Transformers for Natural Language Processing, 2nd Edition
Transformers are a game-changer for natural language understanding (NLU) and have become one of the pillars of artificial intelligence. Transformers for Natural Language Processing, 2nd Edition, investigates deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question-answering, and many more NLP domains with transformers. An Industry 4.0 AI specialist needs to be adaptable; knowing just one NLP platform is not enough anymore. Different platforms have different benefits depending on the application, whether it's cost, flexibility, ease of implementation, results, or performance. In this book, we analyze numerous use cases with Hugging Face, Google Trax, OpenAI, and AllenNLP. This book takes transformers' capabilities further by combining multiple NLP techniques, such as sentiment analysis, named entity recognition, and semantic role labeling, to analyze complex use cases, such as dissecting fake news on Twitter. Also, see how transformers ...
Transformers are a game-changer for natural language understanding (NLU) and have become one of the pillars of artificial intelligence. Transformers for Natural Language Processing, 2nd Edition, investigates deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question-answering, and many more NLP domains with transformers. An Industry 4.0 AI specialist needs to be adaptable; knowing just one NLP platform is not enough anymore. Different platforms have different benefits depending on the application, whether it's cost, flexibility, ease of implementation, results, or performance. In this book, we analyze numerous use cases with Hugging Face, Google Trax, OpenAI, and AllenNLP. This book takes transformers' capabilities further by combining multiple NLP techniques, such as sentiment analysis, named entity recognition, and semantic role labeling, to analyze complex use cases, such as dissecting fake news on Twitter. Also, see how transformers ...
Adaptive Machine Learning Algorithms with Python
Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use. Authors Chanchal Chatterjee and Vwani P. Roychowdhury begin by introducing a common framework for creating adaptive algorithms, and demonstrating how to use it to address various streaming data issues. Examples range from using matrix functions to solve machine learning and data analysis problems to more critical edge computation problems. They handle time-varying, non-stationary data with minimal compute, memory, latency, and bandwidth. Upon finishing this book, you will have a solid understanding of how to solve adaptive machine learning and data analytics problems and be able to derive new algorithms for your own use cases. You will also c ...
Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use. Authors Chanchal Chatterjee and Vwani P. Roychowdhury begin by introducing a common framework for creating adaptive algorithms, and demonstrating how to use it to address various streaming data issues. Examples range from using matrix functions to solve machine learning and data analysis problems to more critical edge computation problems. They handle time-varying, non-stationary data with minimal compute, memory, latency, and bandwidth. Upon finishing this book, you will have a solid understanding of how to solve adaptive machine learning and data analytics problems and be able to derive new algorithms for your own use cases. You will also c ...
Programming for Computations - MATLAB/Octave
This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification. ...
This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification. ...
Open Workbook of Cryptology
Everyone needs a little cryptology. The problem with crypto is that it has a reputation of being very hard and mysterious, as well as very easy to get wrong. While there are aspects of crypto that are connected to quite modern and complex theories - such as number theory, an old and deep branch of mathematics; complexity theory, a new(er) and subtle branch of computer science; and even quantum computation, a quite new wrinkle on a 100 year-old version of physics which is famously counter-intuitive - that are not particularly friendly to the novice, much of the over-all framing of crypto is perfectly easy to comprehend and to use. We contend, further, that this straightforward comprehension of the important basics of cryptology is most easily acquired by actually working with cryptographic primitives, by doing actual coding projects to implement, or use others' implementations of, basic cryptographic ideas. That is the subject of this book. This version uses Python and some ...
Everyone needs a little cryptology. The problem with crypto is that it has a reputation of being very hard and mysterious, as well as very easy to get wrong. While there are aspects of crypto that are connected to quite modern and complex theories - such as number theory, an old and deep branch of mathematics; complexity theory, a new(er) and subtle branch of computer science; and even quantum computation, a quite new wrinkle on a 100 year-old version of physics which is famously counter-intuitive - that are not particularly friendly to the novice, much of the over-all framing of crypto is perfectly easy to comprehend and to use. We contend, further, that this straightforward comprehension of the important basics of cryptology is most easily acquired by actually working with cryptographic primitives, by doing actual coding projects to implement, or use others' implementations of, basic cryptographic ideas. That is the subject of this book. This version uses Python and some ...
Time Series Analysis with Python Cookbook
Time series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting. This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you'll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, A ...
Time series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting. This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you'll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, A ...
Machine Learning for Streaming Data with Python
Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data. You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights. By the end of this book, you will have gained the confidence you need to stream ...
Streaming data is the new top technology to watch out for in the field of data science and machine learning. As business needs become more demanding, many use cases require real-time analysis as well as real-time machine learning. This book will help you to get up to speed with data analytics for streaming data and focus strongly on adapting machine learning and other analytics to the case of streaming data. You will first learn about the architecture for streaming and real-time machine learning. Next, you will look at the state-of-the-art frameworks for streaming data like River. Later chapters will focus on various industrial use cases for streaming data like Online Anomaly Detection and others. As you progress, you will discover various challenges and learn how to mitigate them. In addition to this, you will learn best practices that will help you use streaming data to generate real-time insights. By the end of this book, you will have gained the confidence you need to stream ...
Deep Learning with R, 2nd Edition
Deep Learning with R, 2nd Edition shows you how to put deep learning into action. It's based on the revised new edition of François Chollet's bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks. Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R. Deep Learning with R, 2nd Edition is a hands-on guide to deep learning using the R language. As you move thr ...
Deep Learning with R, 2nd Edition shows you how to put deep learning into action. It's based on the revised new edition of François Chollet's bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks. Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R. Deep Learning with R, 2nd Edition is a hands-on guide to deep learning using the R language. As you move thr ...
Time Series Forecasting in Python
Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. You can predict the future - with a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before. Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you'll learn statistical and deep learning methods for time series forecasting ...
Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. You can predict the future - with a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before. Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you'll learn statistical and deep learning methods for time series forecasting ...
The Recursive Book of Recursion
Recursion has an intimidating reputation: it's considered to be an advanced computer science topic frequently brought up in coding interviews. But there's nothing magical about recursion. The Recursive Book of Recursion uses Python and JavaScript examples to teach the basics of recursion, exposing the ways that it's often poorly taught and clarifying the fundamental principles of all recursive algorithms. You'll learn when to use recursive functions (and, most importantly, when not to use them), how to implement the classic recursive algorithms often brought up in job interviews, and how recursive techniques can help solve countless problems involving tree traversal, combinatorics, and other tricky topics. Al Sweigart has built a career explaining programming concepts in a fun, approachable manner. If you've shied away from learning recursion but want to add this technique to your programming toolkit, or if you're racing to prepare for your next job interview, this book is for y ...
Recursion has an intimidating reputation: it's considered to be an advanced computer science topic frequently brought up in coding interviews. But there's nothing magical about recursion. The Recursive Book of Recursion uses Python and JavaScript examples to teach the basics of recursion, exposing the ways that it's often poorly taught and clarifying the fundamental principles of all recursive algorithms. You'll learn when to use recursive functions (and, most importantly, when not to use them), how to implement the classic recursive algorithms often brought up in job interviews, and how recursive techniques can help solve countless problems involving tree traversal, combinatorics, and other tricky topics. Al Sweigart has built a career explaining programming concepts in a fun, approachable manner. If you've shied away from learning recursion but want to add this technique to your programming toolkit, or if you're racing to prepare for your next job interview, this book is for y ...
Python for Geospatial Data Analysis
In spatial data science, things in closer proximity to one another likely have more in common than things that are farther apart. With this practical book, geospatial professionals, data scientists, business analysts, geographers, geologists, and others familiar with data analysis and visualization will learn the fundamentals of spatial data analysis to gain a deeper understanding of their data questions. Author Bonny P. McClain demonstrates why detecting and quantifying patterns in geospatial data is vital. Both proprietary and open source platforms allow you to process and visualize spatial information. This book is for people familiar with data analysis or visualization who are eager to explore geospatial integration with Python. ...
In spatial data science, things in closer proximity to one another likely have more in common than things that are farther apart. With this practical book, geospatial professionals, data scientists, business analysts, geographers, geologists, and others familiar with data analysis and visualization will learn the fundamentals of spatial data analysis to gain a deeper understanding of their data questions. Author Bonny P. McClain demonstrates why detecting and quantifying patterns in geospatial data is vital. Both proprietary and open source platforms allow you to process and visualize spatial information. This book is for people familiar with data analysis or visualization who are eager to explore geospatial integration with Python. ...