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April 10-11, 2018: Training
April 11-13, 2018: Tutorials & Conference
Beijing, CN

Getting up and running with TensorFlow

This will be presented in English.

Yufeng Guo (Google)
09:0012:30 Wednesday, April 11, 2018
Secondary topics:  深度学习(Deep Learning)

必要预备知识 (Prerequisite Knowledge)

  • 对Python有基本的了解
  • 熟悉机器学习(会有帮助但不是必需
  • A basic understanding of Python
  • Familiarity with machine learning (useful but not required)

该辅导课要求硬件和/或安装 (Hardware and/or installation requirements)

A laptop with TensorFlow installed

您将学到什么 (What you'll learn)

  • 学习如何构建、部署简单及复杂的TensorFlow模型
  • Learn how to build and deploy simple and complex models with TensorFlow

    描述 (Description)

    本教程将以英语授课。不会有同声传译,但您的问题会有翻译。

    Yufeng Guo walks you through training a machine learning system using popular open source library TensorFlow, starting from conceptual overviews and building all the way up to complex classifiers. Along the way, you’ll gain insight into deep learning and how it can apply to complex problems in science and industry.

    Outline

    Machine learning and TensorFlow

    • What is ML, and why do we care?
    • Why is TensorFlow uniquely good or useful for ML?

    A wide and deep thought experiment

    Wide and deep code model

    • Input functions
    • Create, train, eval, predict loop
    • Run the code in Jupyter

    Additional info

    • TensorBoard visualizations of the training and model graph
    • Limitations of this model

    Diving into a lower level of TensorFlow

    • Using MNIST as a toy dataset to play with model structure
    • TensorFlow primitives

    Creating a simple network by hand

    • Using the core TF libraries to create a model for solving MNIST
    • Tips and tricks for improving your model

    Upgrading the model to a CNN (time permitting)

    • Creating CNN layers by hand
    • Available hyperparameters

    Wrap-up and Q&A

    • Other models for other problem domains
    • Production environment considerations
    • Resources

    在本议程中,你将学会如何使用TensorFlow这一流行的开源机器学习框架来训练一个机器学习系统。从基本概念入手,我们会逐步完成构建复杂的分类器的全过程。你将能获得对深度学习的见解,以及学习如何把它应用到学术界和工业界的复杂问题上。


    大纲

    1. 机器学习和TensorFlow入门

    *什么是机器学习,为什么我们要关注?
    *为什么TensorFlow对机器学习而言独一无二地好用/有用?

    2. 更广更深的试验设想

    *讲述一个假想的初创App公司的故事
    *介绍这个更广泛和更深入模型的一些想法。

    3. 更广更深模型的代码

    *输入函数
    *创建、训练、评估、预测的循环
    *在Jupyter里运行这个代码

    4. 额外的信息

    *用TensorBoard可视化训练内容和模型的计算图
    *这一模型的一些局限

    5. 深入了解TensorFlow的底层基础

    *使用MNIST作为一个玩具数据集来尝试不同的模型架构
    *TensorFlow原语

    6. 自己创建一个简单的神经网络

    *用TensorFlow的核心库来创建一个解决MNIST问题的模型
    *探究如何改进模型的技巧和诀窍

    7. (时间许可的情况下)把我们的模型升级为CNN

    *自己创建一个CNN层
    *讨论可以尝试调试的超参数

    8. 总结

    *针对其他问题领域的别的模型
    *生产环境的考虑点
    *资料

    Photo of Yufeng Guo

    Yufeng Guo

    Google

    Yufeng Guo is a developer advocate for the Google Cloud Platform, where he is trying to make machine learning more understandable and usable for all. He enjoys hearing about new and interesting applications of machine learning, so be sure to share your use case with him on Twitter.

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