Getting Started
Setup and Installation
TensorFlow Basics
Creation, Initializing, Saving, and Restoring TensorFlow variables
Feeding, Reading and Preloading TensorFlow Data
How to use TensorFlow infrastructure to train models at scale
Visualizing and Evaluating models with TensorBoard
TensorFlow Mechanics 101
Prepare the Data
Download
Inputs and Placeholders
Build the Graph
Inference
Loss
Training
Train the Model
The Graph
The Session
Train Loop
Evaluate the Model
Build the Eval Graph
Eval Output
Advanced Usage
Threading and Queues
Distributed TensorFlow
Writing Documentation and Sharing your Model
Customizing Data Readers
Using GPUs
Manipulating TensorFlow Model Files
TensorFlow Serving
Introduction
Basic Serving Tutorial
Advanced Serving Tutorial
Serving Inception Model Tutorial
Getting Started with SyntaxNet
Parsing from Standard Input
Annotating a Corpus
Configuring the Python Scripts
Building an NLP Pipeline with SyntaxNet
Obtaining Data
Part-of-Speech Tagging
Training the SyntaxNet POS Tagger
Preprocessing with the Tagger
Dependency Parsing: Transition-Based Parsing
Training a Parser Step 1: Local Pretraining
Training a Parser Step 2: Global Training
Vector Representations of Words
Motivation: Why Learn word embeddings?
Scaling up with Noise-Contrastive Training
The Skip-gram Model
Building the Graph
Training the Model
Visualizing the Learned Embeddings
Evaluating Embeddings: Analogical Reasoning
Optimizing the Implementation |
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