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Data Analysis with Python

You're reading from Data Analysis with Python A Modern Approach

Product type Paperback

Published in Dec 2018

Publisher Packt

ISBN-13 9781789950069

Length 490 pages

Edition 1st Edition

Languages

Python

Tools

Jupyter

Concepts

Data Analysis

Author (1):

David Taieb

View More author details

Table of Contents (14) Chapters

Preface

1. Programming and Data Science – A New Toolset FREE CHAPTER

2. Python and Jupyter Notebooks to Power your Data Analysis

3. Accelerate your Data Analysis with Python Libraries

4. Publish your Data Analysis to the Web - the PixieApp Tool

5. Python and PixieDust Best Practices and Advanced Concepts

6. Analytics Study: AI and Image Recognition with TensorFlow

7. Analytics Study: NLP and Big Data with Twitter Sentiment Analysis

8. Analytics Study: Prediction - Financial Time Series Analysis and Forecasting

9. Analytics Study: Graph Algorithms - US Domestic Flight Data Analysis

10. The Future of Data Analysis and Where to Develop your Skills

A. PixieApp Quick-Reference

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Index

Getting started with TensorFlow

There are multiple open source deep learning frameworks besides TensorFlow (https://www.tensorflow.org) that I could have chosen for this sample application.

Some of the most popular frameworks are as follows:

PyTorch (http://pytorch.org)
Caffee2 (https://caffe2.ai)
MXNet (https://mxnet.apache.org)
Keras (https://keras.io): A high-level neural network abstraction API capable of running other deep learning frameworks such as TensorFlow, CNTK (https://github.com/Microsoft/cntk), and Theano (https://github.com/Theano/Theano)

TensorFlow APIs are available in multiple languages: Python, C++, Java, Go, and, more recently, JavaScript. We can distinguish two categories of APIs: high level and low level, represented by this diagram:

Getting started with TensorFlow

TensorFlow high-level API architecture

To get started with the TensorFlow API, let's build a simple neural network that will learn the XOR transformation.

As a reminder, the XOR operator has only four training examples:

X

Y

Result...

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Authors (1)

David Taieb

David Taieb

David Taieb is the Distinguished Engineer for the Watson and Cloud Platform Developer Advocacy team at IBM, leading a team of avid technologists on a mission to educate developers on the art of the possible with data science, AI and cloud technologies. He's passionate about building open source tools, such as the PixieDust Python Library for Jupyter Notebooks, which help improve developer productivity and democratize data science. David enjoys sharing his experience by speaking at conferences and meetups, where he likes to meet as many people as possible.

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