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Ganapathi Pulipaka

A Greater Foundation for Machine Learning Engineering: The Hallmarks of the Great Beyond in Pytorch, R, Tensorflow, and Python

by Ganapathi Pulipaka (Author)

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This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised machine learning with Gaussian Naïve Bayes, Naïve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector machines lay a more excellent foundation for statistics. The author of the book Dr. Ganapathi Pulipaka, a top influencer of machine learning in the US, has created this as a reference book for universities. This book contains an incredible foundation for machine learning and engineering beyond a compact manual. The author goes to extraordinary lengths to make academic machine learning and deep learning literature comprehensible to create a new body of knowledge. The book aims at readership from university students, enterprises, data science beginners, machine learning and deep learning engineers at scale for high-performance computing environments. A Greater Foundation of Machine Learning Engineering covers a broad range of classical linear algebra and calculus with program implementations in PyTorch, TensorFlow, R, and Python with in-depth coverage. The author does not hesitate to go into math equations for each algorithm at length that usually many foundational machine learning books lack leveraging the JupyterLab environment. Newcomers can leverage the book from University or people from all walks of data science or software lives to the advanced practitioners of machine learning and deep learning. Though the book title suggests machine learning, there are several implementations of deep learning algorithms, including deep reinforcement learning.

The book's mission is to help build a strong foundation for machine learning and deep learning engineers with all the algorithms, processors to train and deploy into production for enterprise-wide machine learning implementations. This book also introduces all the concepts of natural language processing required for machine learning algorithms in Python. The book covers Bayesian statistics without assuming high-level mathematics or statistics experience from the readers. It delivers the core concepts and implementations required with R code with open datasets. The book also covers unsupervised machine learning algorithms with association rules and k-means clustering, metal-learning algorithms, bagging, boosting, random forests, and ensemble methods.

The book delves into the origins of deep learning in a scholarly way covering neural networks, restricted Boltzmann machines, deep belief networks, autoencoders, deep Boltzmann machines, LSTM, and natural language processing techniques with deep learning algorithms and math equations. It leverages the NLTK library of Python with PyTorch, Python, and TensorFlow's installation steps, then demonstrates how to build neural networks with TensorFlow. Deploying machine learning algorithms require a blend of cloud computing platforms, SQL databases, and NoSQL databases. Any data scientist with a statistics background that looks to transition into a machine learning engineer role requires an in-depth understanding of machine learning project implementations on Amazon, Google, or Microsoft Azure cloud computing platforms. The book provides real-world client projects for understanding the complete implementation of machine learning algorithms.

This book is a marvel that does not leave any application of machine learning and deep learning algorithms. It sets a more excellent foundation for newcomers and expands the horizons for experienced deep learning practitioners. It is almost inevitable that there will be a series of more advanced algorithms follow-up books from the author in some shape or form after setting such a perfect foundation for machine learning engineering.

ISBN-10

166415129X
ISBN-13

978-1664151291
Publisher

Xlibris Us
Publication date

October 1, 2021
Language

English
Dimensions

6 x 1.25 x 9 inches
Print length

510 pages
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Product details

Publisher ‏ : ‎ Xlibris Us (October 1, 2021)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 510 pages
ISBN-10 ‏ : ‎ 166415129X
ISBN-13 ‏ : ‎ 978-1664151291
Item Weight ‏ : ‎ 1.98 pounds
Dimensions ‏ : ‎ 6 x 1.25 x 9 inches

Best Sellers Rank: #4,470,378 in Books (See Top 100 in Books)
- #660 in Natural Language Processing (Books)
- #1,106 in Computer Neural Networks
- #5,258 in Artificial Intelligence & Semantics

Customer Reviews:
9 ratings

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Ganapathi Pulipaka

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Dr. Ganapathi Pulipaka currently works as a Chief Data Scientist and SAP Technical Lead for DeepSingularity LLC.

He is also a PostDoc Research Scholar in Doctor of Computer Science, Computer Science Engineering with hands-on expertise

in Big Data Analytics, Machine Learning, Deep Learning, Robotics, IoT, Artificial Intelligence as

part of Doctor of Computer Science program from Colorado Technical University, CO with another

PhD in Data Analytics, Information Systems, and Enterprise Resource Management, California

University, Irvine.

Bestselling Author: Author of two books

“The Future of Data Science and Parallel Computing: A Road to Technological Singularity,” published on June 29, 2018 and “

Big Data Appliances for In-Memory

Computing: A Real-World Research Guide for Corporations to Tame and Wrangle Their Data,”

published December 8, 2015.

Ranked #1 Bestselling author on Amazon for published book Big Data Appliances for In-Memory Computing: A Real-World Research Guide for Corporations to Tame and Wrangle Their Data covering Big Data, Machine Learning, Data Science, Artificial Intelligence, IoT, HPC, Cloud Computing, Networks.

Published eBook in November 2017 for SAP Leonardo IoT “The Digital Evolution of Supply Chain

Management with SAP Leonardo,” sponsored by SAP Leonardo with deep learning and machine

learning algorithms for IoT and edge computing.

Published eBook in December 2017 for Change HealthCare (McKesson’s HealthCare

Corporation) on Machine Learning and Artificial Intelligence for Enterprise HealthCare and Health

Technology Solutions.

Public Keynote Speaker for Robotics and Artificial Intelligence held on May 21-22, 2018, Los

Angeles, USA.

Developed number of machine learning and deep learning programs applying various algorithms

and published articles with architecture and practical project implementations on medium.com,

data driven investor, and LinkedIn.

Ranked #5 Data Science Influencer for 2018 by Onalytica and Joe Fields.

Ranked # 4 Machine Learning Influencer for January 2018 by KCore Analytics and Hernan Makse.

Ranked #3 Deep Learning Influencer for January 2018 by KCore Analytics and Hernan Makse.

Ranked #4 Machine Learning Influencer for March 2018 by KCore Analytics and Hernan Makse.

Ranked #3 Deep Learning Influencer for March 2018 by KCore Analytics and Hernan Makse.

Ranked #3 Data Science Influencer for 2017 by KCore Analytics and Hernan Makse.

Ranked #3 Machine Learning Influencer for 2017 by KCore Analytics and Hernan Makse.

Ranked #12 Business Intelligence Influencer for 2018 by Onalytica and Joe Fields.

Top #10 SAP and AI Solution Providers for 2018 published by Mirror Review Magazine.

Top #10 SAP and AI Solution Providers for 2018 published by Insights Success Magazine.

Recognized as part of the top list of prominent machine learning, deep learning, AI researchers,

and influencers to follow outside Twitter and on Twitter by Mirror Review Magazine.

Top #20 CXO Leaders and SAP Innovative Solution Providers for 2017 published in SAP Special

Annual Edition CIOReview.

Featured as Top 22 Artificial Intelligence Experts predicting the impact of AI in the enterprise

workplace by Microsoft’s Partner Acuvate.

A Data Science Guide and Predictions for the future with Onalytica and Joe Fields (Onalytica’s

Interview – June 12, 2018).

Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018 (2018

Predictions from GP Pulipaka Published by KDNuggets).

A technology leader in artificial intelligence, SAP development, and solution architecture. A

project/program manager for application development of SAP systems, machine learning, deep

learning systems, application development management, basis, infrastructure, and consulting

delivery services offering expertise in delivery execution and executive interaction. Experienced

implementing ASAP, Agile, Agile ASAP 8, HANA ASAP 8, Activate, Prince2, SCRUM, and Waterfall

SDLC project methodologies.

Coming with a background of dealing petabyte-scale data warehouse environments in SAP,

implemented multiple SAP programs/projects managing a team size of 60+ members, managing

budget more than $5M to $10M with SAP backend databases of Oracle, IBM DB2, Sybase,

Informix, MS SQL server on Mac OS and Linux environments.

His background is in Computer Science with a professional skillset and two decades of

management and hands-on development experience in Machine Learning in TensorFlow, Python,

and R, Deep Learning in TensorFlow, Python, PyTorch, and R, SAP ABAP S/4 HANA 1609, SAP S/4

H HANA 1710, SAP IBP on SAP Cloud Platform 1805, Big Data, IaaS, IoT, Data Science, Apache

Hadoop, Apache Kafka, Apache Spark, Apache Storm, Apache Flink, SQL, NoSQL, Tableau,

PowerBI, Mathematics, Data Mining, Statistical Framework, SIEM, SAP, SAP ERP/ECC 6.0

NetWeaver Portals, SAP PLM, cProjects, R/3, BW, SRM 5.0, CRM 7.4, 7.3, 7.2, 7.1, 7.0, Java, C,

C++, VC++, SAP CRM-IPM, SAP CRM- Service management, SAP CRM-Banking, SAP PLM Web UI

7.47, xRPM, SCM 7.1 APO, DP, SNP, SNC, FSCM, FSCD, SCEM, EDI. CRM ABAP/OO, ABAP, CRM

Web UI/BOL/GENIL/ABAP Objects, SAP Netweaver Gateway (OData), SAP Mobility, SAP Fiori,

Information Security, CyberSecurity, Governance, Risk Controls, and Compliance, SAP Fiori HANA,

ABAP Webdynpros, BSPs, EDI/ALE, CRM Middleware, CRM Workflow, JavaScript, SAP KW 7.3

SAP Content server, SAP TREX Server, SAP KPro, SAP PI (PO), SAP BPC, Script logics, Azure, SAP

BPM, SAP UI5, SAP BRM, Unix, Linux, macOS, and always looking for patterns in data and

performing extractions to provide new meanings and insights through algorithms and analytics.

Media Mentions, Twitter Influencer Rankings for AI, Big Data, Machine Learning, Deep Learning, IoT, BI, eBooks published, Interviews with Media Magazines

Ranked #5 Data Science Influencer for 2018 by Onalytica and Joe Fields

Ranked # 4 Machine Learning Influencer for January 2018 by KCore Analytics and Hernan Makse

Ranked #3 Deep Learning Influencer for January 2018 by KCore Analytics and Hernan Makse

Ranked #4 Machine Learning Influencer for March 2018 by KCore Analytics and Hernan Makse

Ranked #3 Deep Learning Influencer for March 2018 by KCore Analytics and Hernan Makse

Ranked #3 Data Science Influencer for 2017 by KCore Analytics and Hernan Makse

Ranked #3 Machine Learning Influencer for 2017 by KCore Analytics and Hernan Makse

Ranked #12 Business Intelligence Influencer for 2018 by Onalytica and Joe Fields

Top #10 SAP and AI Solution Providers for 2018 published by Mirror Review Magazine

Top #10 SAP and AI Solution Providers for 2018 published by Insights Success Magazine

Recognized as part of the top list of prominent machine learning, deep learning, AI researchers, and influencers to follow outside Twitter and on Twitter by Mirror Review Magazine.

Top #20 CXO Leaders and SAP Innovative Solution Providers for 2017 published in SAP Special Annual Edition CIOReview

Published eBook in November 2017 for SAP Leonardo IoT “The Digital Evolution of Supply Chain Management with @SAPLeonardo.”

Published eBook in December 2017 for Change HealthCare (McKesson’s HealthCare Corporation) on Machine Learning and Artificial Intelligence for Enterprise HealthCare and Health Technology Solutions.

Featured as Top 22 Artificial Intelligence Experts predicting the impact of AI in the enterprise workplace by Microsoft’s Partner Acuvate.

A Data Science Guide and Predictions for the future with Onalytica and Joe Fields (Onalytica’s Interview – June 12, 2018).

Data Science, Machine Learning: Main Developments in 2017 and Key Trends in 2018 (2018 Predictions from GP Pulipaka Published by KDNuggets).

Professional Background

Ganapathi Pulipaka is a distinguished technology leader providing innovatory solution architecture on SAP Business systems, Enterprise application development design engineering, management, and consulting delivery services offers expertise in SAP delivery execution and executive interaction as a trusted advisor. He started his career as an SAP ABAP Programmer. He has implemented around 34 SAP projects for Fortune 100 corporations and various other clients in the past 23+ years on next-generation SAP Applications providing a comprehensive portfolio of consulting solutions. He worked for 21 global corporations implementing SAP projects in building Global COE for SAP ERP, HANA, CRM, SRM, SCM, PLM, PPM, and BW Netweaver products. Ganapathi Pulipaka is an SAP Certified Professional.

Customer reviews

3.7 out of 5

9 global ratings

5 star		56%
4 star 0% (0%)		0%
3 star		22%
2 star 0% (0%)		0%
1 star		23%

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Top reviews from the United States

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Amazon Customer

Great resource

Reviewed in the United States 🇺🇸 on February 19, 2022

What people need is understanding of the real-world tools required to implement machine learning. The book provides such tools, libraries, and packages with in-depth coverage of statistics and mathematics with programming skills. This is what is needed for any student to succeed from undergrad, grad, or PhD level. The blogs I read only point to a particular issue and solution for it, but I read a blog of the writer, I stumbled across this book and I’m glad I found this to be beneficial, as the author does not assume, everybody is an advanced expert.

Reviewed in the United States 🇺🇸 on January 3, 2023

Really focuses on approaches for people who use MacOS. Some pictures of the code are really low resolution so you really cannot read it. Gives little to no information about where to find the different datasets. Would recommend to buy other books in this category before this one.

Reviewed in the United States 🇺🇸 on December 16, 2021

This book includes neural networks, deep learning, machine learning, supervised, unsupervised, reinforcement learning algorithms in R, TensorFlow, PyTorch, and Python. This is a third book from the author’s earlier series of books on machine learning, deep learning, AI, IoT, and big data analytics. For people who want to get into natural language processing, robotics, IoT with reinforcement learning algorithms, this book gets you started with PyTorch and Python implementations. If you’re looking for what are strings, data variables in Python, then this book is not for you. The book assumes you’ve basic programming knowledge and it launches you from classical programming to machine learning algorithms. Though, it takes you to the advanced reinforcement learning, it gives you many practical projects for you to practise and grasp the concepts of machine learning as a foundation. I personally feel it benefits people who have not written AI code before. I’m enormously benefitted from this book by now able to implement machine learning and deep learning in R, TensorFlow, PyTorch, and Python.

One person found this helpful

Helpful

vishal

Awesome Book.

Reviewed in the United States 🇺🇸 on December 31, 2021

After reading reader's digest Gloria Pullman's review, I decide to write this review on a greater foundation for machine learning engineering. We are in the age of machines, but we're humans. How enterprises can power the machines with intelligence and build algorithmic thinking, we hear about algorithms all the time, but the author of this book engages the audience with an excellent foundation for statistics illustrating all the mathematical and stastical equations to get equipped with the machine learning. It's beneficial for all levels of researchers, beginners, and experts to get into the field of machine learning with this book. Personally, I enjoyed this book, it's highly well-researched and engaging work in the past few decades.

Helpful

Srikrishna Murthy Annam

Excellent Book for Machine Learning

Reviewed in the United States 🇺🇸 on December 6, 2021

When I started reading this book, I realized that the book has very good content. The content is very well explained. This book has a lot to offer both in the way of theory and practical real use case scenarios. The python concepts and how python can be used in ML is also explained very well. This book helped me great way to deepen my understanding of ML. This book is definitely been written by knowledgeable writer. All the topics are useful and to the point. If you are into ML and data analytics, You will find this book invaluable. I would definitely recommend this book for ML and Data Science.

Reviewed in the United States 🇺🇸 on December 31, 2021

As many books just start getting into the programs without explaining any concepts that are fundamental for the understanding of the machine learning algorithms, this book lays a greater foundation for the audience to understand and implement machine learning algorithms. The author has written multiple books and if you are enthusiastic to get into the field of machine learning or data analytics. This book is for you.

Helpful

mamidanna

A good book on machine Learning for beginners

Reviewed in the United States 🇺🇸 on December 25, 2021

he author challenges your machine learning concepts and pushes the audience to reinvent themselves by exploring all the tools and algorithms that machine learning can offer. The book aims at readership from university students, enterprises, data science beginners, machine learning and deep learning engineers at scale for high-performance computing environments. I can see myself running through several exercises provided to me for gaining practical experience for jumping onto the enterprise wide implementations.

Helpful

Kash9k

Very helpful and interesting book

Reviewed in the United States 🇺🇸 on December 6, 2021

I am a lover of AI and found this book very interesting and helpful. Worth to buy it and get benefited.

Helpful