Natural Language Processing with TensorFlow培训

课程编码

tsflw2v

课程时长

35 小时 通常来说是5天,包括中间休息。

要求

Working knowledge of python

课程概览

TensorFlow ™是一个使用数据流图进行数值计算的开源软件库。

SyntaxNet是TensorFlow的神经网络自然语言处理框架。

Word 2Vec用于学习单词的矢量表示,称为“单词嵌入”。 Word 2vec是一种特别计算有效的预测模型,用于学习原始文本中的单词嵌入。它有两种形式,连续Bag-of- Word模型(CBOW)和Skip-Gram模型(Mikolov等人的第3.1和3.2章)。

使用串联,SyntaxNet和Word 2Vec允许用户从自然语言输入生成学习嵌入模型。

听众

本课程面向打算在TensorFlow图中使用SyntaxNet和Word 2Vec模型的开发人员和工程师。

完成本课程后,代表们将:

  • 了解TensorFlow的结构和部署机制
  • 能够执行安装/生产环境/架构任务和配置
  • 能够评估代码质量,执行调试,监控
  • 能够实现高级生产,如培训模型,嵌入术语,构建图形和记录

Machine Translated

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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