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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 (1)
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