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What is TensorFlow?

https://www.tensorflow.uk

What is TensorFlow?

TensorFlow is an open source software library for machine learning developed by Google – Google Brain team. Name TensorFlow derives from the operations which neural networks perform on multidimensional data arrays, often referred to as “tensors”. It is using data flow graphs and is capable of building and training variety of different machine learning algorithms and deep neural networks, but it is general enough to be applicable in a wide variety of other domains as well. Flexible architecture allows deploying computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

TensorFlow is Google Brain’s second generation machine learning system, released as open source software in 2015. TensorFlow is available on 64-bit Linux, MacOS, and mobile computing platforms including Android and iOS. TensorFlow provides a Python API, as well as C++, Haskell, Java and Go APIs. In 2016 Google announced Tensor Processing Unit (TPU), a custom built for machine learning programmable AI accelerator designed to provide high throughput of low-precision arithmetic.

TensorFlow applications.

Among a variety of applications for which TensorFlow is used and listed in the TesnsorFlow website are:

RankBrain – A large-scale deployment of deep neural nets for search ranking on Google.

Inception Image Classification Model – highly accurate computer vision models, starting with the model that won the 2014 Imagenet image classification challenge.

SmartReply – Deep LSTM model to automatically generate email responses

Massively Multitask Networks for Drug Discovery – A deep neural network model for identifying promising drug candidates.

On-Device Computer Vision for OCR – computer vision model to do optical character recognition to enable real-time translation.

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If you want to look for more information, check some free online courses available at   coursera.orgedx.org or udemy.com.

Recommended reading list:

 

Data Science from Scratch: First Principles with Python

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If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.

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Practical Statistics for Data Scientists: 50 Essential Concepts

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Why exploratory data analysis is a key preliminary step in data science
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The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists

The Data Science Handbook contains interviews with 25 of the world s best data scientists. We sat down with them, had in-depth conversations about their careers, personal stories, perspectives on data science and life advice. In The Data Science Handbook, you will find war stories from DJ Patil, US Chief Data Officer and one of the founders of the field. You ll learn industry veterans such as Kevin Novak and Riley Newman, who head the data science teams at Uber and Airbnb respectively. You ll also read about rising data scientists such as Clare Corthell, who crafted her own open source data science masters program. This book is perfect for aspiring or current data scientists to learn from the best. It s a reference book packed full of strategies, suggestions and recipes to launch and grow your own data science career.
Introduction to Machine Learning with Python: A Guide for Data Scientists

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, you’ll learn:

Fundamental concepts and applications of machine learning
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Advanced methods for model evaluation and parameter tuning
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Methods for working with text data, including text-specific processing techniques
Suggestions for improving your machine learning and data science skills

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