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Course Outline

Getting Started

  • Setup and Installation

TensorFlow Basics

  • Creation, initialization, saving, and restoring of TensorFlow variables
  • Feeding, reading, and preloading of TensorFlow data
  • Leveraging TensorFlow infrastructure for large-scale model training
  • Visualizing and evaluating models using TensorBoard

TensorFlow Mechanics 101

  • Prepare the Data
    • Download
    • Inputs and Placeholders
  • Build the Graph
    • Inference
    • Loss
    • Training
  • Train the Model
    • The Graph
    • The Session
    • Training Loop
  • Evaluate the Model
    • Constructing the Evaluation Graph
    • Evaluation Output

Advanced Usage

  • Threading and Queues
  • Distributed TensorFlow
  • Writing Documentation and Sharing Your Model
  • Customizing Data Readers
  • Utilizing 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 Python Scripts

Building an NLP Pipeline with SyntaxNet

  • Acquiring 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
  • Constructing the Graph
  • Training the Model
  • Visualizing the Learned Embeddings
  • Evaluating Embeddings: Analogical Reasoning
  • Optimizing the Implementation

Requirements

Practical working knowledge of Python

 35 Hours

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