Natural Language Processing (NLP) with TensorFlow Training Course
TensorFlow™ is an open-source software library designed for numerical computation using data flow graphs.
SyntaxNet serves as a neural-network framework for Natural Language Processing built on TensorFlow.
Word2Vec is utilized to learn vector representations of words, known as "word embeddings." It offers a computationally efficient predictive model for acquiring these embeddings from raw text. The framework includes two primary models: the Continuous Bag-of-Words (CBOW) model and the Skip-Gram model (refer to Chapters 3.1 and 3.2 in Mikolov et al.).
When used together, SyntaxNet and Word2Vec enable users to generate learned embedding models directly from natural language input.
Audience
This course is designed for developers and engineers who plan to integrate SyntaxNet and Word2Vec models into their TensorFlow graphs.
Upon completion of this course, participants will be able to:
- grasp the structure and deployment mechanisms of TensorFlow
- perform installation, production environment setup, architecture design, and configuration
- assess code quality, conduct debugging, and monitor performance
- implement advanced production-grade tasks such as training models, embedding terms, building graphs, and logging
Course Outline
Getting Started
- Setup and Installation
TensorFlow Basics
- Creation, initialization, saving, and restoring TensorFlow variables
- Feeding, reading, and preloading TensorFlow data
- Utilizing 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
Open Training Courses require 5+ participants.
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Testimonials (3)
Very knowledgeable
Usama Adam - TWPI
Course - Natural Language Processing with TensorFlow
The way he present everything with examples and training was so useful
Ibrahim Mohammedameen - TWPI
Course - Natural Language Processing with TensorFlow
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Course - Natural Language Processing with TensorFlow
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