樹莓派、樹莓派之學習、樹莓派之教育

教育和學習︰ Up《grade》【六】

活在快速變遷的時代，或許更該慎選『API 骨架』耶？

Why use Keras?

There are countless deep learning frameworks available today. Why use Keras rather than any other? Here are some of the areas in which Keras compares favorably to existing alternatives.

Keras prioritizes developer experience

  • Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error.
  • This makes Keras easy to learn and easy to use. As a Keras user, you are more productive, allowing you to try more ideas than your competition, faster — which in turn helps you win machine learning competitions.
  • This ease of use does not come at the cost of reduced flexibility: because Keras integrates with lower-level deep learning languages (in particular TensorFlow), it enables you to implement anything you could have built in the base language. In particular, as tf.keras, the Keras API integrates seamlessly with your TensorFlow workflows.

Keras has broad adoption in the industry and the research community

With over 200,000 individual users as of November 2017, Keras has stronger adoption in both the industry and the research community than any other deep learning framework except TensorFlow itself (and Keras is commonly used in conjunction with TensorFlow).

You are already constantly interacting with features built with Keras — it is in use at Netflix, Uber, Yelp, Instacart, Zocdoc, Square, and many others. It is especially popular among startups that place deep learning at the core of their products.

Keras is also a favorite among deep learning researchers, coming in #2 in terms of mentions in scientific papers uploaded to the preprint server arXiv.org:

Keras has also been adopted by researchers at large scientific organizations, in particular CERN and NASA.

Keras makes it easy to turn models into products

Your Keras models can be easily deployed across a greater range of platforms than any other deep learning framework:

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若說只靠手冊與指南

Keras: The Python Deep Learning library

You have just found Keras.

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, orTheano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.

Use Keras if you need a deep learning library that:

  • Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
  • Supports both convolutional networks and recurrent networks, as well as combinations of the two.
  • Runs seamlessly on CPU and GPU.

Read the documentation at Keras.io.

Keras is compatible with: Python 2.7-3.6.

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怕不容易駕馭『 AI 的應用』乎！

故盼藉著讀本深度學習之 Keras 中介書

《 Deep Learning with Python (Manning Publications) 》

透過『練習範例』

Companion Jupyter notebooks for the book “Deep Learning with Python”

This repository contains Jupyter notebooks implementing the code samples found in the book Deep Learning with Python (Manning Publications). Note that the original text of the book features far more content than you will find in these notebooks, in particular further explanations and figures. Here we have only included the code samples themselves and immediately related surrounding comments.

These notebooks use Python 3.6 and Keras 2.0.8. They were generated on a p2.xlarge EC2 instance.

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立能嫻熟概念之『表述法』也☆