Natural Language Processing (NLP) with TensorFlow Training Course

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