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List of All PARAM supercomputers

September 20, 2022

A country’s ability to develop its technology is one of its greatest assets, and adding AI applications to supercomputers is just the beginning. Supercomputers are high-performance systems where computational power is measured in floating-point operations per second (flops). The inventions of supercomputers date back to 1964, and the age of supercomputers in India started in the 1980s. In November 1987, the Indian Government decided to create C-DAC, the center for Development of Advanced Computing technology. C-DAC started the PARAM supercomputers, led by Vijay P. Bhatkar, the architect of India’s national initiative in supercomputing since the 1990s. Today, supercomputers in India are among the fastest 500 in the world. One of those supercomputers is a series of PARAM. PARAM means ‘supreme’ in Sanskrit, devoting the idea of supreme computer systems. Here is the list of PARAM supercomputers, organized by the year of their launch.

1. PARAM 8000

PARAM 8000 is the first machine in the PARAM supercomputers series built from scratch in 1991. A prototype of the PARAM 8000 supercomputer came second in the 1990 Zurich supercomputing show, where it was introduced and tested. PARAM 8000 was launched in the market in August 1991 with a 64-node machine, making it India’s first supercomputer. It was a collaboration of C-DAC and the Institute of computer aided design (ICAD), Moscow. PARAM 8000 was successful with its Inmos T800/T805 transputers, distributed memory MIMD (Multiple Instruction, Multiple Data) architectures, and a reconfigurable interconnection network. First installed in ICAD, Moscow, it rapidly took over the home market, attracted 14 other buyers, and was later exported to Germany, the UK, and Russia.

2. PARAM 8600

In 1992, PARAM 8600 was designed in the light of C-DAC wanting to make India’s supercomputer more powerful by integrating the Intel i860 processor. PARAM 8600 supercomputer is an upgrade to PARAM 8000, where the node structure was changed from Inmos T800/805 to one i860 and four Inmos T800 transputers. Each PARAM 8600 cluster resulted in as powerful as four times the PARAM 8000 cluster.

3. PARAM 9000

PARAM 9000 was developed in 1994 to merge cluster processing and massively parallel processing computing workloads. The standard PARAM 9000/SS used SuperSPACRC II processor variant, PARAM 9000/US used UltraSPARC processor, and PARAM 9000/AA used DEC Alpha. To accommodate newer processors, the design of PARAM supercomputers changed to modular with this version by scaling up to 200 CPUs with 32-40 processors and using Clos network topology.

4. PARAM 10000

PARAM 10000 was launched in 1998 based on SMPs (symmetric multiprocessors) clusters that is a relevantly replicated UNIX OS. It contains independent nodes where each node is based on the Sun enterprise 250 server with two 400 Mhz UltraSPARC II processors. PARAM 10000 was exported to Russia and Singapore with the base system’s best speed recorded at 6.4 GFLOPS (giga-floating point operations per second). The base configuration got three compute nodes and a server node, and the system has 160 CPUs capable of 100 GFLOPS. However, it is easily scalable to the TFLOP (trillion floating point operations) range.

5. PARAM Padma

PARAM Padma is a 1Teraflop supercomputer, which is India’s first supercomputer to earn a place, ranked 171th in the Top500 list of supercomputers of the world in June 2003. This PARAM supercomputer was launched in 2002 with a storage capacity of 1TB, 248 IBM Power4 1GHz processors, IBM AIX 5.1L Unix OS, and PARAMNet for the main connection.

6. PARAM Yuva

PARAM Yuda came out in November 2008 with a peak speed (Rmax) of 38.1 Tflops and a maximum speed (Rpeak) of 54 Tflops. It has a storage capacity of 25TB up to 200TB, 4608 cores, Intel 73XX-2.9 GHz processor, and PARAMNet 3 as the primary connection. PARAM Yuva ranked 69 in the Top500 list of supercomputers in the world after PARAM Padma as India’s supercomputer.

7. PARAM Yuva II

PARAM Yuva II was developed in February 2013, the project took three months and cost ₹160 million. The investment paid off as PARAM Yuva II became the first India’s supercomputer to reach 500 Tflops. PARAM Yuva II is a high-performance computing cluster that uses 35% less energy compared to other PARAM supercomputers and performs ten times quicker at 524 Tflops. It has a hybrid cluster with multiple interconnects, a high storage capacity of 200TB, and supports software for parallel computing. In the series of PARAM supercomputers, Yuva II is an upgrade of PARAM Yuva, which was created for the purpose of a research-oriented computational environment. PARAM Yuva II is a milestone for C-DAC in PARAM supercomputers as it ranked 1st in India, 9th in the Asia Pacific region, and 44th worldwide among the list of most powerful computer systems. Additionally, PARAM Yuva II earned a position on the Green500 list in November 2013 and again in June 2015, and also it ranked 172 in the Top500 supercomputers list in June 2015.

8. PARAM ISHAN

Param ISHAN was developed and launched in September 2016 at the Indian Institute of Technology Guwahati. It has a hybrid high-performance computing system with a peak computing performance of 250 Tflops. PARAM ISHAN has 162 computer nodes, including 126 nodes having 2 Intel Xeon E5-2680 v3, 12 cores, 2.5 GHz processors, and 64 GB RAM per node. Also, four high memory compute nodes, 16 nodes containing 2 NVIDIA Tesla k40 (GPGPU) per node, and the rest 16 nodes having 2 Intel Xeon Phi 7120 (MIC) per node. PARAM ISHAN is first India’s supercomputer with a 300TB storage capacity based on a luster parallel file system and a software stack comprising CentOS 6.6, Intel parallel studio 2016, GNU compilers, Intel MPSS, CUDA, Mellanox OFED, Luster, SLURM resource manager & scheduler and Bright cluster manager.

9. PARAM Brahma

PARAM Brahma was built in India by C-DAC and IISc under the national supercomputing mission (NSM), co-funded by the ministry of electronics and information technology and the department of science and technology. It has a computational power of 797 Tflops (Rpeak) and 526.5 Tflops (Rmax) with a storage capacity of 1PB. The unique property of PARAM Brahma is that it has a cooling system called direct contact liquid. This cooling system uses the thermal conductivity of liquids, mainly water, to maintain the system’s temperature during operations. PARAM Brahma supercomputer was launched in 2019 and, as of 2020, is available at IISER Pune and has 2 X Intel Xeon Cascadelake 8268, 24 cores, and 2.9 GHz processors.

10. PARAM Siddhi-AI

PARAM Siddhi-AI is the fastest among the PARAM supercomputers, with a Rpeak of 5.267 Pflops and a sustained Rmax of 4.6 Pflops. It is a high-performance computing artificial intelligence (HPC-AI) system built in India. The integration of AI in supercomputers helps research for advanced materials, computational chemistry and astrophysics, health care system, flood forecasting, faster simulations in the covid-19 application, and medical imaging and genome sequencing. PARAM Siddhi-AI was released in 2020, containing the NVIDIA DGX SuperPOD based networking architecture, HPC-AI engine software and frameworks, and cloud platform. It ranked 63rd in the Top500 list of supercomputers worldwide in November 2020 and is one of top India’s supercomputers sharing the position with the Pratyush supercomputer.

11. PARAM Pravega

PARAM Pravega is a recently released supercomputer in January 2022 under the national supercomputer mission at the Indian Institute of Science, Bengaluru. It hosts an array of program development tools, utilities, and libraries for developing and executing high-performance computing operations. PARAM Pravega runs on CentOS 7.x, has a combination of heterogeneous nodes, including Intel Xeon Cascadelake processors for CPU nodes and NVIDIA Tesla V100 cards for GPU nodes, and has a storage capacity of 4PB. The peak computing power of PARAM Pravega is 3.3 Pflops.

12. PARAM Ganga

Under the national supercomputing mission, PARAM Ganga is established at the Indian Institute of Technology Roorkee. Among PARAM supercomputers, PARAM Ganga is based on heterogeneous and hybrid configurations of nodes similar to PARAM Pravega. It has 312 nodes, combining CPU, GPU, and HM modes with a peak computing power of 1.67 Pflops. The cluster of the supercomputer contains compute nodes connected to Mellanox (HDR) InfiniBand interconnect network. In addition, the PARAM Ganga supercomputer uses a luster parallel file system and runs on CentOS 7.x.

13. PARAM Shakti

PARAM Shakti is a petascale supercomputer in the PARAM supercomputers series built at the Indian Institute of Technology Kharagpur under the NSM launched in March 2022. This supercomputer facility aims to amplify the research and development initiatives in academics and industries in India and focuses on solving large-scale problems in various fields of science and engineering. PARAM Shakti has 17680 CPU cores and 44GPUs and an RHDX-based cooling system, with a computing power of 1.6 Pflops.

Google’s AI model can predict odor like human beings

Disha Chopra

September 20, 2022

google ai model predicts odor like humans

A team of researchers at Google AI has developed an artificial intelligence model that can predict odor like human beings. The model maps molecule structure to the smell of a substance and is even capable of recognizing unnoticeable smells.

Today we introduce an ML-generated sensory map that relates thousands of molecules and their perceived odors, enabling the prediction of odors from unseen molecules and providing a potential tool to address global health issues like insect-borne disease. https://t.co/wmiq6wPKv5
— Google AI (@GoogleAI) September 6, 2022

Mapping molecules to recognize or predict odor is challenging because smells are senses when molecules stick to some sensory receptors in the nose. The nose is host to over 300 such receptors, making it extremely difficult to draw conclusions about an odor with certainty.

There have been a few attempts in the past to explore the interplay of molecules and map to the corresponding smells. In 2019, a graph neural network (GAN) was trained to learn several molecule-smell pairs and fall under specific labels like “floral,” “beefy,” or “minty.”

The model developed by Google researchers has successfully generated a “Principal Odour Map” (POM) with characteristics of a sensory map. Scientists have tested the model on many parameters to check if it has learned to predict odors that humans fail to recognize. The model surpassed several tests and presented exceptional results in odor prediction. They found the map could predict the activity of sensory receptors and the behavior of many animals.

Scientists hope to use POM to predict animal olfaction and their response to deadly diseases transmitted by insects who are attracted by odors. The aim is to develop less expensive, longer lasting, and safer repellents to reduce the instances of diseases and save lives.

Rephrase.ai raises $10.6 Million in Series A Funding

Disha Chopra

September 20, 2022

Rephrase.ai, an AI startup dealing with synthetic media, has raised US$10.6m in a series A funding round led by Red Ventures, a global investor with a diverse portfolio. The investment will improve the company’s capabilities in product experiences and scale hiring within engineering, marketing, and sales teams.

Rephrase.ai came into existence four years ago, intending to develop an engine to create professional-level videos “as easy as writing text.” It leverages high-grade synthetic video-making capabilities for all companies. Synthetic video creation has become an integral part of communications and content teams to humanize virtual meetings. Its synthetic video capabilities increase conversion rates, reduce costs, and improvise content quality. The company utilizes deep learning to create avatars of humans that are used in synthetic videos and text inputs.

Its video capabilities can be seen in Cadbury’s ‘Not Just a Cadbury Ad’ starring Shah Rukh Khan. the ad used Rephrase.ai’s video technology with location targeting to name local sweet stores.

Carlos Angrisano, President of Red Ventures, emphasized the investor’s aim of reinventing video production as process technology. He said, “We are impressed by Rephrase.ai’s leadership and talent bench, which is a tremendous competitive advantage in such a nascent field.”

Over the years, Rephrase.ai has developed many Chief Experience Officers (CXOs), celebrities, and influencers through lip-syncing, facial mapping, and expression capabilities and aided enterprises to grow effectively around the globe.

With 35 people, Rephrase.ai has built a top-tier team, including researchers and developers with experience at companies like Amazon and Google. Ashray Malhotra, co-founder and CEO of Rephrase.ai said, “In the last year, we’ve developed hundreds of digital human clones, creating millions of videos during the process. I’m thrilled to welcome Red Ventures, Silver Lake, and 8VC as partners on this journey to help expedite the world’s adoption of generative AI videos.”

European Union’s Metaverse initiative to come live in 2023

Disha Chopra

September 19, 2022

The European Union is in the process of integrating and formulating new regulatory frameworks for metaverse activities. The EU has announced a union-wide metaverse initiative that will come live sometime in 2023.

The metaverse initiative is part of the letter of intent that envisions a “Europe fit for the digital age.” The letter states that the EU will “continue looking at new digital opportunities and trends, such as the metaverse.”

Thierry Breton, EU’s internal market commissioner, explained that the organization would put forward many structures to address issues in existing digital systems and develop infrastructure to enhance interoperability amongst metaverse worlds.

As metaverse is based on a system of multiple services, including software, 5G connectivity, cloud platforms, etc., the organization has launched the Virtual and Augmented Reality Industrial Coalition, an institution to bring all stakeholders together.

Breton said, “We will launch a comprehensive reflection and consultation on the vision and business model of the infrastructure that we need to carry the volumes of data and the instant and continuous interactions which will happen in the metaverses.”
This is not the first time European Union has implemented tech-related frameworks and initiatives. The union recently presented a counterfeiting project for NFTs using blockchain technologies.

Meta and YouTube to expand policies, research to fight online extremism

Disha Chopra

September 19, 2022

meta youtube policies to fight online extremism

Meta’s Facebook and Alphabet’s YouTube have faced the heat due to some hate speeches and violent rhetoric surfacing on the platforms. President Joe Biden addressed the issue during the White House Summit on September 15 and called on Americans to combat online extremism. As a response, Meta and YouTube agreed to expand policies and research new techniques to fight online extremism.

YouTube, the video streaming site, currently has policies that prohibit hateful incitements. However, it has not been able to apply them extensively in preventing militia groups from streaming their agenda. Jack Malon, a YouTube spokesperson, said the updated policies would go further with enforcement.

Facebook, owned by Meta, also comes under scrutiny for having some violent comments, posts, and videos surfacing on the social media platform. Facebook announced its plan to collaborate with the Middlebury Institute of International Studies Center on Terrorism, Extremism, and Counterterrorism to delve into research and figure out more extensive policies against violent rhetorics and hateful speeches.

Other tech giants like Microsoft also attended the summit and announced a basic and affordable version of their machine learning and AI tools to track and prevent violent situations in schools.

Diffusion Bee: a Mac app that creates AI images with text

Disha Chopra

September 19, 2022

diffusion bee mac app ai images with text

Diffusion Bee is a Mac app that creates AI images with sound diffusion using an open-source AI image generation framework. The app, developed by Divam Gupta, can be installed on M1/M2 running at least macOS 15.2. It utilizes Apple’s Silicon’s built-in AI and runs the Stable Diffusion model.

Introducing Diffusion Bee, the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.

Link : https://t.co/0sHMpqezKd pic.twitter.com/4LgRbQ2iYI
— Divam Gupta (@divamgupta) September 11, 2022

Stable diffusion is a text-to-image machine learning model that enables image generation from input texts. Until now, text-to-image was only possible via MidJourney or DALL-E, initially only if you were to make the waitlist.

Besides DALL-E and MidJourney, an open-source, free-to-use Stable Diffusion tool is also accessible on GitHub or Hugging Face. While using the open-source stable diffusion tool is a feasible option, it requires you to follow a step-by-step guide to install all the requisites. Mac’s Diffusion Bee app makes it a little more convenient. You just have to drop the app in your applications folder, open click, and write your prompts.

The app enables you to tweak image height and width, the number of steps the model runs, and the importance the model gives to your text prompts. There is no fee, and it does not require any additional software/hardware connection except the internet.

The developers recommend 16GB RAM due to the memory-intensive nature of machine learning, but it works very typically with 8GB RAM without any glitch.

White House Releases ‘Comprehensive Framework’ for Responsible Development of Digital Assets

Disha Chopra

September 19, 2022

white house releases framework for digital assets

Following President Joe Biden’s March 9 executive order, the white house has released a comprehensive framework for the responsible development of digital assets, especially crypto. Over the last six months, the whole-of-government approach has been focused on mitigating risks and harnessing the benefits of digital assets.

Several agencies have collaborated to develop frameworks and policies with six key priorities: financial stability, managing illicit finance, US leadership in global finance, economic competitiveness, financial inclusions, and responsible innovation.

Nine comprehensive reports, consistent with the orders’ deadlines, have been submitted to the President. These reports suggest agencies promote innovation by indulging in private-sector research and development. The reports also call for the Central Bank Digital Currency (CBDC) risk mitigation methods by experimentation and evaluating a “Treasury-led interagency working group.”

Digital assets come with a significant risk for consumers, businesses, and investors due to price volatility. The Biden-Harris administration and regulators have been working to ensure fair play in the financial and digital assets market. The administration has issued guidance and pursued the fraudulent agents to protect consumers.

Some significant steps that have been taken:

Securities and Exchange (SEC) and Commodity Futures Trading Commission (CFTC) to enforce actions against unlawful practices.
Consumer Financial Protection Bureau (CFRB) and Federal Trade Commission (FTC) to monitor consumer grievances.
Financial Literacy Education Commission (FLEC) to lead public awareness to help consumers understand the risks of digital assets.
Office of Science and Technology Policy (OSTP) and NSF will develop a Digital Assets Research and Development Agenda to initiate advancements.
NSF will cover social sciences and the training of diverse groups of stakeholders.
The Department of Commerce will establish a forum to convene agencies, academics, and civil society to exchange debates on standards, rules, technical requirements, and research support.

You can check the official press release to know more about the framework.

Top Statistics Institutes in India 2022

Sahil Pawar

September 19, 2022

The discipline of statistics concerns the collection, organization, interpretation, analysis, and presentation of data. The practice of applied statistics, which is the root of data analysis, involves the analysis of data to help better define and determine organizational needs. Today, applied statistics and its applications are employed in various fields, including information technology, medicine, finance, engineering, marketing, accounting, business, etc.

Famous statisticians like Prasanta Chandra Mahalanobis, C.R. Rao, and Debabrata Basu have contributed profoundly to the field of statistics. India has been a major hub for statistical education and research for decades. This article lists some of the top statistics institutes in India. However, the numbering is not necessarily the institute ranking or an order of preference.

Hindu College, New Delhi

Hindu College is one of the most popular statistics institutes in India. Course offerings include BSc and MSc in statistics. The college is a constituent of Delhi University and was established in 1899, making it one of India’s oldest offering statistics courses. The statistics department at Hindu College was the first in its league at the undergraduate level at Delhi University. The statistics department has committed faculties who specialize in various academic fields related to statistics, including stochastic modeling, inference, biostatistics prediction theory, etc. Besides regular teaching, the faculty is also involved in research projects and publishing books.

Lady Shri Ram College for Women, New Delhi

Lady Shri Ram College for Women is counted among the top statistics institutes in India. The Bachelor of Science (B.Sc.) program in the department of statistics offers an exciting space to study the quantitative aspects of the biological, social, and physical sciences. The courses focus on the crucial role of statistics in diverse areas and its vitality in finance, marketing, and strategy-making. Students are given the training to acquire tools in the fields of statistical methods, applied statistics, and analysis. The courses also teach students rigorous methods and techniques to sift through a plethora of data and comment on it in a skilled manner. The emphasis of the teaching-learning process at LSR College is mainly on relating statistical concepts to real-world examples that are familiar to students.

Loyola College, Chennai

Loyola College, one of the top statistics colleges in India, is affiliated with the University of Madras. Loyola College’s statistics department was established as an undergraduate department in 1959. The department was upgraded to a postgraduate department in 1982 with the introduction of the MSc Statistics program. The department further advanced to a research department with the introduction of the M.Phil. and Ph.D. programs in statistics. The statistics department faculty is well qualified to teach allied and elective courses and provide consultancy services. The department has witnessed many outstanding students with university rankings. Loyola college is a private higher education institution that is run by the Society of Jesus in Chennai.

Madras Christian College, Chennai

Madras Christian College (MCC) is another name on the list of top statistics colleges in India. Course offerings include BSc, MSc, PhD, and MPhil in Statistics. The college is affiliated with the University of Madras, although it functions as an autonomous institute from its main campus in Chennai. The statistics department at MCC was established in 1969. However, the undergraduate course in statistics was started back in 1962. The department offers courses with a comprehensive combination of computer applications, statistics, operational research, and managerial economics. The department has a well-equipped laboratory named Gift Siromoney Statistical Computing Laboratory. One of the salient features of the statistics department is the practical training provided to the students through the conduction of socio-economic and tribal surveys and public opinions.

Presidency College, Chennai

Presidency College is one of the oldest statistics institutes in India. The department of statistics of the college is one of the pioneering departments in the south of India. The college introduced the undergraduate and postgraduate programs in statistics in 1960. The intake of students at present for UG is 24 and 18 for PG. Currently, the college is also offering research degrees, including MPhil and PhD. During the last five decades, the statistics department has produced several eminent personalities in both professional and academic domains. The research scholars and teachers of the department have made notable contributions in several areas of research, multivariate data analysis, including quantitative management, sample surveys, quality assurance, model building, etc.

Indian Statistical Institute, Kolkata

Indian Statistical Institute (ISI), Kolkata, is one of the premium statistics institutes in India. It was created out of the statistical laboratory established by Prasanta Chandra Mahalanobis in Kolkata’s Presidency College. Established in 1931, this esteemed institution is one of India’s oldest institutes focused on statistics. Indian Statistical Institute offers varied undergraduate, postgraduate and doctoral degree programs. The bachelor’s and master’s degree programs in statistics at ISI, commonly known as BStat and MStat, are flagship programs of the college that are acclaimed internationally. Research activities at the institute are organized under several broad disciplines. Research scholars, students, faculty members, and scientists at the institute work on a diverse range of problems in the statistics field.

Fergusson College, Pune

Fergusson College, Pune is one of the top statistics colleges in India that offers various courses in the stream of computational sciences like statistics. Course offerings include BSc and BA in statistics. The Bachelor of Science (BSc) in Statistics degree at Fergusson College is focused on the interpretation of numerical data and statistical analysis. The statistics department of the college was established in 1956. Various distinguished statisticians, including Dr.Y.S. Sathe, Dr. Badhe, Dr. Huzurbazar, Dr. B.K.Kale and Dr. A.V.Kharshikar have delivered lectures at the institute. Talks and lectures on core statistical concepts, innovative topics in statistics, and their applications in diverse fields are regularly held by the Statistics Association of the college.

IIT Kanpur

IIT Kanpur is another name on the list of esteemed statistics institutes in India. The Mathematics and Statistics department of IIT Kanpur shares the vision of achieving excellence in teaching and research. The institute offers MSc and PhD programs in statistics. The courses in statistics at IIT Kanpur are designed to cultivate mathematical interests, motivate research in mathematical sciences, cultivate mathematical taste, and train computational scientists to enable them to work on challenging real-life problems. The department’s faculty have written more than 40 research monographs and textbooks. Moreover, the research contributions made by the facilities of the statistics department are noteworthy. Over the years, IIT Kanpur has evolved into one of the premier departments in the country, providing excellent teaching and research in Statistics.

St. Xavier’s College, Mumbai

St. Xavier’s College, Mumbai, is counted among the prestigious statistics colleges in India. BSC in statistics. The statistics department at St. Xavier’s was started back in the late fifties. The alums have been placed in top multinational companies in several domains, including insurance, finance, risk management, portfolio analysis, and statistical software development. St. Xavier’s College’s Mumbai campus offers BSc and BA degrees in statistics. During 1987-88, six statistics units were offered at a three-year BSc level. In 1989-90 three statistics units were introduced at the three-year BA level. The statistics department was selected for the DBT Star Scheme in the year 2017. The college is affiliated with the University of Mumbai. It also offers various undergraduate and postgraduate courses in commerce and management.

Ramjas College, New Delhi

Ramjas College is another name on the list of well-known statistics colleges in India. The college provides undergraduate and postgraduate degrees under the purview of the University of Delhi. The department of statistics offers comprehensive BSc and MSc degrees in statistics. The department of mathematics and statistics of Ramjas College boasts of its state-of-the-art laboratory and well-stocked library. Since its inception, the department has played a dominant role in the evolution of modern theories of statistics and mathematics. The department also boasts of a remarkable record of a large number of student placements in renowned companies and organizations over the past several years.

PSG College of Arts and Science, Coimbatore

PSG College of Arts and Science (PSG CAS) is one of the top statistics institutes in India. It was founded by G. R. Damodaran in 1947. The college’s course offerings include BSc and MSc in statistics. The department has a well-equipped statistical computing laboratory with 56 terminals that allow students to work independently of each other. The computer system supports the standard statistical softwares such as SYSTAT, STATA, SPSS, and R Language. The students will have ample opportunities at the college to get fully trained in using these softwares, which can guarantee immediate placement after graduation. One of the salient features of the department is the practical training, both manual and computer-based, provided by way of conducting socio-economic surveys and public opinions with NSSO.

Christ University Bangalore, Bangalore

Christ University, Bangalore, is undeniably one of the top statistics institutes in India and is well renowned for its quality education. It is a deemed-to-be university. The course offerings include BSc in statistics and actuarial science. The department of statistics at Christ University intends to provide a dynamic research environment and practical education, including excellent training in scientific data collection, data management, and data analysis methods in different areas of statistics. The department offers a profound interplay between application, computation, and theories of statistics. It provides a dynamic combination of pure and applied statistics with quality software skills, allowing students to participate successfully in professional environments by gaining knowledge.

Top Data Science Books 2022

Manjiri Gaikwad

September 18, 2022

Data science has become one of the IT industry’s most paid and recommended domains. As many companies are adopting the new way of data science applications to solve their business problems, there is a requirement for skilled data science professionals. If you want to be an expert in data science, this article will help you with some top data science books that are readily available and help you learn data science from the beginning.

Data Science from Scratch (2nd Edition)

Written by Joel Grus, The Data Science from Scratch is another exciting book for beginners to study data science. This book was first released in April 2019 and highlighted the different data science tools and algorithms necessary to implement machine learning algorithms.

If you have prior experience in mathematics and programming language, this book helps you quickly start with statistics in data science. While reading this book, you can also learn the hacking skills needed to be a data scientist. The book guides you on K-nearest neighbors, decision trees, logistic regression, clustering models, and linear regression. It also exposes you to network analysis, natural language processing, and more.

Link to the book: Data Science from Scratch

Approaching (Almost) Any Machine Learning Problem

Written by Abhishek Thakur, Approaching (Almost) Any Machine Learning Problem is one of the best books for people with theoretical knowledge of machine learning and deep learning algorithms. This book focuses more on solving machine learning and deep learning real-life problems rather than explaining the basics of it.

If you want to implement deep learning and machine learning algorithms with code, this book is for you. With this book, you can explore topics like supervised learning, unsupervised learning, cross-validation, evaluation metrics, feature engineering, feature selection, arranging machine learning projects, image classification, text classification, regression, stacking, and model serving.

Link to the book: Approaching (Almost) Any Machine Learning Problem

Elements of Statistical Learning

The Elements of Statistics Learning book by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, is the complete book for machine learning fundamentals. It covers everything from supervised machine learning methods, graphical models, unsupervised machine learning methods, and high-dimensional problems. This book includes chapters on neural networks, support vector machines, classification trees, random forests, ensemble methods, spectral clustering, and non-negative matrix factorization.

Link to the book: Elements of Statistical Learning

Introduction to Data Science: Practical Approach with R and Python

Introduction to Data Science: Practical Approach with R and Python, by B. Uma Maheshwari and R. Sujatha, covers all the basic data science concepts. It guides you on the insights and golden rules needed in analyzing data. This book is used as a practical guide by many engineering/science/MBA students interested in data science.

The first author of this book, Dr. B. Uma Maheshwari, is an Associate Professor of the Decision Stream at PSG Institute of Management, Tamil Nadu, India. Whereas Dr. Sujatha, the second author of this book, is an Associate Professor at PSG college of Technology in PSG Institute of Technology, Tamil Nadu, India.

Link to the book: Python Machine Learning By Example

Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow, 2nd Edition

Written by Aurelien Geron, Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow, 2nd Edition covers the practical aspects of machine learning with efficient tools. It consists of concrete examples, minimal theory, and two production-ready Python frameworks like TensorFlow, Scikit-Learn, and Keras. With this book, you can learn techniques from simple linear regression to deep neural networks.

You need basic Python programming language knowledge to implement the algorithms included in this book. While reading this book, you will also get exposed to the topics like net architectures consisting of convolutional nets, recurrent nets, and reinforcement learning. You can also explore several training models, such as support vector machines, random forests, ensemble methods, and decision trees.

Link to the book: Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow

Deep Learning From Scratch: Building with Python From First Principles

The Deep Learning From Scratch: Building with Python From First Principles by Seth Weidman gives you a comprehensive introduction to data science with deep learning algorithms. With this book, you can start with the basics of deep learning and then move to advanced architectures, implementing everything from scratch.

The author explains how neural networks work using the first principles approach. You can also learn to apply multilayer neural networks, recurrent neural networks, and convolutional neural networks from scratch. While reading this book, you can understand how neural networks work mathematically, conceptually, and computationally on real-life deep learning projects.

This book explains all neural networks, code examples, and mathematical expressions in simple language. With this book, you can implement neural networks using the popular PyTorch framework.

Link to the book: Deep Learning From Scratch: Building with Python From First Principles

R for Data Science

Written by Garrett Grolemund and Hadley Wickham, R for Data science is an exciting book for beginners to learn data science with R. With this book, you can learn to get your data into R, transform it, visualize it, and model it accordingly. The book also explains the concepts of statistics with other data science processes like data cleaning and filtering. It also guides you in data transformation with concepts like median, standard deviation, average, and more.

The book helps you understand the raw data and how it is processed. You can also learn the different methods for transforming data and processing it to gain meaningful insights using R with the help of this book.

Link to the book: R for Data Science

Introduction to Machine Learning with Python: A Guide for Data Scientists

Introduction to Machine Learning with Python, written by Andreas Muller and Sarah Guido, was released in October 2016. This book is freely available on the O’Reilly platform with a 10-day free trial.

While reading this book, you can learn different practical ways to build machine learning models. This book focuses on the basics of machine learning applications with Python and the Scikit-learn library. The authors of this book have prioritized more on practical aspects of machine learning rather than the mathematics behind it. You need to be familiar with the Python libraries like NumPy and matplotlib to understand the concepts in the book more easily.

Link to the book: Introduction to Machine Learning with Python

Practical Statistics for Data Scientists

The Practical Statistics for Data Scientists book by Andrew Bruce and Peter Bruce is a good option if you are a beginner and want to master data science. This book was released in May 2017, and you can read it now on the O’Reilly platform with the 10-day free trial.

This book provides practical guidance on applying various statistical methods to data science and how to avoid their misuse. If you are familiar with the R programming language and have some statistics knowledge, you can quickly learn the concepts included in this book.

The book explains important concepts like randomization, distribution, sample bias, and sampling. It explains how these concepts are relevant in data science with examples.

Link to the book: Practical Statistics for Data Scientists

Business Data Science: Combining Machine Learning and Economics to Optimize, automate, and Accelerate Business Decisions

The Business Data Science book by Matt Taddy explains the basic data science concepts from the business perspective. With this book, you can learn how data science can bring value to organizations. This book explains the fundamentals of machine learning and how to use it to solve business problems and implement it with R programming language.

Matt Taddy, the developer of the Big Data Curriculum at the University of Chicago Booth School of Business, realized that many books had the basic concepts and statistics for machine learning. Still, they lacked the proper description of machine learning concepts for solving business problems. Therefore, he introduced different tools and machine learning techniques used in businesses to solve real-life problems.

This book is available online on Amazon. It is the best option for organizations that want machine learning concepts for their business strategy.

Link to the book: Business Data Science

Top Machine Learning Books

Disha Chopra

September 17, 2022

Machine learning (ML) is a sub-field of artificial intelligence (AI) that enhances software applications’ accuracy without explicit programming changes. It uses algorithms to predict future outcomes based on historical data input. All machine learning books talk about four basic approaches to machine learning, categorized based on how an algorithm predicts. There is supervised, unsupervised, semi-supervised, and reinforcement learning.

Supervised learning: In this, algorithms are applied with labeled training data and pre-defined variables to be assessed by the algorithm. Here, both the input and the output are specified.

Unsupervised learning: In this approach, algorithms are trained on unlabeled data and try to find a meaningful connection. The data and the recommendations (or predictions) are predetermined.

Semi-supervised learning: This approach is a combination of the above two types. Here, the training data may be labeled, but even then, the model is allowed to find connections on its own.

Reinforcement learning: Reinforcement learning is used to train a machine to do a pre-defined multi-step process. The algorithms are programmed to perform a task with positive/negative cues during the process. However, the algorithm majorly decides on its own.

Many study materials are available if you wish to learn more about machine learning and algorithms. Choosing the correct reference can be tedious if you do not know what you are looking for. Here is a list of some top machine learning books and a brief description of what they offer to make your task easier.

Top Machine Learning Books

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow – 2nd Edition

If you are familiar with Python programming, this machine learning book is probably the best guiding material for understanding concepts. Written by Geron Aurelien, the book also explains the tools and frameworks required to build intelligent systems. Extra attention has been given to frameworks like TensorFlow, Keras, and Scikit-Learn. Each chapter is written in a reader-friendly manner and features exercises to apply the concepts learned in the previous chapters. You can refer to the book to hone your technical skills for projects and advance your machine learning career.

Link to the book: Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Industries like medicine, finance, marketing, etc., house much information (potential data) that can be used to analyze specific trends and patterns. However, it is challenging to understand and analyze this data. This machine learning book, written by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, discusses several tools and techniques that have surfaced to overcome the challenges of understanding data. It discusses data mining techniques, machine learning models, and bioinformatics. The book also discusses many ideas from a statistical perspective. Despite the statistical approach, the focus is maintained on the concepts, not the mathematical end.

Link to the book: The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Machine Learning for Streaming Data with Python

Joos Korstanje’s machine learning book highlights the need for real-time data analysis due to evolving business and data streaming technologies. It focuses on adopting machine learning techniques to deal with data more efficiently. In the initial chapters, you will learn about the basic architecture of streaming data and real-time machine learning. The following few chapters mention a few state-of-the-art frameworks like River. In the following chapters, industrial use cases and challenges will be discussed. Once you finish the book, you will be confident in streaming data and working on your machine learning models.

Link to the book: Machine Learning for Streaming Data with Python

The Big Book of Machine Learning Use Cases – Databricks eBook

This comprehensive guide on machine learning can give you a quick start in your career. As machine learning technologies are ever-evolving, finding more relevant real-life uses becomes challenging. With this book, you can directly jump to applying the mentioned use cases, code scripts, and notebooks. You will also learn about dynamic time warping, decision trees to detect financial frauds, and sales trends via MLflows. The book will also enable you to perform multivariate time series forecasting using RNNS (recurrent neural networks). Additionally, the ebook mentions several case studies from companies like Comcast, Nationwide, and Regeneron. With this ML book, you can instantly start using the Databricks Lakehouse Platform.

Link to the book: The Big Book of Machine Learning Use Cases

Fundamentals of Machine Learning for Predictive Data Analysis

This book, written by John D. Kelleher, is one of the best machine learning books following an analytical approach. After getting familiarized with the basic concepts of machine learning, the next step is to learn about those concepts’ practical applications and real-world use cases. Knowing about the practical use of machine learning fundamentals will help you professionally, and referring to this book will help you become proficient in predictive data analytics as it provides a comprehensive collection of ML algorithms and models.

Link to the book: Fundamentals of Machine Learning for Predictive Data Analysis

Approaching (Almost) Any Machine Learning Problem

Written by Abhishek Thakur, Approaching (Almost) Any Machine Learning Problem talks about coding and practical applications. It is the perfect book for you if you are a machine learning practitioner and do not wish to delve into theoretical concepts in detail. The book is very practical and contains many coding scripts that form the backbone of machine learning algorithms. This book is an excellent problem-solving tool if you have basic knowledge about related concepts. It explains how to set up an environment, cross-validation, evaluation metrics, feature engineering, hyperparameter optimization, and many other processes. It can be referred to as a guide with readily applicable solutions.

Link to the book: Approaching (Almost) Any Machine Learning Problem

AI and Machine Learning for Coders: A Programmer’s Guide to Artificial Intelligence

Written by Laurence Moroney, based on his successful AI and ML courses, the book is an ideal place to start your transition from programming to becoming an AI and machine learning specialist. It is an introductory book that will offer a hands-on and code-based approach to enhancing your programming skills. The book also explains several machine learning scenarios like computer vision, sequence modeling, cloud computing, TensorFlow, and natural language processing (NLP). By the end of this book, you will be confident enough to build your own models using TensorFlow, code samples, and NLP-based tokenization and ultimately serve these models over the web via clouds.

Link to the book: AI and Machine Learning for Coders

Pattern Recognition and Machine Learning

Written by Christopher M. Bishop, this machine learning book is an excellent reference for learning and using statistical techniques to recognize patterns in data and machine learning. If you are acquainted with linear algebra and multivariate calculus, you will be able to gain a lot from this book. It leverages graphic models by describing probability distributions and features detailed practice exercises.

Link to the book: Pattern Recognition and Machine Learning

Applied Predictive Modeling

Applied Predictive Modeling is an introductory guide to predictive modeling and its application. Written by Dr. Kuhn and Dr. Johnson, the book will also be a treat for non-mathematical readers because it provides intuitive explanations with a problem-solving approach. The wide-ranging applications will help practitioners hone their expertise in working with accurate data. However, the book will be helpful if you have a basic understanding of statistical concepts like regression, correlation, etc. The majority of this book does not contain many complex computations, but you will need a mathematical background to understand some topics.

Link to the book: Applied Predictive Modeling

Machine Learning Yearning

Machine Learning Yearning, written by Andrew Ng, provides expert guidance to ML practitioners in decision-making, data collection, debugging, etc. It introduces all necessary topics to understand machine learning and mentions real-world case studies. However, it cannot work as a guide to ML modeling as it does not contain any codes, but it will discuss all necessary background information. It is preferable for non-mathematical readers who do not want to delve into complex computations and suit those with prior experience with machine learning models but who need a more intuitive understanding.

Link to the book: Machine Learning Yearning

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