Learning TensorFlow. A Guide to Building Deep Learning Systems

(ebook) (audiobook) (audiobook)

Autorzy:: Tom Hope, Yehezkel S. Resheff, Itay Lieder

Promocja Przejdź

Learning TensorFlow. A Guide to Building Deep Learning Systems Tom Hope, Yehezkel S. Resheff, Itay Lieder - okladka książki

Learning TensorFlow. A Guide to Building Deep Learning Systems Tom Hope, Yehezkel S. Resheff, Itay Lieder - audiobook MP3

Learning TensorFlow. A Guide to Building Deep Learning Systems Tom Hope, Yehezkel S. Resheff, Itay Lieder - audiobook CD

Wydawnictwo:: O'Reilly Media (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 242
Dostępne formaty:: ePub

Mobi

Ebook (152,15 zł najniższa cena z 30 dni)

~~189,00 zł~~ (-15%)
160,65 zł

Dodaj do koszyka lub Kup na prezent Kup 1-kliknięciem

(152,15 zł najniższa cena z 30 dni)

Przenieś na półkę

Do przechowalni

Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics.

Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. You’ll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you’ll know how to build and deploy production-ready deep learning systems in TensorFlow.

Get up and running with TensorFlow, rapidly and painlessly
Learn how to use TensorFlow to build deep learning models from the ground up
Train popular deep learning models for computer vision and NLP
Use extensive abstraction libraries to make development easier and faster
Learn how to scale TensorFlow, and use clusters to distribute model training
Deploy TensorFlow in a production setting

Wybrane bestsellery

Ebooka "Learning TensorFlow. A Guide to Building Deep Learning Systems" przeczytasz na:

czytnikach Inkbook, Kindle, Pocketbook, Onyx Booxs i innych
systemach Windows, MacOS i innych

systemach Windows, Android, iOS, HarmonyOS
na dowolnych urządzeniach i aplikacjach obsługujących formaty: PDF, EPub, Mobi

Masz pytania? Zajrzyj do zakładki Pomoc »

Audiobooka "Learning TensorFlow. A Guide to Building Deep Learning Systems" posłuchasz:

w aplikacji Ebookpoint na Android, iOS, HarmonyOs
na systemach Windows, MacOS i innych

na dowolonych urządzeniach
i aplikacjach obsługujących format MP3
(pliki spakowane w ZIP)

Masz pytania? Zajrzyj do zakładki Pomoc »

Kurs Video "Learning TensorFlow. A Guide to Building Deep Learning Systems" zobaczysz:

Oceny i opinie klientów: Learning TensorFlow. A Guide to Building Deep Learning Systems Tom Hope, Yehezkel S. Resheff, Itay Lieder (0) Weryfikacja opinii następuję na podstawie historii zamówień na koncie Użytkownika umieszczającego opinię. Użytkownik mógł otrzymać punkty za opublikowanie opinii uprawniające do uzyskania rabatu w ramach Programu Punktowego.

Szczegóły książki

ISBN Ebooka:: 978-14-919-7846-7, 9781491978467
Data wydania ebooka :: 2017-08-09 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
Język publikacji:: angielski
Rozmiar pliku ePub:: 10.7MB
Rozmiar pliku Mobi:: 22.3MB

Zgłoś erratę
Kategorie:
Big Data » Analiza danych
Uczenie maszynowe

Spis treści książki

Preface
1. Introduction
- Going Deep
  - Using TensorFlow for AI Systems
    - Pre-trained models: state-of-the-art computer vision for all
    - Generating rich natural language descriptions for images
    - Text summarization
- TensorFlow: Whats in a Name?
- A High-Level Overview
- Summary
2. Go with the Flow: Up and Running with TensorFlow
- Installing TensorFlow
- Hello World
- MNIST
- Softmax Regression
- Summary
3. Understanding TensorFlow Basics
- Computation Graphs
  - What Is a Computation Graph?
  - The Benefits of Graph Computations
- Graphs, Sessions, and Fetches
  - Creating a Graph
  - Creating a Session and Running It
  - Constructing and Managing Our Graph
  - Fetches
- Flowing Tensors
  - Nodes Are Operations, Edges Are Tensor Objects
    - Setting attributes with source operations
  - Data Types
    - Casting
  - Tensor Arrays and Shapes
    - Matrix multiplication
  - Names
    - Name scopes
- Variables, Placeholders, and Simple Optimization
  - Variables
  - Placeholders
  - Optimization
    - Training to predict
    - Defining a loss function
    - MSE and cross entropy
    - The gradient descent optimizer
    - Sampling methods
    - Gradient descent in TensorFlow
    - Wrapping it up with examples
      - Example 1: linear regression
      - Example 2: logistic regression
- Summary
4. Convolutional Neural Networks
- Introduction to CNNs
- MNIST: Take II
  - Convolution
  - Pooling
  - Dropout
  - The Model
- CIFAR10
  - Loading the CIFAR10 Dataset
  - Simple CIFAR10 Models
- Summary
5. Text I: Working with Text and Sequences, and TensorBoard Visualization
- The Importance of Sequence Data
- Introduction to Recurrent Neural Networks
  - Vanilla RNN Implementation
    - MNIST images as sequences
    - The RNN step
    - Applying the RNN step with tf.scan()
    - Sequential outputs
    - RNN classification
    - Visualizing the model with TensorBoard
  - TensorFlow Built-in RNN Functions
    - tf.contrib.rnn.BasicRNNCell and tf.nn.dynamic_rnn()
- RNN for Text Sequences
  - Text Sequences
  - Supervised Word Embeddings
  - LSTM and Using Sequence Length
  - Training Embeddings and the LSTM Classifier
    - Stacking multiple LSTMs
- Summary
6. Text II: Word Vectors, Advanced RNN, and Embedding Visualization
- Introduction to Word Embeddings
- Word2vec
  - Skip-Grams
  - Embeddings in TensorFlow
  - The Noise-Contrastive Estimation (NCE) Loss Function
  - Learning Rate Decay
  - Training and Visualizing with TensorBoard
  - Checking Out Our Embeddings
- Pretrained Embeddings, Advanced RNN
  - Pretrained Word Embeddings
  - Bidirectional RNN and GRU Cells
- Summary
7. TensorFlow Abstractions and Simplifications
- Chapter Overview
  - High-Level Survey
- contrib.learn
  - Linear Regression
  - DNN Classifier
  - FeatureColumn
  - Homemade CNN with contrib.learn
- TFLearn
  - Installation
  - CNN
  - RNN
  - Keras
    - Installation
    - Sequential model
    - Functional model
    - Autoencoders
  - Pretrained models with TF-Slim
    - TF-Slim
    - Creating CNN models with TF-Slim
    - Downloading and using a pretrained model
- Summary
8. Queues, Threads, and Reading Data
- The Input Pipeline
- TFRecords
  - Writing with TFRecordWriter
- Queues
  - Enqueuing and Dequeuing
  - Multithreading
  - Coordinator and QueueRunner
    - tf.train.Coordinator
    - tf.train.QueueRunner and tf.RandomShuffleQueue
- A Full Multithreaded Input Pipeline
  - tf.train.string_input_producer() and tf.TFRecordReader()
  - tf.train.shuffle_batch()
  - tf.train.start_queue_runners() and Wrapping Up
- Summary
9. Distributed TensorFlow
- Distributed Computing
  - Where Does the Parallelization Take Place?
  - What Is the Goal of Parallelization?
- TensorFlow Elements
  - tf.app.flags
  - Clusters and Servers
  - Replicating a Computational Graph Across Devices
  - Managed Sessions
  - Device Placement
- Distributed Example
- Summary
10. Exporting and Serving Models with TensorFlow
- Saving and Exporting Our Model
  - Assigning Loaded Weights
  - The Saver Class
- Introduction to TensorFlow Serving
  - Overview
  - Installation
    - Installing Serving
  - Building and Exporting
    - Exporting our model
- Summary
A. Tips on Model Construction and Using TensorFlow Serving
- Model Structuring and Customization
  - Model Structuring
    - Modular design
    - Variable sharing
    - Class encapsulation
  - Customization
    - Homemade loss functions
    - Regularization
    - Writing your very own op
- Required and Recommended Components for TensorFlow Serving
  - What Is a Docker Container and Why Do We Use It?
  - Some Basic Docker Commands
Index