Neural Network Programming with TensorFlow: Unleash the power of TensorFlow to train efficient neural networks

By 作者: Manpreet Singh Ghotra – Rajdeep Dua

ISBN-10 书号: 1788390393

ISBN-13 书号: 9781788390392

Release Finelybook 出版日期: 2017-11-10

pages 页数: 274

$39.99

Book Description to Finelybook sorting

If you’re aware of the buzz surrounding the terms such as “machine learning,” “artificial intelligence,” or “deep learning,” you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that.You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders.

By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow constructs.

**Contents**

1: MATHS FOR NEURAL NETWORKS

2: DEEP FEEDFORWARD NETWORKS

3: OPTIMIZATION FOR NEURAL NETWORKS

4: CONVOLUTIONAL NEURAL NETWORKS

5: RECURRENT NEURAL NETWORKS

6: GENERATIVE MODELS

7: DEEP BELIEF NETWORKING

8: AUTOENCODERS

9: RESEARCH IN NEURAL NETWORKS

10: GETTING STARTED WITH TENSORFLOW

What You Will Learn

Learn Linear Algebra and mathematics behind neural network.

Dive deep into Neural networks from the basic to advanced concepts like CNN, RNN Deep Belief Networks, Deep Feedforward Networks.

Explore Optimization techniques for solving problems like Local minima, Global minima, Saddle points

Learn through real world examples like Sentiment Analysis.

Train different types of generative models and explore autoencoders.

Explore TensorFlow as an example of deep learning implementation.

Authors

Manpreet Singh Ghotra

Manpreet Singh Ghotra has more than 15 years of experience in software development for both enterprise and big data software. He is currently working on developing a machine learning platform/api’s using open source libraries and frameworks like Keras, Apache Spark, Tensorflow at Salesforce. He has worked on various machine learning systems like sentiment analysis, spam detection and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout and the R Recommendation system using Apache Mahout. With a master’s and postgraduate degree in machine learning, he has contributed to and worked for the machine learning community.

Rajdeep Dua

Rajdeep Dua has over 18 years of experience in the Cloud and Big Data space. He worked in the advocacy team for Google’s big data tools, BigQuery. He worked on the Greenplum big data platform at VMware in the developer evangelist team. He also worked closely with a team on porting Spark to run on VMware’s public and private cloud as a feature set. He has taught Spark and Big Data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and College of Engineering Pune.

Currently, he leads the developer relations team at Salesforce India. He also works with the data pipeline team at Salesforce, which uses Hadoop and Spark to expose big data processing tools for developers.

He has published Big Data and Spark tutorials. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and he has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library.

His contributions to the open source community are related to Docker, Kubernetes, Android, OpenStack, and cloud foundry. You can connect with him on LinkedIn.

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