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Natural Language Processing with TensorFlow培训

 
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上课地点:【上海】:同济大学(沪西)/新城金郡商务楼(11号线白银路站) 【深圳分部】:电影大厦(地铁一号线大剧院站)/深圳大学成教院 【北京分部】:北京中山学院/福鑫大楼 【南京分部】:金港大厦(和燕路) 【武汉分部】:佳源大厦(高新二路) 【成都分部】:领馆区1号(中和大道) 【沈阳分部】:沈阳理工大学/六宅臻品 【郑州分部】:郑州大学/锦华大厦 【石家庄分部】:河北科技大学/瑞景大厦 【广州分部】:广粮大厦 【西安分部】:协同大厦
最近开课时间(周末班/连续班/晚班):2019年1月26日
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        1、培训过程中,如有部分内容理解不透或消化不好,可免费在以后培训班中重听;
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课程大纲
 

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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