Rights offering detail :
Mastering PyTorch: Deep learning frameworks in high performance computing environments, PyTorch for Advanced Data Scientists
Dec. 26, 2018
Ganapathi Pulipaka
International rights

1. Problem statement

PyTorch is powered by GPUs with tensor computations with its first-class integration of Python for advanced data scientists and AI deep learning researchers running large-scale datasets. PyTorch performs extremely faster inference by optimizing mobile systems with IoT edge computing on Android, iPhone, and other devices. By the end of this course, advanced data scientists will be able to take their deep learning on PyTorch to the next level by developing advanced deep learning models.

Due to the absence of significant literature, this book as a course aims to create a new body of knowledge for the enterprises to choose the right computing machinery for machine learning and deep learning implementations in their organizations at scale. The traditional data analytics crunching the big data are unable to provide the insights with data-processing power. The rise of artificial intelligence in the recent decade has shown the potential of tapping into deep learning and machine learning platforms with the right processes and optimized hardware. Organizations, researchers, and developers like you need the ability to process large-scale big data in heterogeneous distributed systems for data processing on data centers with GPU accelerators. Several deep learning frameworks such as Python, TensorFlow, and R offer a variety of features and ability to process imperative and declarative programming languages. However, there is no integrated research that offers the insights into the deep learning frameworks to crunch the big data at scale. There are limitations and advantages of each framework for the organizations to move to the next level from classical statistics to the next generation advanced machine intelligence and artificial intelligence. This video course will take the audience through the research-driven code on PyTorch that helps you as an advanced data scientist in building advanced deep learning models.

2. The importance of the problem
Deep learning is applied to various fields of artificial intelligence such as neural machine translation, natural language processing, machine vision, speech recognition, and object detection. Recurrent neural networks are applied to denoise the speech signals to identify the speech patterns. A stack of autoencoders is applied on genomics with deep learning models to discover new drugs and understand the causes for the diseases. To take deep learning to the next level by pushing the limits of artificial intelligence, it’s critical to understand, why the deep learning models are the way they are today. Many researchers and developers of artificial intelligence built the deep learning framework inspired by the neural networks architecture of the human brain. Restricted Boltzmann Machine and Deep Belief Net models built to tear down the performance wall and computational workloads. This course will introduce you to PyTorch and take you to the next level of building advanced deep learning models with PyTorch.

Deep learning is a subfield of machine learning that is the branch of artificial intelligence under Computer Science Engineering. The genesis of deep learning models evolved from neural networks, recurrent neural networks, convolutional neural networks, and deep belief neural networks. In recent times, there are several innovatory deep learning models that are built based on generative adversarial networks and variational autoencoders. As the world is progressing with building artificial general intelligence, this course aims to build the necessary programming skills to advance your understanding and ability to code in PyTorch by building various deep learning models.

4. This book is the best solution to the problem
The machine intelligence of algorithms is now distributed in a cloud-computing environment and will aid the organizations in future to discover valuable insights and perform several operations through APIs.
Organizations are mass-manufacturing algorithms since it meets economies of scale in a distributed environment. Artificial intelligence is the new inferno for powering AI winter (that lasted from 1990s through 2010s) with the machine intelligence platforms through deep learning to rapidly prototype and deploy in production from sandboxes. A number of open-source machine learning and deep learning platforms have been released in the recent times such as TensorFlow by Google, Caffe by University of Berkeley, NLTK (Natural Language Tookit) by University of Pennsylvania for natural language processing, Scikit-learn machine learning library for Python, a number of R packages for deep learning and machine learning, Theano a numerical computation library for Python, Torch a platform for developing machine learning and deep learning with an underlying C implementation. However, PyTorch, a replacement of numpy package in Python has taken artificial intelligence to the next level with built-in support for GPUs with tensor computations. The course uses more than 1600 hours of research performed on deep learning frameworks as part of Postdoc research dissertation in Computer Science Engineering to benefit you and your organizations with real-world hands-on examples.


1. This course intends to offer expertise for data science enthusiasts, data scientists, and advanced data scientists with an approach covering advanced deep learning algorithms for advanced data scientists.
2. This course lays a solid foundation in deep learning and helps you to advance your skills in the deep learning research field to become a deep learning expert. Though, the course expects some basic algebra background, it transitions a data scientist from the field of deep learning to an advanced deep learning engineer or advanced data scientist.
3. The purpose of building this course is for you to follow the videos with step-by-step guides that can help you a build a data science team in your organization for performing advanced research in artificial intelligence. It can also help individuals to gain more hands-on experience with PyTorch and secure a career in the field of deep learning.

• Gain hands-on experience with natural language processing and computer vision using real-world and practical examples.
• A data scientist who is looking to master PyTorch and expand his/her career in deep learning can benefit from this course.
• Many organizations hire data scientists every day. However, the organization expects to build a large-scale data science team by cross-training the data scientists. This video course aims to build a large-scale data science teams or AI practice for any consulting company. If you’re a corporation, get this course to cross-train data scientists to advance their careers from data scientists to senior data scientists and principal data scientists.
This book covers:
1. An advanced data scientist expert series level with complete deep learning architecture and hands-on instructions approach with deep learning on PyTorch covering natural language processing, reinforcement learning, transfer learning, recurrent neural networks, translation with sequence to sequence network and attention with interesting and illustrative examples, clear explanations.
2. Follow me while coding or code along with me for tips and advice to succeed and overcome obstacles.
3. The course will begin with deep learning architectures and various types of algorithms in high performance computing environments and take the data scientists to the advanced level.


• Dr. Ganapathi Pulipaka currently works as a Chief Data Scientist and SAP Technical Lead for one of the largest technology practice corporations in the world.
• He is also a PostDoc Research Scholar in 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 of two books on Amazon “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.
• 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 Conference 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.
•. Ranked #5 Machine Learning, #1 Analytics, #3 Data Science #1 Big Data by Agilence Authority Index
• 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).
•. Recognized as 8 AI Influencers You Must Follow On Twitter To Stay On Top Of Your Game by Analytics India Magazine.
• 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 with 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.
Rights sold:
Rights available.
Other Information:
Yes. The book is in works. Initially, Packt offered a video course contract for the book. However, I'm looking for an agent who can get a contract and publish it with a publishing company than publishing it as a video course.
Currently, there is no book or competition for this particular topic in the market. I'm open to amending the book based on the requirements of the publishers to turn it into pure research material in a scientific journal or for corporate audience for AI use cases.
Ganapathi Pulipaka
Los Angeles, CA 90278
Offering #:
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