Course Outline

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

 

 

Requirements

Working knowledge of python

  35 Hours
 

Testimonials

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