Natural Language Processing (NLP) with TensorFlow Training Course and Workshop in Bangalore, Mysore, Chennai, Hyderabad, Pune, Mumbai, Delhi, Noida, Gurgaon, Kolkata

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.

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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Natural Language Processing with TensorFlow

COURSE AGENDA