Google Deep Learning Framework for Tensorflow

Links:Pan.baidu.com/s/1wJ0h86DA…Extraction code: YBR9

√ Former Google experts and now upstart Tensorflow entrepreneurs are invited to join us to share our core technologies and cutting-edge cases.

Tick book former version as the industry first to accompany Tensorflow fire all over world, aimed at the production | business scenarios, | linking principle completely.

Visit | | sometimes based on the principle of square root deep into the real project, AI, ML team rushed to praise recommended, together with Tensorflow become a DE facto standard.

√ Code has been upgraded to version 1.4+, focusing on the new version and adding special topics on TF high-level packaging and deep learning natural language applications.

TensorFlow is a mainstream deep learning framework that Google opened source in 2015 and is now widely used. TensorFlow: Google Deep Learning Framework in Action (2nd Edition) is an introduction to TensorFlow. It aims to help readers get started with TensorFlow and deep learning in a fast and effective way. Instead of complicated mathematical model derivation, the book introduces how to use deep learning to solve practical problems through specific TensorFlow examples. The book contains an introduction to deep learning and a great deal of practical experience, making it the preferred reference book for entering this cutting-edge and hot field of artificial intelligence.

Version 2 updates all the sample code in the book from TensorFlow 0.9.0 to TensorFlow 1.4.0. In addition to the API upgrade, release 2 adds more features only supported by TensorFlow 1.4.0. In addition, two new chapters on TensorFlow high-level encapsulation and deep learning for natural language applications have been added to release 2.

TensorFlow: Practical Google Deep Learning Framework (Version 2) is designed for data scientists and engineers who want to use Deep learning or TensorFlow, engineers who want to learn about big data platforms, computer professionals and students interested in artificial intelligence and deep learning

Chapter 1 Introduction to Deep Learning 1.1 Artificial Intelligence, Machine Learning and Deep Learning 1.2 Development history of Deep Learning 1.3 Application of Deep learning 1.3.1 Computer vision 1.3.2 Speech Recognition 1.3.3 Natural Language Processing 1.3.4 Man-machine Game 1.4 Chapter 2 TensorFlow Environment Setup 2.1 TensorFlow main dependencies 2.1.1 Protocol Buffer 2.1.2 Bazel 2.2 TensorFlow Installation 2.2.1 Chapter 3 Introduction to TensorFlow chapter 3 Introduction to TensorFlow 3.1 TensorFlow Calculation Model -- Calculation diagram 3.1.1 Concept of calculation diagram 3.1.2 Use of computational graphs 3.2 TensorFlow data model -- Tensors 3.2.1 Concept of tensors 3.2.2 Use of Tensors 3.3 TensorFlow operation model -- Sessions 3.4 TensorFlow implementation of neural networks 3.4.1 Introduction to TensorFlow Playground and Neural Network 3.4.2 Introduction to Forward Propagation Algorithm 3.4.3 Neural network parameters and TensorFlow variables 3.4.4 Training neural network model through TensorFlow 3.4.5 Summary of complete neural network sample program Chapter 4 Deep neural network 4.1 Deep learning and Deep Neural Networks 4.1.1 Limitations of linear models 4.1.2 Activation function to achieve de-linearization 4.1.3 Multi-layer networks to solve xOR operations 4.2 Loss function definition 4.2.1 Classical loss function 4.2.2 Custom loss function 4.3 Neural network optimization Algorithm 4.4 Neural network further optimization 4.4.1 Setting of learning rate 4.4.2 Overfitting problem 4.4.3 Summary of moving average Model Chapter 5 MNIST number recognition problem 5.1 MNIST data processing 5.2 Neural network model training and comparison of results of different models 5.2.1 TensorFlow training neural network 5.2.2 Judging model effects using validated data sets 5.2.3 Comparison of different models 5.3 Variable Management 5.4 TensorFlow Model persistence 5.4.1 Implementation of Persistence code 5.4.2 Persistence principle and Data Format 5.5 Chapter 6 Image recognition and Convolutional neural Networks 6.1 Introduction to Image recognition problems and classical data sets 6.2 Introduction to Convolutional neural Networks 6.3 Common structures of convolutional neural networks 6.3.1 Convolutional layer 6.3.2 Pooling layer 6.4 Classical convolutional network model 6.4.1 Lenet-5 Model 6.4.2 Inception- V3 model 6.5 Convolutional neural network Transfer learning 6.5.1 Introduction to Transfer Learning 6.5.2 TensorFlow Implementation of Transfer Learning Summary Chapter 7 Image data processing 7.1 TFRecord input data format 7.1.1 TFRecord Format introduction 7.1.2 TFRecord sample program 7.2 Image data processing 7.2.1 TensorFlow Image processing function 7.2.2 Image preprocessing Complete sample 7.3 Multi-threaded input data processing framework 7.3.1 Queue and Multi-threading 7.3.2 Input file queue 7.3.3 Batching data 7.3.4 Input data processing framework 7.4 Dataset 7.4.1 Basic usage method of Dataset 7.4.2 Summary of high-level Operation of Dataset Chapter 8 Recurrent neural network 8.1 An introduction to Recurrent neural networks 8.2 Structures of LSTM 8.3 Variants of recurrent neural Networks 8.3.1 Bidirectional and deep Recurrent neural Networks 8.3.2 Dropout 8.4 Summary of sample applications of Recurrent neural Networks Chapter 9 Natural language Processing 9.1 Background knowledge of language Models 9.1.1 Introduction to Language models 9.1.2 Evaluation methods of language models 9.2 Neuro-linguistic Models 9.2.1 Preprocessing of PTB data sets 9.2.2 Batching method of PTB data 9.2.3 Neuro-linguistic models based on recurrent neural networks 9.3 Introduction to NEURAL network Machine Translation 9.3.1 Machine Translation Background and Seq2Seq Model 9.3.2 Preprocessing of machine translation text data 9.3.3 Code implementation of Seq2Seq model 9.3.4 Attention mechanism Summary Chapter 10 TensorFlow high-level encapsulation 10.1 TensorFlow Overview 10.2 Introduction to Keras 10.2.1 Basic Usage of Keras 10.2.2 Advanced Usage of Keras 10.3 Estimator 10.3.1 Basic Usage of Estimator 10.3.2 Estimator custom model 10.3.3 Using Dataset as Estimator Input Summary Chapter 11 TensorBoard Visualization 11.1 Introduction to TensorBoard 11.2 TensorFlow Diagram Visualization 11.2.1 Chapter 12 TensorFlow Computing Acceleration 12.1 TensorFlow uses GPU 12.2 Deep learning to train parallel modes 12.3 Multi-GPU Parallelism 12.4 Distributed TensorFlow 12.4.1 Principles of Distributed TensorFlow 12.4.2 Distributed TensorFlow Model Training SummaryCopy the code

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