes
  • Simpler APIs
  • High-Level APIs
  • Lower-Level APIs
  • Session Execution
  • Eager Execution
  • tf.function
  • Keras
  • Redundancy
  • Improved Documentation and More Inbuilt Data Sources
  • Performance-Related Changes
  • Installation and Basic Operations in TensorFlow 2.0
  • Anaconda
  • Colab
  • Databricks
  • Conclusion
  • Chapter 2: Supervised Learning with TensorFlow
  • What Is Supervised Machine Learning?
  • Linear Regression with TensorFlow 2.0
  • Implementation of a Linear Regression Model, Using TensorFlow and Keras
  • Logistic Regression with TensorFlow 2.0
  • Boosted Trees with TensorFlow 2.0
  • Ensemble Technique
  • Bagging
  • Boosting
  • Gradient Boosting
  • Conclusion
  • Chapter 3: Neural Networks and Deep Learning with TensorFlow
  • What Are Neural Networks?
  • Neurons
  • Artificial Neural Networks (ANNs)
  • Simple Neural Network Architecture
  • Forward and Backward Propagation
  • Building Neural Networks with TensorFlow 2.0
  • About the Data Set
  • Deep Neural Networks (DNNs)
  • Building DNNs with TensorFlow 2.0
  • Estimators Using the Keras Model
  • Conclusion
  • Chapter 4: Images with TensorFlow
  • Image Processing
  • Convolutional Neural Networks
  • Convolutional Layer
  • Pooling Layer
  • Fully Connected Layer
  • ConvNets Using TensorFlow 2.0
  • Advanced Convolutional Neural Network Architectures
  • Transfer Learning
  • Transfer Learning and Machine Learning
  • Variational Autoencoders Using TensorFlow 2.0
  • Autoencoders
  • Applications of Autoencoders
  • Variational Autoencoders
  • Implementation of Variational Autoencoders Using TensorFlow 2.0
  • Conclusion
  • Chapter 5: Natural Language Processing with TensorFlow 2.0
  • NLP Overview
  • Text Preprocessing
  • Tokenization
  • Word Embeddings
  • Text Classification Using TensorFlow
  • Text Processing
  • Deep Learning Model
  • Embeddings
  • TensorFlow Projector
  • Conclusion
  • Chapter 6: TensorFlow Models in Production
  • Model Deployment
  • Isolation
  • Collaboration
  • Model Updates
  • Model Performance
  • Load Balancer
  • Python-Based Model Deployment
  • Saving and Restoring a Machine Learning Model
  • Deploying a Machine Learning Model As a REST Service
  • Templates
  • Challenges of Using Flask
  • Building a Keras TensorFlow-Based Model
  • TF ind deployment
  • Conclusion
  • Index

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