Code Monkey home page Code Monkey logo

keras_lstm's Introduction

keras_lstm

lstm with python (Jason Brownlee, Deep mind)

Welcome to the keras_lstm wiki!

Learn LSTM with Python from Jason Brownlee of DeepMind

I Introductions iv

Welcome v

  • Who Is This Book For? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
  • About Your Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
  • How to Read This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
  • About the Book Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
  • About Lessons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
  • About LSTM Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
  • About Prediction Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
  • About Python Code Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
  • About Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
  • About Getting Help . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
  • Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii

II Foundations 1

  • 1 What are LSTMs 2
  • 1.1 Sequence Prediction Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
  • 1.2 Limitations of Multilayer Perceptrons . . . . . . . . . . . . . . . . . . . . . . . . 7
  • 1.3 Promise of Recurrent Neural Networks . . . . . . . . . . . . . . . . . . . . . . . 9
  • 1.4 The Long Short-Term Memory Network . . . . . . . . . . . . . . . . . . . . . . 10
  • 1.5 Applications of LSTMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
  • 1.6 Limitations of LSTMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
  • 1.7 Further Reading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
  • 1.8 Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
  • 1.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

keras_lstm's People

Contributors

yanghaocsg avatar

Stargazers

 avatar  avatar  avatar

Watchers

 avatar  avatar  avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.