TensorFlow Practices and Intelligent Systems

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TensorFlow Practices for Machine Intelligence by Sam Abraham, Daniar Hafner, Eric Erwitt, and Ariel Scarpinelli is an introductory guide to TensorFlow. The authors, all from the front line of research and development, use their valuable experience and high-quality code to explain the underlying principles of TensorFlow, and introduce the practical application of two common models — deep convolutional networks and cyclic neural networks to typical tasks of image understanding and natural language processing. In addition, it introduces a number of practical tips that can be used in model deployment and programming.

The book is divided into four parts, a total of nine chapters. The section (Chapter ~ 2) discusses the design patterns of TensorFlow, the advantages and challenges of choosing TensorFlow as a deep learning library, and provides detailed installation instructions. Part 2 (Chapters 3 and 4) delves into the fundamentals of the TensorFlow API and machine learning. The third part (Chapter 5 ~ 6) discusses how to implement depth model with TensorFlow, involving convolutional neural network (OR CNN) model and cyclic neural network (or RNN) model. Part 4 (Chapters 7 and 8) explores the new features in the TensorFlow API, including how to prepare a model for deployment, some useful programming patterns, and more. Chapter 9 provides resources for learning more about TensorFlow.Copy the code

Chapter 1 Introduction 1.1 Data Everywhere 1.2 Deep Learning 1.3TensorFlow: A Modern Machine Learning Library 1.4TensorFlow: When to Use TensorFlow 1.7 The Advantages of TensorFlow 1.8 The Challenges of Using TensorFlow TensorFlow 2.1 Select an Installation environment 2.2Jupyter Notebook and Matplotlib 2.3 Create Virtualenv 2.4 Easy Installation of TensorFlow 2.5 Source code construction and installation examples: Installing THE GPU version of TensorFlow on 64-bit Ubuntu Linux 2.5.1 Installing the Libraries 2.5.2 Installing Bazel 2.5.3 Installing CUDA Software (NVIDIA GPU) 2.5.4 Building and installing TensorFlow from source code TensorFlow and Matplotlib 2.9 TensorFlow and Machine learning Chapter 3 Fundamentals of TensorFlow 3.1 Introduction to Data Flow Diagrams 3.1.1 Fundamentals of Data flow Diagrams 3.1.2 Node dependencies 3.2 Defining data flow Diagrams in TensorFlow 3.2.1 Constructing TensorFlow diagrams 3.2.2 Tensor thinking 3.2.3 Shape of tensors 3.2.4 Operation of TensorFlow 3.2.5 Graph object of TensorFlow 3.2.6TensorFlow Session 3.2.7 Using placeholder nodes to add input 3.2.8Variable objects 3.3 Organizing data Flow diagrams by Name scoping 3.4 Exercises: Integrated application of various components 3.4.1 Constructing data Flow Diagram 3.4.2 Running data Flow Diagram 3.5 Chapter Summary chapter 4 Fundamentals of Machine Learning 4.1 Introduction to Supervised Learning 4.2 Checkpoint preservation training 4.3 Linear regression 4.4 Logarithmic probability regression 4.5 SoftMax Classification Chapter 5 Object recognition and classification 5.1 Convolutional neural Network 5.2 Convolution 5.2.1 Input and Convolution Kernel 5.2.2 Span 5.2.3 Boundary filling 5.2.4 Data Format 5.2.5 In-depth Discussion of the Convolution Kernel 5.3 Common Layers 5.3.1 Convolution layer 5.3.2 Activation Function 5.3.3 Pooling layer 5.3.4 Normalization 5.3.5 Layers 5.4 Images and TensorFlow 5.4.1 Loading images 5.4.2 Image Format 5.4.3 Image manipulation 5.4.4 Color 5.5 IMPLEMENTATION of CNN 5.5.1Stanford Dogs data set 5.5.2 Converting images into TFRecord files 5.5.3 Loading images 5.5.4 Model 5.5.5 Training 5.5.6 Debugging filters with TensorBoard 5.6 Chapter Summary chapter 6 Recurrent Neural Networks and Natural Language Processing 6.1 Introduction to Recurrent Neural Networks 6.1.1 The world of Timing 6.1.2 Approximating Arbitrary programs 6.1.3 Backpropagation over time 6.1.4 Encoding and decoding of sequences 6.1.5 Implementation of recurrent neural networks 6.1.6 gradient extinction and gradient 6.1.7 LONG and Short memory networks 6.1.8 Variations of RNN structures 6.2 Word vector embedding 6.2.1 Preparing wikipedia corpus 6.2.2 Model structures 6.2.3 Noise contrast classifiers 6.2.4 Training model 6.3 Sequence classification 6.3.1Imdb film review data set 6.3.2 Embedding with word vector 6.3.3 Sequence labeling model 6.3.4 Softmax layer from post-correlated activity values 6.3.5 gradient cropping 6.3.6 Training model 6.4 Sequence labeling 6.4.1OCR dataset 6.4.2 Soft-Max layer shared between time steps 6.4.3 Training model 6.4.4 Bidirectional RNN 6.5 Predictive coding 6.5.1 Character-level language modeling 6.5.2ArXiv abstract API 6.5.3 Data preprocessing 6.5.4 Predictive coding Model 6.5.5 Training Model 6.5.6 Generating Similar Sequences 6.6 Chapter Summary Part 4 Other Tips, Techniques and Features Chapter 7 Deployment of models in product Environment 7.1 Setting up TensorFlow Service Development Environment 7.1.1Docker Image 7.1.2Bazel Workspace 7.2 Exporting the trained model 7.3 Defining the Server interface 7.4 Implementing Inference server 7.5 Client Applications 7.6 Product Preparation 7.7 Chapter Summary chapter 8 Auxiliary Functions, Code Structures, and Classes 8.1 Ensuring the existence of directory structures 8.2 Download functions 8.3 Disk Cache decorator 8.4 Property dictionary 8.5 Lazy Property Decorator 8.6 Overrides Data Flow graph decorator Chapter 9 Conclusion: Other ResourcesCopy the code

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