Raghav Bali's Books

Hands-On Transfer Learning with Python

Implement advanced deep learning and neural network models using TensorFlow and Keras

By: Dipanjan Sarkar,Raghav Bali,Tamoghna Ghosh

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Hands-On Transfer Learning with Python

Implement advanced deep learning and neural network models using TensorFlow and Keras

By: Dipanjan Sarkar,Raghav Bali,Tamoghna Ghosh

Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Key Features Build deep learning models with transfer learning principles in Python implement transfer learning to solve real-world research problems Perform complex operations such as image captioning neural style transfer Book Description Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLP Who this book is for Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.

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430

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Packt Publishing Ltd

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ISBN 10

1788839056

ISBN 13

9781788839051

Learning Social Media Analytics with R

By: Raghav Bali,Dipanjan Sarkar,Tushar Sharma

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Learning Social Media Analytics with R

By: Raghav Bali,Dipanjan Sarkar,Tushar Sharma

Tap into the realm of social media and unleash the power of analytics for data-driven insights using R About This Book A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media data Learn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms. Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering. Who This Book Is For It is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise. What You Will Learn Learn how to tap into data from diverse social media platforms using the R ecosystem Use social media data to formulate and solve real-world problems Analyze user social networks and communities using concepts from graph theory and network analysis Learn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channels Understand the art of representing actionable insights with effective visualizations Analyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so on Learn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many more In Detail The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data. The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights. Style and approach This book follows a step-by-step approach with detailed strategies for understanding, extracting, analyzing, visualizing, and modeling data from several major social network platforms such as Facebook, Twitter, Foursquare, Flickr, Github, and StackExchange. The chapters cover several real-world use cases and leverage data science, machine learning, network analysis, and graph theory concepts along with the R ecosystem, including popular packages such as ggplot2, caret,dplyr, topicmodels, tm, and so on.

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394

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Packt Publishing Ltd

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ISBN 10

1787125467

ISBN 13

9781787125469

R: Unleash Machine Learning Techniques

By: Raghav Bali,Dipanjan Sarkar,Brett Lantz,Cory Lesmeister

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R: Unleash Machine Learning Techniques

By: Raghav Bali,Dipanjan Sarkar,Brett Lantz,Cory Lesmeister

Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner. About This Book Build your confidence with R and find out how to solve a huge range of data-related problems Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today Don't just learn – apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis Who This Book Is For Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science What You Will Learn Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action Solve interesting real-world problems using machine learning and R as the journey unfolds Write reusable code and build complete machine learning systems from the ground up Learn specialized machine learning techniques for text mining, social network data, big data, and more Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, big data, and more In Detail R is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it... This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R. In the first module you'll get to grips with the fundamentals of R. This means you'll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems. For the following two modules we'll begin to investigate machine learning algorithms in more detail. To build upon the basics, you'll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they're all focused on solving real problems in different areas, ranging from finance to social media. This Learning Path has been curated from three Packt products: R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar Machine Learning with R Learning - Second Edition By Brett Lantz Mastering Machine Learning with R By Cory Lesmeister Style and approach This is an enticing learning path that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.

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1123

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Packt Publishing Ltd

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ISBN 10

1787128288

ISBN 13

9781787128286

Generative AI with Python and TensorFlow 2

Create images, text, and music with VAEs, GANs, LSTMs, Transformer models

By: Joseph Babcock,Raghav Bali

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Generative AI with Python and TensorFlow 2

Create images, text, and music with VAEs, GANs, LSTMs, Transformer models

By: Joseph Babcock,Raghav Bali

Fun and exciting projects to learn what artificial minds can create Key FeaturesCode examples are in TensorFlow 2, which make it easy for PyTorch users to follow alongLook inside the most famous deep generative models, from GPT to MuseGANLearn to build and adapt your own models in TensorFlow 2.xExplore exciting, cutting-edge use cases for deep generative AIBook Description Machines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you’ll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You’ll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There’s been an explosion in potential use cases for generative models. You’ll look at Open AI’s news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that’s under the hood and uncover surprising links between text, image, and music generation. What you will learnExport the code from GitHub into Google Colab to see how everything works for yourselfCompose music using LSTM models, simple GANs, and MuseGANCreate deepfakes using facial landmarks, autoencoders, and pix2pix GANLearn how attention and transformers have changed NLPBuild several text generation pipelines based on LSTMs, BERT, and GPT-2Implement paired and unpaired style transfer with networks like StyleGANDiscover emerging applications of generative AI like folding proteins and creating videos from imagesWho this book is for This is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.

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489

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Packt Publishing Ltd

Published Date

ISBN 10

1800208502

ISBN 13

9781800208506

Nanotechnology for CO2 Utilization in Oilfield Applications

By: Tushar Sharma,Krishna Raghav Chaturvedi,Japan Trivedi

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Nanotechnology for CO2 Utilization in Oilfield Applications

By: Tushar Sharma,Krishna Raghav Chaturvedi,Japan Trivedi

Nanotechnology for CO2 Utilization in Oilfield Applications delivers a critical reference for petroleum and reservoir engineers to learn the latest advancements of combining the use of CO2 and nanofluids to lower carbon footprint. Starting with the existing chemical and physical methods employed for synthesizing nanofluids, the reference moves into the scalability and fabrication techniques given for all the various nanofluids currently used in oilfield applications. This is followed by various, relevant characterization techniques. Advancing on, the reference covers nanofluids used in drilling, cementing, and EOR fluids, including their challenges and implementation problems associated with the use of nanofluids. Finally, the authors discuss the combined application of CO2 and nanofluids, listing challenges and benefits of CO2, such as carbonation capacity of nanofluids via rheological analysis for better CO2 utilization. Supported by visual world maps on CCS sites and case studies across the industry, this book gives today's engineers a much-needed tool to lower emissions. - Covers applications for the scalability and reproducibility of fabrication techniques for various nanofluids used in the oilfield, including visual world maps that showcase current stages and future CCS sites - Helps readers understand CO2 case studies for subsurface applications, including CO2 injection into depleted reservoirs - Provides knowledge on the existing challenges and hazards involved in CO2 for safer utilization

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312

Publisher

Gulf Professional Publishing

Published Date

ISBN 10

0323906516

ISBN 13

9780323906517

Unburdening A Burden

By: Raghav Dwivedi

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Unburdening A Burden

By: Raghav Dwivedi

Unburdening a Burden' is a bouquet of 121 devotional sonnets written in simple yet graceful language which hitherto remains unparalleled in the history of English literature for its exquisite beauty, quaint whimsicality and richness of illustration. The style in which the book is written is a model of lucidity and order and the language employed exhibits a chiselled excellence and a well-trimmed form. The emotions expressed in the book are garbed in rich and graceful drapery of apposite language. There is natural grace, classical elegance and pleasing beauty of description. Every word expresses with directness and vividness what is there in the writer's mind. The work has rational cadence, a super-abundant wealth of words and superfluous ornaments. There is irresistible charm of congruity and magnificent evocation transfused with sublimity. Its expository and ratiocinative approach is inimitable. Clarity of thought and felicity of expression adds a deeper crimson to the blooming rose of the work.

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164

Publisher

Lulu.com

Published Date

ISBN 10

1304726819

ISBN 13

9781304726810

Kali's Daughter

By: Raghav Chandra

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Kali's Daughter

By: Raghav Chandra

On a chilly November morning in Geneva, Deepika Thakur prepares to address the United Nations Human Rights Council. Despite her personal experience of oppression as a Dalit woman, she must claim that the Indian government remains firmly committed to eradicating castebased discrimination in the country. As echoes of humiliation and atrocities flood her memory, Deepika is transported back in time, to almost six years ago, when she became the first member of her family to be selected for the Indian Civil Services. She had moved from Bhopal, her home town, to the Lal Bahadur Shastri National Academy of Administration in Mussoorie, to prepare for a career as a civil servant. It was here that she met Aman, an uppercaste Brahmin, and Vijay, a fellow Dalit. Both relationships defined by caste and class politics, Deepika had found herself in the crosshairs of an ancient history built on inequality and prejudice. Yet, as a diplomat from India's Foreign Service, she must deny caste, and the fact that India's fractured society, despite its apparent modernization and progress, remains stuck in the middle ages. Her father's words come back to haunt her: ‘When you cannot fight the system, you must endure.’ Will Deepika fight? Will she endure? What will she say to the Human Rights Council? How will she represent India to the rest of the world?

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358

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Pan Macmillan

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ISBN 10

1529039371

ISBN 13

9781529039375

Immersive Systemic Knowing

Advancing Systems Thinking Beyond Rational Analysis

By: Raghav Rajagopalan

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Immersive Systemic Knowing

Advancing Systems Thinking Beyond Rational Analysis

By: Raghav Rajagopalan

This book advances systems thinking by introducing a new philosophy of systemic knowing. It argues that there are inescapable limits to rational understanding. Humankind has always depended on extended ways of knowing to complement the rational-analytic approach. The book establishes that the application of such methods is fundamental to systemic practice. The author advocates embracing two modes of consciousness: intentionality, which Western philosophy has long recognized, and non-intentional awareness, which Eastern philosophy additionally highlights. The simultaneity of these two modes of consciousness, and the variety of knowings they spawn are harnessed for a more holistic, systemic knowing. Four practices from fields related to systems thinking are examined: two contemporary action research methodologies from the US and the UK; the Sumedhian (Indian) approach to inquiry about processes within groups; and a technique of group psychotherapy originating in Eastern Europe. Each of these systematically harnesses knowing using both modes of consciousness. Therefore, the author insists, such approaches must be included in systemic practice, in purposeful and methodical juxtaposition to rational-analytic ways. The book provides examples and guidelines for deployment. “All researchers and practitioners of systems thinking and action research must read this book...Raghav has craftfully blended Eastern and Western wisdom. He uses his immersion into Eastern ways of knowing practically, to elaborate the systems philosophy in rich detail. He has incorporated, from cooperative inquiry as action research, the idea of four ways of knowing: practical, propositional, presentational and experiential, to bolster the foundations of systems thinking”―SHANKAR SANKARAN, Professor, University of Technology Sydney, Australia; President International Society of Systems Sciences (ISSS) 2019-2020 “This is a book with the potential to stimulate the emergence of a new paradigm. Raghav shows that systems thinking can transcend rational analysis and incorporate other ways of knowing, such as arts-based methods... also, rather than be overly preoccupied with striving for change, there is value in simply abiding, which comes with a deep appreciation of the ecological relationships we are part of. It’s not that rational analysis is wrong – it’s that it is only part of a genuinely transformative practice”. ―GERALD MIDGLEY, Co-Director, Centre for Systems Studies, University of Hull; former President, ISSS (2013-14) “Raghav Rajagopalan’s writing on generating deep appreciation for the social and ecological interdependencies ties in closely with my own work. The philosophical ideas he develops contain the tracings and essential tones of Gregory Bateson’s idea of "Mind" as a process of living complexities reaching well beyond the notion of the body. This book demonstrates outstanding erudition and deep compassion at the same time. It should delight the adventurous reader unafraid of big questions”.―NORA BATESON, President of the International Bateson Institute

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231

Publisher

Springer Nature

Published Date

ISBN 10

3030491358

ISBN 13

9783030491352

R: Unleash Machine Learning Techniques

By: Raghav Bali,Dipanjan Sarkar,Brett Lantz,Cory Lesmeister

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R: Unleash Machine Learning Techniques

By: Raghav Bali,Dipanjan Sarkar,Brett Lantz,Cory Lesmeister

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1123

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Packt Publishing Ltd

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1787128288

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9781787128286

R Machine Learning By Example

By: Raghav Bali,Dipanjan Sarkar

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R Machine Learning By Example

By: Raghav Bali,Dipanjan Sarkar

Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully About This Book Get to grips with the concepts of machine learning through exciting real-world examples Visualize and solve complex problems by using power-packed R constructs and its robust packages for machine learning Learn to build your own machine learning system with this example-based practical guide Who This Book Is For If you are interested in mining useful information from data using state-of-the-art techniques to make data-driven decisions, this is a go-to guide for you. No prior experience with data science is required, although basic knowledge of R is highly desirable. Prior knowledge in machine learning would be helpful but is not necessary. What You Will Learn Utilize the power of R to handle data extraction, manipulation, and exploration techniques Use R to visualize data spread across multiple dimensions and extract useful features Explore the underlying mathematical and logical concepts that drive machine learning algorithms Dive deep into the world of analytics to predict situations correctly Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action Write reusable code and build complete machine learning systems from the ground up Solve interesting real-world problems using machine learning and R as the journey unfolds Harness the power of robust and optimized R packages to work on projects that solve real-world problems in machine learning and data science In Detail Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems. This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems. You'll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms. Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R. Style and approach The book is an enticing journey that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.

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340

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Packt Publishing Ltd

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ISBN 10

1784392634

ISBN 13

9781784392635

Learning Social Media Analytics with R

By: Raghav Bali,Dipanjan Sarkar,Tushar Sharma

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Learning Social Media Analytics with R

By: Raghav Bali,Dipanjan Sarkar,Tushar Sharma

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394

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1787125467

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9781787125469

Book's by Raghav Bali

Hands-On Transfer Learning with Python

Hands-On Transfer Learning with Python

Learning Social Media Analytics with R

Learning Social Media Analytics with R

R: Unleash Machine Learning Techniques

R: Unleash Machine Learning Techniques

Generative AI with Python and TensorFlow 2

Generative AI with Python and TensorFlow 2

Nanotechnology for CO2 Utilization in Oilfield Applications

Nanotechnology for CO2 Utilization in Oilfield Applications

Unburdening A Burden

Unburdening A Burden

Kali's Daughter

Kali's Daughter

Immersive Systemic Knowing

Immersive Systemic Knowing

R: Unleash Machine Learning Techniques

R: Unleash Machine Learning Techniques

R Machine Learning By Example

R Machine Learning By Example

Learning Social Media Analytics with R

Learning Social Media Analytics with R

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