Found insideStyle and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn ... Found insideAuthor Allen Downey explains techniques such as spectral decomposition, filtering, convolution, and the Fast Fourier Transform. This book also provides exercises and code examples to help you understand the material. Found insideThis book is about making machine learning models and their decisions interpretable. After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Found insideWHAT WILL YOU LEARN Raspberry Pi, Python 3 Basics Scientific Python Ecosystem NumPy and Matplotlib Visualization withÊMatplotlib ÊBasic NumPy, Advanced Image Processing with NumPy andÊMatplotlib Getting started with scikit-image ... We hope this little book will go some way towards meeting this need. Many newcomers to Prolog find that the task of writing a Prolog program Is not like specifying an algorithm in the same way as In a conventional programming language. Data Mining and Data Visualization focuses on dealing with large-scale data, a field commonly referred to as data mining. The book is divided into three sections. Foundations of Artificial Intelligence critically evaluates the fundamental assumptions underpinning the dominant approaches to AI. In the 11 contributions, theorists historically associated with each position identify the basic tenets of ... This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning. This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets. Found insideThis volume focuses on Big Data Analytics. The contents of this book will be useful to researchers and students alike. This volume comprises the select proceedings of the annual convention of the Computer Society of India. Found insideThis volume focuses on natural language processing, artificial intelligence, and allied areas. Found insideWith this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas ... Found inside – Page 1With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data ... The Long Short-Term Memory network, or LSTM for short, is a type of recurrent neural network that achieves state-of-the-art results on challenging prediction problems. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Found insideLearn about what AI has contributed to society Explore uses for AI in computer applications Discover the limits of what AI can do Find out about the history of AI The world of AI is fascinating—and this hands-on guide makes it more ... An Introduction to Conditional Random Fields provides a comprehensive tutorial aimed at application-oriented practitioners seeking to apply CRFs. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. Found inside – Page 218Available at https://www.geeksforgeeks.org/integration-of-blockchain-andai/#:~: ... Available at https://www.guru99.com/blockchain-tutorial.html Hacioglu. Found inside – Page 1The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Scientific knowledge grows at a phenomenal pace--but few books have had as lasting an impact or played as important a role in our modern world as The Mathematical Theory of Communication, published originally as a paper on communication ... This volume brings together some of this recent work in a manner designed to be accessible to students and professionals interested in these new insights and developments. Authorship Attribution surveys the history and present state of the discipline, presenting some comparative results where available. Found inside – Page 30924(12), 1565–1567 (2006) 13. https://www.machinelearningplus.com/nlp/topic ... June 2020 24. https://www.geeksforgeeks.org/removing-stop-words-nltk-python/. This book addresses theoretical or applied work in the field of natural language processing. Found inside – Page 1This Book Is Perfect For Total beginners with zero programming experience Junior developers who know one or two languages Returning professionals who haven’t written code in years Seasoned professionals looking for a fast, simple, crash ... Found insideThis is an excellent, up-to-date and easy-to-use text on data structures and algorithms that is intended for undergraduates in computer science and information science. Found insideUsing clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, ... This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. Found insideLeverage the power of machine learning and deep learning to extract information from text data About This Book Implement Machine Learning and Deep Learning techniques for efficient natural language processing Get started with NLTK and ... This tutorial introduces the reader informally to the basic concepts and features of the python language and system. This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. Found inside – Page 1In this book, you'll learn how ANTLR automatically builds a data structure representing the input (parse tree) and generates code that can walk the tree (visitor). Found insideThe need for a systematic and methodological development of visual analytics was detected. This book aims at addressing this need. Found insideUsing clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how ... Found insideProbability is the bedrock of machine learning. Found insideXGBoost is the dominant technique for predictive modeling on regular data. You must understand the algorithms to get good (and be recognized as being good) at machine learning. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures. Found insideUsing clear explanations, simple pure Python code (no libraries!) and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement a suite of linear, nonlinear and ensemble machine learning ... 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