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Because of its ease of use and focus on user experience keras is the deep learning solution of choice for many university courses.
Keras meaning in python. It has been developed by an artificial intelligence researcher at google named francois chollet. Two of the top numerical platforms in python that provide the basis for deep learning research and development are theano and tensorflow. Last updated on september 15 2020.
Model definition in keras. This blog post is now tensorflow 2 compatible. It was developed and maintained by françois chollet an engineer from google and his code has been released under the permissive license of mit.
Keras is a python library that provides in a simple way the creation of a wide range of deep learning models using as backend other libraries such as tensorflow theano or cntk. The first concept in the keras tutorial that you should look out for is on how to build models in keras. In this tutorial you will discover how to create your first deep learning.
This is a linear stack of layers arranged one after the other. It wraps the efficient numerical computation libraries theano and tensorflow and allows you to define and train neural network models in just a few lines of code. Following are the simple code snippets that cover them.
Today s keras tutorial is designed with the practitioner in mind it is meant to be a practitioner s approach to applied deep learning. In this tutorial i ll first detail some background theory while dealing with a toy game in the open ai gym toolkit. Keras is an open source deep learning framework for python.
We ll then create a q table of this game using simple python and then create a q network using keras. In fact we ll be training a classifier for handwritten digits that boasts over 99 accuracy on the famous mnist dataset. As you briefly read in the previous section neural networks found their inspiration and biology where the term neural network can also be used for neurons.
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