TensorFlow 1.9 Docs APK

TensorFlow 1.9 Documentation

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TensorFlow 1.9 Documentation

TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

Attribution: 
the TensorFlow logo and any related marks are trademarks of Google Inc.

Table of Content

Install TensorFlow
Install TensorFlow on Ubuntu
Install TensorFlow on macOS
Install TensorFlow on Windows
Install TensorFlow on Raspbian
Install TensorFlow from Sources
Transitioning to TensorFlow 1.0
Install TensorFlow for Java
Install TensorFlow for Go
Install TensorFlow for C
TensorFlow Guide
Keras
Eager Execution
Importing Data
Introduction to Estimators
Premade Estimators
Checkpoints
Feature Columns
Datasets for Estimators
Creating Custom Estimators
Using GPUs
Using TPUs
Introduction
Tensors
Variables
Graphs and Sessions
Save and Restore
Embeddings
TensorFlow Debugger
Visualizing Learning
Graphs
Histograms
TensorFlow Version Compatibility
Frequently Asked Questions
Overview
Basic classification
Text classification
Regression
Overfitting and underfitting
Save and restore models
Overview
Custom training: walkthrough
Linear model with Estimators
Text classifier with TF-Hub
Build a CNN using Estimators
Image recognition
Image retraining
Advanced CNN
Recurrent neural network
Drawing classification
Simple audio recognition
Vector representations of words
Kernel methods
Large-scale linear models
Mandelbrot set
Partial differential equations
Next steps
Deploy
Distributed TensorFlow
How to run TensorFlow on Hadoop
How to run TensorFlow on S3
Deploy to JavaScript
Introduction
Architecture Overview
Installation
Serving a TensorFlow Model
RESTful API
Building Standard TensorFlow ModelServer
Serving Inception Model with TensorFlow Serving and Kubernetes
Creating a new kind of servable
Creating a module that discovers new servable paths
SignatureDefs in SavedModel for TensorFlow Serving
Using TensorFlow Serving via Docker
Performance
Performance Guide
Input Pipeline Performance Guide
Benchmarks
Fixed Point Quantization
XLA Overview
Broadcasting semantics
Developing a new backend for XLA
Using JIT Compilation
Operation Semantics
Shapes and Layout
Using AOT compilation
Extend
TensorFlow Architecture
Adding a New Op
Adding a Custom Filesystem Plugin
Reading custom file and record formats
TensorFlow in other languages
A Tool Developer's Guide to TensorFlow Model Files
Overview
Introduction to TensorFlow Lite
Developer Guide
Android Demo App
iOS Demo App
Performance
Introduction to TensorFlow Mobile
Building TensorFlow on Android
Building TensorFlow on iOS
Integrating TensorFlow libraries
Preparing models for mobile deployment
Optimizing for mobile
Community
Roadmap
Contributing to TensorFlow
Mailing Lists
User Groups
Writing TensorFlow Documentation
TensorFlow Style Guide
Defining and Running Benchmarks
About TensorFlow
TensorFlow In Use
TensorFlow White Papers
Attribution
Overview
Installation
Using a Module
Creating a New Module
Fine-Tuning
Hosting a Module
Image Retraining
Text Classification
Overview
Common Signatures for Images
Common Signatures for Text
Overview
add_signature
create_module_spec
get_expected_image_size
get_num_image_channels
image_embedding_column
LatestModuleExporter
load_module_spec
Module
ModuleSpec
register_module_for_export
text_embedding_column
Overview
Image Modules
Text Modules
Other Modules 

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