The New York City public school system has banned the use of ChatGPT on school networks and devices which is an AI-based program for generating text, citing concerns about the safety and accuracy of the content produced.
The artificial intelligence chatbot, which is developed by OpenAI, was released publicly for users to test in November.
New York City schools said ChatGPT would be banned across the district. However, specific sites or schools will be able to request access to provide students with cutting-edge tech education.
A spokesperson for the city’s department of education, Jenna Lyle, said that due to concerns over negative impacts on students’ learning and the accuracy of content, access to ChatGPT is restricted on devices and networks of New York City Public Schools.
While the tool seems able to provide easy and quick answers to questions, it does not, however, build critical thinking and problem-solving skills, which are essential for academic and lifelong success, they added.
Soon after the release, some users began to speculate about how the program may be used to help students cheat in writing essays. According to OpenAI, officials are working on ways to identify text generated by the bot.
Google has announced that they are officially shutting down Google Stadia, a cloud gaming service that allows players to access a wide range of video games through the internet. According to a statement, Google Stadia failed to generate enough traction as the company first anticipated.
Players will continue to have access to the games library and play till January 18, so they can complete final play sessions. Google will refund all Stadia hardware purchases that were made through the Google Store and all game add-on content purchases made through the Stadia store.
Stadia allowed players to play games on a variety of devices, such as laptops, smartphones, and TVs, as long as one had a compatible screen and a good internet connection.
One of the significant features of Google Stadia was that it enabled players to access games at high frame rates and in up to 4K resolution. This allowed players to experience smooth gameplay and high-quality graphics, even on devices that may not be powerful enough to natively run the games, including certain smart TVs.
Moreover, Stadia allowed players to use various controllers, including the Stadia Controller, as well as specific third-party controllers.
The racing division of famous automaker Ferrari, Scuderia Ferrari, is the latest to join the rising number of Formula One racing teams who have terminated their ties with their cryptocurrency sponsors. After a tumultuous first year, Ferrari ended its multi-year collaboration deals with Velas Blockchain. Ferrari has severed partnerships with Snapdragon as well, costing the Italian club a net US$55 million in losses before the 2023 season.
In late 2021, Ferrari announced a long-term partnership with Velas Blockchain, with branding appearing on vehicles and driver apparel. The primary objective behind the partnership was to boost fan engagement via NFTs and other joint projects. But, according to RacingNews365, the team did not adhere to the provisions that allowed Velas to produce NFT images. Meanwhile, Velas Blockchain is allegedly facing a financial breach. As a result, now both parties are considering taking legal action.
Another insider informed RacingNews365 that the split from Snapdragon is mutual and that a joint statement will be released soon. Snapdragon is expected to join another F1 team, possibly Mercedes, following the announcement of a major ‘cockpit’ electronics joint venture between its parent company Qualcomm and Mercedes.
About a quarter of Scuderia’s commercial revenue was generated by the Velas Blockchain, and Snapdragon combined. Now, Ferrari must fill a sizable vacuum in its portfolio as a result of the loss of Velas and Snapdragon.
This development comes before Ferrari’s plans to unveil its 2023 F1 car on Valentine’s Day on 14th February, though the launch location is yet to be announced.
The cryptocurrency landscape has changed drastically over the past year, with the value of cryptocurrencies plummeting alarmingly fast. The most notable illustration of the volatility and fragile financial status of cryptocurrency sponsors in Formula One was kickstarted by the collapse of FTX. In November 2022, after discontinuing its relationship with FTX as the cryptocurrency exchange filed for Chapter 11 bankruptcy, rival team Mercedes suffered a loss of US$15 million. At the time, Team Principal Toto Wolff predicted that most F1 teams might witness a similar effect.
In December, Red Bull Racing and Tezos Foundation cut ties after the blockchain platform apparently decided not to renew their contract due to a lack of strategic alignment.
NVIDIA announced important updates to Isaac Sim, a robotics simulation and synthetic data generation tool that allows users to create and test virtual robots in a variety of operating scenarios, at CES 2023. Companies adopting the robots platform can automate a variety of industries, from shipping and manufacturing to energy, retail, and more, thanks to improved AI and cloud access features.
Isaac Sim is accessible via the cloud and is built on Nvidia Omniverse, the company’s platform for developing and running metaverse applications. This enables teams working on robotics projects around the world to collaborate with increased accessibility, agility, and scalability for testing and training virtual robots.
Using Isaac Sim makes it possible for robot manufacturers to train and test robots more efficiently. NVIDIA asserts that roboticists can create more realistic simulations of a robot interacting with a virtual imitation of a real-world in ways that go beyond what is achievable in the real world.
With Isaac Sim’s new people simulation feature, you can mimic how people might interact in settings like warehouses and manufacturing plants while being instructed to carry out common tasks like stacking goods or pushing carts. According to NVIDIA, many of the most typical behaviors are already supported, so simulating them only requires issuing a command. The objective is to assist autonomous mobile robots (AMRs) or collaborative robots (cobots) in understanding and detecting common behavior and potential hazards in the real world.
Isaac Sim can now display physically correct sensor data in real-time thanks to NVIDIA RTX technology. This includes ray tracing offers more precise sensor data for an RTX-simulated lidar under varying lighting situations or in response to reflective materials. In order to ensure that the robots are trained as accurately as possible, this feature enables simulated worlds to be built on physically realistic sensor models, minimizing the disparities between the simulation and the real environment.
For developers and users to get started building right away, Isaac Sim updates also feature a load of new simulation-ready 3D assets, such as warehouse components and well-known robots. These 3D simulation elements are essential for creating physically accurate simulated settings.
Additionally, NVIDIA unveiled advancements in Isaac Gym for reinforcement learning and Isaac Cortex for collaborative robot programming, two important new capabilities for robotics researchers. Furthermore, it introduced a brand-new tool called Isaac ORBIT that offers benchmarks and simulation operating environments for robot learning and motion planning.
NVIDIA further notes that Isaac Sim has upgraded support for ROS 2 Humble and Windows for the sizable community of Robot Operating System (ROS) developers. It is now possible to simulate using the entire Isaac ROS software.
The antitrust watchdog in South Korea said it will penalize Tesla 2.85 billion won ($2.2 million) for failing to inform its consumers that their electric vehicles had a reduced range when the temperature is low outside. The alleged infractions of the country’s advertising legislation might be a blow for the US automaker, which is looking to grow its footprint in the Asian country.
The Korea Fair Trade Commission (KFTC) said that from August 2019 until recently, Tesla’s official local website had overstated the driving ranges of its cars on a single charge, their fuel cost-effectiveness compared to gasoline vehicles, and the performance of its Superchargers. When the watchdog opened a probe in February last year, the Elon Musk-led company changed the advertisement on its Korean website.
Tesla does not mention the drop in driving range in below-freezing conditions on its South Korean website, but it does provide winter driving recommendations including pre-conditioning cars using external power sources and using its updated Energy app to monitor energy usage. According to the KFTC, the driving range of Tesla vehicles decreases in cold weather by up to 50.5% compared to how they are advertised online.
According to information from the nation’s environment ministry, a South Korean consumer group Citizens United for Consumer Sovereignty, stated in 2021 that the driving range of most EVs falls by up to 40% in freezing weather when batteries need to be heated, with Tesla incurring the most drop.
The KFTC quoted the long-range Model 3 example from Tesla and stated the company promised the car could drive more than 446 kilometers on a full charge. However, the battery performed far worse than the company marketed in most of the scenarios. In freezing conditions below minus 7 degrees Celsius, the Tesla model could travel only to 220.7 kilometers on a single charge, which is less than half of the claimed range. According to the antitrust regulator, Tesla used the word “up to” on its website in the US to highlight the vehicle’s maximum driving range, in contrast to the deceptive advertising employed here. Tesla also ran deceptive or overstated marketing on its V2 and V3 superchargers, as well as the amount of money that can be saved by charging.
According to the KFTC, the company prevented customers from making fair deals by providing false information about the factors EV purchasers would most likely consider before purchasing.
Tesla was also thought to have broken the e-commerce transaction law in South Korea. When buying its automobiles online, Tesla asks for 100,000 won as a deposit from local buyers. Even if consumers changed their minds and canceled their purchases before the automobiles arrived, the deposits were nonrefundable. As a result, Tesla gathered a total of 95.2 million won in fees between January 2020 and January 2021, which they referred to as “cancellation penalties.”
The antitrust watchdog also noted that although local customers could place their orders online, they could only cancel them by calling the customer support line. The FTC viewed it as an infringement of customers’ rights to cancel orders and imposed a correction order, as well as a 1 million won penalty on Tesla for violations of the Electronic Commerce Act.
This development could make it more difficult for South Korean President Yoon Suk Yeol to implement his plans to provide “tailored” incentives to Tesla in order to persuade them to build an electric car ‘gigafactory’. For false advertising regarding the gas emissions of its diesel passenger vehicles, the KFTC slapped German automaker Mercedes-Benz, and its Korean subsidiary with 20.2 billion won last year.
The antitrust agency pledged to make every effort to provide accurate and trustworthy information to customers by regularly monitoring the businesses’ unethical behavior.
At the Consumer Electronics Show (CES) 2023, Sony showcased a brief look of the metaverse experience that the company is building in partnership with the football club Manchester City.
According to a press release, Sony and Manchester City call this metaverse experience a “proof of concept” (PoC). This special experience will enable players to participate in activities or events with their own custom avatars at a virtual version of the team’s Etihad Stadium.
In an official video posted by Sony, one can see virtual avatars running around the stadium field, dancing and celebrating together. A senior product planner at Sony, Nami Iwamoto, said that avatars, 3D images, and other expressions, which are unique to the metaverse, will allow players to communicate in a new way.
Another video by the company explained how Sony used only seven sensors to record volumetric data and footage of Manchester City players to recreate them in this metaverse space digitally.
According to Sony’s spokesperson Yo Kikuchi, the app will be released this year. However, it is still unclear whether the virtual stadium, avatar creation, and highlights will be included in that application.
The partnership was first announced in November of 2021. At the time, both Manchester City and Sony used the term metaverse to address the initiative and described the effort as a PoC.
GoogleAI has introduced a novel text-to-image synthesizing model, Muse, using a masked image modeling approach with generative transformers. Muse is trained on a masked modeling task in discrete token space using the text embedding derived from a pre-trained large language model (LLM).
Generative image models have advanced significantly over the past few years because of novel training methods and improved deep learning architectures. As a result, many image generation models like DALL-E 2, Midjourney, and Stable Diffusion have been developed. But with Muse, Google takes the technology a step further.
Muse comprises several sub-models, like the VQGAN tokenizer model for encoding and decoding, a base masked image model to predict marginal distributions of tokens, and a superres transformer model to transform low-resolution into high-resolution with T5-XXL embeddings.
Since Muse employs discrete tokens and needs fewer sample iterations than pixel-space diffusion models like Imagen and DALL-E 2, it claims to be more efficient. The model iteratively resamples image tokens based on a language prompt to produce a zero-shot, mask-free editing for free.
The researchers trained multiple Muse models with varying sizes between 632M to 3B parameters. Muse uses parallel decoding architecture, combining several decoded bits to accomplish an instruction. Due to this architecture, Muse outperforms Parti, an autoregressive model. The researchers also claim that Muse is approximately 10 times faster at inference than Imagen 3B or Parti 3B models.
Per the PartiPrompts assessment, Muse generates images better related to the text prompt at least 2.7 times more accurately than Stable Diffusion, as it can generate images using nouns, adjectives, verbs, and other parts of speech.
Statistics is the crucial component of data science that helps data science learners to capture and convert data patterns into meaningful insights. Data scientists perform statistics to gather, review, analyze, and draw conclusions from data. Consequently, gaining expertise in statistics is essential for data scientists to obtain accurate insights into the data. This article provides an overview of some of the crucial and widely used books for statistics in data science so that you can improve your knowledge of statistics.
Statistics books for data science
Listed below are some essential and most-read statistics books for data science that are available on Amazon.
New Advances in Statistics and Data Science
Written by Ding-Geng Chen, Zhezhen Jin, Gang Li, Yi Li, and Yichua Zhao, The New Advances in Statistics and Data Science book is a collection of selected papers from the 4th ICSA-Canada Chapter Symposium. It also includes the invited articles from established researchers in the field of statistics and data science.
The book covers various topics like methodology development in data science, methodology in the analysis of high dimensional data, features screening in ultra high dimensional data, statistical analysis challenges in sampling, and multivariate survival models. With this book, you can use frontier research methods to tackle research, education, training, consultancy, and more problems.
Published in March 2019, The Art of Statistics: Learning from Data by Professor David Spiegelhalter provides readers with essential principles needed to derive knowledge from the data. He has used real-life problems in the book to explain conceptual topics and determine how statistics can be applied to make important decisions. Students who want to use statistics to solve or analyze real-life problems can use this book.
The Art of Statistics is one of Professor’s David best-selling books and has been published in more than 11 languages. Professor David is the Chairperson of the Winton Center for Risk and Evidence Communication in the Center for Mathematics Sciences at the University of Cambridge. He was appointed as the President of the Royal Statistical Society in 2017-2018 and became a Non-Executive Director of the UK Statistics Authority in 2020.
Naked Statistics: Stripping the Dread from the Data
The Naked Statistics: Stripping the Dread from the Data book by Charles Wheelan mainly focuses on the underlying intuition behind statistical analysis while moving away from the technicalities.
The author Wheelan highlights concepts such as regression analysis, inference, and correlation. He teaches how data can be manipulated and interpreted by third parties and how it can be explored by data scientists to answer difficult questions.
Naked Statistics is the best book for people who believe in learning by understanding intuition rather than mathematical theories. It is the perfect book in data science for statistics and probability.
Written by Peter Bruce and Andrew Bruce, Practical Statistics for Data Scientists is one of the best books for data science. It explains how to apply various statistical methods while avoiding mistakes.
This book’s authors explain how exploratory data analysis is the initial step in data science. They have then covered essential topics such as regression, classification methods, random sampling, principles of experimental designs, and some machine learning techniques that can be learned from data.
This book gives you the statistical perspective that you need to perform the duties of a data scientist effectively. If you have a basic knowledge of R programming language, this book is the best for data science statistics.
The Computer Age Statistical Inteferce is a book by Bradley Efron and Trevor Hastie that explores the data analysis and data science revolution with classical inferential Bayesian, Fisherian, and Frequentist theories.
It guides you on the theories behind machine learning algorithms with in-depth explanations and use-case examples on spam data. This book also covers hypothesis testing, deep learning, empirical Bayes, machine learning, the jackknife and bootstrap, inference after model selection, and Markov chain Monte Carlo.
Computer Age Statistical Inference is divided into Classical Statistical Inference, Early Computer-Age Methods, and Twenty-First-Century Topics. It is a great book that explains statistical analysis’s algorithmic and inferential aspects.
High-Dimensional Probability: An Introduction With Applications In Data Science
The High-Dimensional Probability: An Introduction with Applications in Data Science by Roman Vershynin book provides meaningful insights into the behavior of random metrics, random vectors, random subspaces, and objects to identify uncertainty in data. This book is excellent in presenting modern tools of high dimensional geometry and probability in an application-oriented manner with many informative exercises.
This book provides an overview of applications in mathematics, statistics, signal processing, optimization, theoretical computer science, and more. The author has integrated theories, essential tools, and modern applications of high dimensional probability in this book.
Probability, Statistics, and Data: A Fresh Approach Using R
Written by Darrin Speegle and Bryan Clair, Probability, Statistics, and Data: A Fresh Approach Using R book provides a fresh approach to calculus-based probability and statistics using R. With this book, you can learn probability through Monte Carlo simulation. In this book, simulation finds answers to difficult probability questions. This book consists of calculus-based mathematical approaches that are connected to experimental computations.
Due to R and simulation in this book, you can get an idea of statistical inference. There are fifty-two datasets included in this book with complementary R package fosdata. Most of these datasets are borrowed from recently published papers to make you work with the current data. In this book, two chapters use powerful tidyverse tools like ggplot2, dplyr, tidyr, and stringr for wrangling data and producing meaningful visualizations.
Statistics 101: From Data Analysis to Predictive Modeling to Measuring Distribution and Determining Probability, Your essential guide to Statistics
Published in December 2018, Statistics 101 by David Borman is a comprehensive guide to statistics that guides readers on collecting, measuring, analyzing, and presenting statistical data. David Borman provides you with the basics of statistics that are very simple to understand and apply in real-life examples.
With Statistics 101, you can learn probability theories and different distribution concepts to identify data patterns and graphs presenting precise findings. The Statistics 101 book is suitable for students looking to improve their statistical skills and also for professionals to understand how statistics works in businesses.
David Borman is a working professional at Deutsche bank, TCM Custom House, Morgan Stanley, Phillip Capital, and Merril Lynch. He is into trading mutual funds, stocks, Commodities, and Derivatives. He has worked with the Risk Management Desk of a Singapore Based Future Commission Merchant.
By Gareth James, Daniela Written, Robert Tibshirani, and Trevor Hastie, An Introduction to Statistical Learning give a feasible overview of statistics with examples and applications. This book covers classification, regression, resampling, support vector machines, clustering, and tree-based methods.
An Introduction to Statistical Learning uses an R programming language to implement statistics concepts. Whether you are a technical person or not, this book helps you to understand different statistical methods to analyze data. Therefore, An Introduction to Statistical Learning is the best book for statistics in data science.
Statistics without Tears: An introduction for non-mathematicians
Statistics without Tears: An introduction for non-mathematicians was published in July 2018. Written by Derek Rowntree, Statistics without Tears is the perfect book for beginners that explains how statistics work with diagrams.
The book consists of simple concepts of statistics, such as dispersion, correlation, and normal distribution, with relevant examples. The author clearly explains the intuitions behind the statistics concepts in simple words. Derek Rowntree has spent most of his working life in education. He was appointed as the founding member of the Open University and helped students to overcome the challenges of open learning and distance education.
Elements of Statistical Engineering: Data Mining, Inference, and Prediction, Second Edition
The Elements of Statistical Engineering by Trevor Hastie, Jerome Friedman, and Robert Tibshirani describes the key ideas in a wide range of fields like finance, medicine, biology, and marketing in a common conceptual framework. The approach in this book is statistical but focuses more on concepts rather than mathematics.
The book contains many examples with liberal use of color graphics and is a valuable resource for many data scientists. It covers many topics, from supervised machine learning to unsupervised learning, including support vector machines, classification methods, neural networks, and more.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors in statistics at Standford University. Authors Hastie and Tibshirani have developed additive models and written popular books about them. Hastie has also co-developed many statistical modeling software and environment in R/S-PLUS.
The week witnessed many new announcements as the world entered 2023 with enthusiasm. The year began with the CES tech event happening in Las Vegas, featuring many recent technological developments. Additionally, there have been many announcements on blockchain-based projects, like Turkey’s blockchain-driven identification initiative for public services. The week also witnessed other updates like NVIDIA’s early access program and Microsoft’s plans to bring ChatGPT-like technology to its search engine Bing.
Here are some Top AI News of The Week [January 6, 2023]:-
Smart AI Stroller by Glüxkind
Canada-based Glüxkind Technologies presented an AI-driven smart stroller Ella at the CES 2023 tech event in Las Vegas. Ella was developed to support new parents on their everyday walks, be more inclusive, and promote family time. Parents and caregivers can take leisurely strolls on any terrain, including inclines, even when they are completely loaded with toys and groceries. Parents can turn on Ella’s clever hands-free strolling when their child wants to take a short stroll outside the stroller.
As per CEO Kevin Huang, with Ella, the company wants to “embolden parents to explore and create their own paths on their parenting journey and be the best parents they can be.”
The Universal AI University: India’s 1st AI University
In close proximity to its campus in Karjat, Mumbai, the Universal Business School has established the first AI university in the country, the Universal AI University. It will offer degree programs in AI and ML, liberal arts and humanities, design, law, sports, management, and a few other fields, making it a top university for artificial intelligence. Numerous tech-focused corporations, including Tata Capital, NSE, L&T, Wipro, Xiaomi, etc., have visited the campus and are willing to assist with collaborations, hiring, and placements.
The announcement was made by Bharat Puri (MD of Pidilite Industries) and Arundhati Bhattacharya (CEO and Chairperson of Salesforce), claiming that the AI university aims to encourage students to get education on AI and promote AI-driven skill development.
PyTorch Gets Compromised with Malicious Dependency
Recently, PyTorch discovered a supply-chain intrusion that could have resulted in people downloading a tainted PyTorch dependency in a malicious attack. The team wrote a warning note informing developers to reinstall the torchtriton package (an internal dependency) as it was uploaded maliciously in the PyPI repository. The team wrote, “This malicious package has the same name, torchtriton but added in code that uploads sensitive data from the machine.”
The guy who is allegedly responsible for this incident said that his activities were a part of an ethical study. He later admitted his wrongdoing and expressed regret to all impacted developers.
Microsoft Plans to Bring ChatGPT-like Technology to Bing
Microsoft plans to release a version of its search engine Bing that uses ChatGPT-powered artificial intelligence (AI) technology in March. For many years, Microsoft and OpenAI have been collaborating on this integration. In 2019, Microsoft engaged in a partnership with OpenAI with a US$1 billion investment. The recent Bing announcement was reported by some internal sources that are yet unnamed, and if it turns out to be accurate, then Bing could result in more human-like answers than other search engines.
NVIDIA Releases Early Access for its Omniverse Avatar Cloud Engine
NVIDIA reveals the Omniverse Avatar Cloud Engine (ACE) early access program. It allows developers and teams to create avatars and virtual assistants to improve business application workflows. The Omniverse ACE is a comprehensive suite of cloud-native services that simplify creating and using intelligent virtual assistants and digital humans at scale. The suite provides AI building blocks required to give these assistants intelligence and animations.
The early access program allows developers to experience pre-release versions of ACE services along with 3D animation AI microservices (for third-party avatars), 2D animation AI microservices (for two-dimensional portraits), and text-to-speech microservices with NVIDIA Riva TTS (for natural speech recognition).
Turkey’s Blockchain-driven Digital Identification for Public Services
Turkey intends to employ blockchain technology for online public services login procedures. Turkish individuals will be verified when logging in to E-Devlet, the digital government site used in the country to access various public services. The announcement was made by Fuat Oktay, Turkey’s Vice President, during the Digital Turkey 2023 event. He said that the new login system will be used within the scope of e-wallet management and will revolutionize e-government efforts in the country.
The country started treading the blockchain path in 2019 and announced multiple blockchain technology projects. However, only a few were successful. With this new project, the country aims at enhancing its global position in adapting blockchain and other related technologies.
Cryptosat Launches “Cryptographically-Equipped” Satellite Crypto2, With the Help of SpaceX
A cryptographically-equipped orbiting satellite, Crypto2, has been launched by Cryptosat onboard SpaceX Falcon 9. Cryptosat is a startup that creates satellites with cryptographic building blocks to beam down to Earth. The startup successfully launched its first satellite, Crypto1, last year in May with the help of SpaceX. With the advent of Crypto1, Cryptosat aimed to increase the usability of blockchain applications by providing a physically impenetrable, tamper-proof platform. Additionally, Cryptosat published an API tutorial to educate developers on communicating with Cryptosat’s low earth orbit (LEO). The tutorial was developed with the help of a satellite simulator using the Cryptoim software.
With Crypto2, the startup is moving forward to expand its satellite network. Compared to Crypto1, Crypto2 has 30 times higher computing power. According to Yonatan Winetraub, Cryptosat’s co-founder, “This launch gives us more availability and more powerful specs to support the growing portfolio of use cases in our development pipeline.”
Natural Language Processing (NLP) is an essential component of artificial intelligence that refers to the computer program that can understand human language. NLP approaches are used in businesses to develop smart assistants, email filters, predictive texts, language translations, digital phone calls, data analysis, and text analysis. Due to such developments, business operations are seamlessly performed with NLP. Therefore, there is a requirement for NLP professionals who can simplify business processes. So if you are looking for a reference for NLP, this article will guide you to top ten mainly used NLP books that are readily available on Amazon.
Natural language processing books
Below is the list of the widely read natural language processing books on Amazon.
Practical Natural Language Processing
The Practical Natural Language Processing book is written by Sowmya Vajjala, Anju Gupta, Bodhisattwa Majumder, and Harshit Surana. This is the best book if you want to build, iterate, and scale NLP systems in your business.
The authors of this book guide you through the entire process of building real-world NLP solutions. This book allows you to learn to adapt NLP solutions for social media, healthcare, and retail industries. It also helps you understand NLP’s wide range of problem statements, tasks, and solution approaches.
After reading this book, you can implement and evaluate different NLP applications using deep learning and machine learning methods and modify your NLP solutions based on your business problems. You can also evaluate various algorithms and approaches for NLP product tasks, datasets, and more.
Written by Steven Bird, Edward Loper, and Ewan Klein, Natural Language Processing with Python book introduces natural language processing with Python language. It will teach you to write Python scripts that work with large unstructured text data.
This book contains examples and exercises that help you extract information from unstructured text and identify named entities. With this book, you can also learn to analyze the texts’ linguistic structure and semantic analysis.
This book allows readers to use popular linguistic datasets like treebanks and WordNet. It guides you in gaining practical skills in NLP and the Natural Language Processing Toolkit (NLTK) open-source library. If you want to develop web applications, analyze multilingual news sources, and are curious about knowing how human language works, you can find this book very useful and exciting.
Released in June 2017, Text Mining in R book by Julia Silge and David Robinson allows you to explore text mining techniques with tidytext. Authors have developed the tidytext package to use the tidy principles behind R packages like ggraph and dplyr. This book will teach you how tidytext and other tidy tools in R make text analysis easier.
The authors of this book teach how treating text as data frames allow you to manipulate, summarize and visualize characteristics of texts. It also guides you in integrating natural language processing into effective workflows. It contains practical code examples and data explorations that help you gain real insights from news, literature, and social media.
By reading this book, you can learn to apply the tidytext package to NLP, use sentiment analysis to detect the emotional content of texts, identify essential terms in documents with frequency measurements, use topic modeling to classify document collections into natural groups, and explore relationships between ggraph and dplyr packages.
Introduction to Natural Language Processing by Jacob Eisenstein offers a technical perspective on natural language processing. This book teaches you methods that understand, manipulate and generate human language. It highlights the contemporary data-driven approaches that focus on techniques from supervised and unsupervised machine learning techniques.
The book’s first section teaches you the foundations of machine learning by building a set of tools used for word-based textual analysis. The second section focuses on structured representations of language that include sequences, trees, and graphs. The third section refers to the different approaches for the representation and analysis of linguistic meaning that range from formal logic to neural word embeddings. Lastly, the book’s final section provides chapter-length treatments of three transformative applications of natural language processing information extraction, text generation, and machine translation.
By Li Deng and Yang Liu, Deep Learning in Natural Language Processing describes the art of deep learning research and its applications to NLP tasks like speech recognition, lexical analysis, knowledge graphs, sentiment analysis, social computing, and parsing.
This book covers all the essential tasks and techniques of natural language processing. It consists of an up-to-date and comprehensive survey of deep learning research and its application in natural language processing.
Any undergraduate, postgraduate or post-doctoral researchers, industrial researchers, lecturers, and someone interested in learning deep learning with natural language processing can read this book.
Neural Network Methods in Natural Language Processing
The Neural Network Methods in Natural Language Processing book by Yoav Goldberg and Graeme Hirst highlights the applications of neural network models to natural language data. The book’s initial section covers the basics of supervised machine learning and feed-forward neural networks. It also covers the basics of machine learning and using vector-based instead of symbolic representation for words.
The book also guides you on the computation-graph abstraction that allows you to easily define and train arbitrary neural networks. It also introduces you to the more specialized neural network architecture that consists of 1D convolutional neural networks, conditioned generation models, recurrent neural networks, and attention-based models. These neural network architectures are the backbone behind the algorithms for syntactic parsing, machine translation, and many other applications.
Other essential concepts like tree-shaped neural networks, prospects of multi-task learning, and structured prediction are also explained in this book.
The Taming Text on Natural language processing by Grant S. Ingersoll, Thomas S. Morton, and Drew Farris is the complete guide to work on unstructured texts in real-world examples. It teaches you to organize texts using approaches like proper name recognition, full-text searches, clustering, tagging, summarization, and information extraction. This book explains all the concepts with examples.
You do not need prior statistics or natural language processing knowledge while reading the Taming Text textbook. The examples in this book are in Java language, but you can apply the concepts in any programming language. The book provides an understanding of open-source libraries like Solr and Mahout. It also teaches you to build text-processing applications.
The Natural Language Processing with Java book by Richard M Reese and Ashish Singh Bhatia teaches various approaches for organizing and extracting useful texts from unstructured data using Java. This book guides on popular Java libraries like CoreNLP, OpenNLP, and Mallet.
In this book, you will learn the basics of NLP and how it helps to identify patterns, company names, unique names, and more in sentences. It also teaches you to perform language analysis with the help of Java libraries to gain insights from the sentences. After reading the book, you can perform tasks such as tokenizations, parts of speech, model training, and parsing trees on sentences.
This book also explains statistical machine translation, dialog systems, summarization, complex searches, and supervised unsupervised NLPs.
Written by Hobson Lane, Hannes Hapke, and Cole Howard, Natural Language Processing in Action guides you in creating machines that understand human language with Python programming language. This book explains traditional NLP approaches, including neural networks, modern deep learning algorithms, and generative techniques that can tackle real-life problems like composing texts, extracting dates, and answering free-form questions.
Reading this book requires a basic understanding of deep learning and intermediate Python skills. With this book, you will learn to work with Python libraries like Keras, TensorFlow, scikit-learn, and gensim. You will also learn about the rule-based, data-based NLPs and their practices.
Written by Nitin Indurkhya and Fred J. Damerau, the Handbook of Natural Language Processing highlights tools and techniques for implementing NLP in computer systems. This book is divided into three sections: the first section consists of surveys of classical techniques that include both symbolic and empirical approaches. The section highlights the statistical approaches to natural language processing. In the final section, every chapter describes the specific application class, from Chinese machine translation to information visualizations, biomedical text mining, and more.
This book focuses on practically implementing natural language processing with tools. It consists of chapters like text preprocessing, lexical analysis, semantic analysis, syntactic analysis, and more.
