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TensorFlow: Powerful Predictive Analytics with TensorFlow

You're reading from TensorFlow: Powerful Predictive Analytics with TensorFlow Predict valuable insights of your data with TensorFlow

Product type Paperback

Published in Mar 2018

Publisher Packt

ISBN-13 9781789136913

Length 164 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Predictive Analytics

Author (1):

Md. Rezaul Karim

View More author details

Table of Contents (6) Chapters

Preface

1. From Data to Decisions – Getting Started with TensorFlow FREE CHAPTER

2. Putting Data in Place – Supervised Learning for Predictive Analytics

3. Clustering Your Data – Unsupervised Learning for Predictive Analytics

4. Using Reinforcement Learning for Predictive Analytics

A. Assessment Answers

Assessments

Each tensor is described by a unit of dimensionality called ____.
1. Data type
2. Rank
3. Variables
4. Fetches
State whether the following statement is True or False: TensorFlow uses the computation graph to execute an application, where each node represents an operation and the arcs are the data between operations.
State whether the following statement is True or False: NumPy has the n–dimensional array support, but it doesn't offer methods to create tensor functions and automatically compute derivatives (+ no GPU support).
Which objects does a TensorFlow graph contains?
When you're performing a little complex operation with TensorFlow, for example training a linear regression, TensorFlow internally represents its computation using a dataﬂow graph. The graph is called as?
1. Dataflow graph
2. Linear graph
3. Computational graph
4. Regression graph

You have been reading a chapter from

TensorFlow: Powerful Predictive Analytics with TensorFlow

Published in: Mar 2018

Publisher: Packt

ISBN-13: 9781789136913

© 2018 Packt Publishing Limited All Rights Reserved

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Authors (1)

Karim

Karim

Md. Rezaul Karim is a researcher, author, and data science enthusiast with a strong computer science background, coupled with 10 years of research and development experience in machine learning, deep learning, and data mining algorithms to solve emerging bioinformatics research problems by making them explainable. He is passionate about applied machine learning, knowledge graphs, and explainable artificial intelligence (XAI). Currently, he is working as a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Centre for Data Analytics, Ireland. Previously, he worked as a lead software engineer at Samsung Electronics, Korea.

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