EncartaLabs

Natural Language Processing with TensorFlow

( Duration: 5 Days )

TensorFlow is an open source software library for numerical computation using data flow graphs. SyntaxNet is a neural-network Natural Language Processing framework for TensorFlow.

Word2Vec is used for learning vector representations of words, called "word embeddings". Word2vec is a particularly computationally-efficient predictive model for learning word embeddings from raw text. It comes in two flavors, the Continuous Bag-of-Words model (CBOW) and the Skip-Gram model. Used in tandem, SyntaxNet and Word2Vec allows users to generate Learned Embedding models from Natural Language input.

By attending Natural Language Processing (NLP) with TensorFlow workshop, delegates will learn to:

  • Understand TensorFlow’s structure and deployment mechanisms
  • Carry out installation / production environment / architecture tasks and configuration
  • Assess code quality, perform debugging, monitoring
  • Implement advanced production like training models, embedding terms, building graphs and logging

  • Working knowledge of Tensorflow
  • This Natural Language Processing (NLP) with TensorFlow training course is targeted at Developers and Engineers who intend to work with SyntaxNet and Word2Vec models in their TensorFlow graphs.

COURSE AGENDA

1

Getting Started

  • Setup and Installation
2

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
3

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
4

Advanced Usage

  • Threading and Queues
  • Distributed TensorFlow
  • Writing Documentation and Sharing your Model
  • Customizing Data Readers
  • Using GPUs
  • Manipulating TensorFlow Model Files
5

TensorFlow Serving

  • Introduction
  • Basic Serving Tutorial
  • Advanced Serving Tutorial
  • Serving Inception Model Tutorial
6

Getting Started with SyntaxNet

  • Parsing from Standard Input
  • Annotating a Corpus
  • Configuring the Python Scripts
7

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
8

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

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 4,000 Modules on a variety of subjects
  • All courses are delivered by Industry Veterans
  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

View our other course offerings by visiting http://encartalabs.com/course-catalogue-all.php

Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.

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