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

著者

    • Singh, Pramod
    • Manure, Avinash

書誌事項

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

Pramod Singh, Avinash Manure

Apress L.P., 〓2020

機械可読データファイル（リモートファイル）

大学図書館所蔵 件 / 全1件

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Includes index

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収録内容

  • 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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