Deep Learning With Python: Develop Deep Learning Models on Theano and TensorFlow using Keras
by: Jason Brownlee
Publication Date 出版日期: 2016
Print Length 页数: 255
Language 语言: English
Print Length 页数: PDF
Size: 10 Mb
Book Description
By finelybook
Deep learning is the most interesting and powerful machine learning technique right now.
Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras,the best-of-breed applied deep learning library.
In this mega Ebook is written in the friendly Machine Learning Mastery style that you’re used to,learn exactly how to get started and apply deep learning to your own machine learning projects
Use Python,Build On Top of Theano and TensorFlow
Develop and evaluate deep learning models in Python.
The platform for getting started in applied deep learning is Python.
Python is a fully featured general purpose programming language,unlike R and Matlab. It is also quick and easy to write and understand,unlike C++ and Java.
The SciPy stack in Python is a mature and quickly expanding platform for scientific and numerical computing. The platform hosts libraries such as scikit-learn the general purpose machine learning library that can be used with your deep learning models.
It is because of these benefits of the Python ecosystem that two top numerical libraries for deep learning were developed for Python,Theano and the newer TensorFlow library released by Google (and adopted recently by the Google DeepMind research group).
Theano and TensorFlow are two top numerical libraries for developing deep learning models,but are too technical and complex for the average practitioner. They are intended more for research and development teams and academics interested in developing wholly new deep learning algorithms.
The saving grace is the Keras library for deep learning,that is written in pure Python,wraps and provides a consistent agnostic interface to Theano and TensorFlow and is aimed at machine learning practitioners that are interested in creating and evaluating deep learning models.
It is a little over one year old and is clearly the best-of-breed library for getting started with deep learning because of both the speed at which you can develop models and the numerical power it is built upon.
