Deep Learning with TensorFlow培训

课程编码

tf101

课程时长

21 小时 通常来说是3天,包括中间休息。

要求

  • Statistics
  • Python
  • (optional) A laptop with NVIDIA GPU that supports CUDA 8.0 and cuDNN 5.1, with 64-bit Linux installed

课程概览

TensorFlowGo ogle Deep Learning开源软件库的第二代API。该系统旨在促进机器学习的研究,并使其从研究原型到生产系统的快速和轻松过渡。

听众

本课程面向希望将TensorFlow用于Deep Learning项目的工程师

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,构建图形和记录

Machine Translated

课程大纲

Machine Learning and Recursive Neural Networks (RNN) basics

  • NN and RNN
  • Backprogation
  • Long short-term memory (LSTM)

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 GPUs¹
  • Manipulating TensorFlow Model Files

TensorFlow Serving

  • Introduction
  • Basic Serving Tutorial
  • Advanced Serving Tutorial
  • Serving Inception Model Tutorial

¹ The Advanced Usage topic, “Using GPUs”, is not available as a part of a remote course. This module can be delivered during classroom-based courses, but only by prior agreement, and only if both the trainer and all participants have laptops with supported NVIDIA GPUs, with 64-bit Linux installed (not provided by NobleProg). NobleProg cannot guarantee the availability of trainers with the required hardware.

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