What You'll Learn Understand machine learning development and frameworks Assess model diagnosis and tuning in machine learning Examine text mining, natuarl language processing (NLP), and recommender systems Review reinforcement learning and ... Found inside – Page 536English Wizard – Dictionary Administrator's Guide . ... Retrieved from NLP APIs : https://tedboy.github.io/nlps/generated/generated/gensim.summarization. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and ... Found insideThis book constitutes the proceedings of the 15th International Conference on Distributed Computing and Internet Technology, ICDCIT 2019, held in Bhubaneswar, India, in January 2019. Found inside – Page 1Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. 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 insideLearn to build powerful machine learning models quickly and deploy large-scale predictive applications About This Book Design, engineer and deploy scalable machine learning solutions with the power of Python Take command of Hadoop and Spark ... Found insideThis book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems. By the end of the book, you'll be creating your own NLP applications with Python and spaCy. Found insideNeural networks are a family of powerful machine learning models and this book focuses on their application to natural language data. The book introduces neural networks with TensorFlow, runs through the main applications, covers two working example apps, and then dives into TF and cloudin production, TF mobile, and using TensorFlow with AutoML. Found insideBuild your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. Found insideYour Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. Found insideIn this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. This book provides system developers and researchers in natural language processing and computational linguistics with the necessary background information for working with the Arabic language. You can easily jump to or skip particular topics in the book. You also will have access to Jupyter notebooks and code repositories for complete versions of the code covered in the book. NLP in Python is among the most sought-after skills among data scientists. With code and relevant case studies, this book will show how you can use industry grade tools to implement NLP programs capable of learning from relevant data. Found inside – Page ivThis book provides a complete and modern guide to web scraping, using Python as the programming language, without glossing over important details or best practices. Found insideEach chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you’re stuck. This book starts by identifying the business processes in the banking and insurance industry. This involves data collection from sources such as conversations from customer service centers, online chats, emails, and other NLP sources. Found insideThe book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. This book has numerous coding exercises that will help you to quickly deploy natural language processing techniques, such as text classification, parts of speech identification, topic modeling, text summarization, text generation, entity ... This book is intended for Python programmers interested in learning how to do natural language processing. Found insideThis book contains extended versions of selected papers from the workshop. Found inside – Page iBuild your own chatbot using Python and open source tools. This book begins with an introduction to chatbots where you will gain vital information on their architecture. Arabic Computational Linguistics documents the recent work of researchers in both academia and industry who have taken up the challenge of solving the real-life problems posed by an understudied language. Found inside – Page 105To get the required data, I would recommend that you clone the Gensim repository from GitHub. In the code examples shown here, we will be taking a look at ... Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. This first collection of selected articles from researchers in automatic analysis, storage, and use of terminology, and specialists in applied linguistics, computational linguistics, information retrieval, and artificial intelligence offers ... Found insideAcquire and analyze data from all corners of the social web with Python About This Book Make sense of highly unstructured social media data with the help of the insightful use cases provided in this guide Use this easy-to-follow, step-by ... Found inside – Page iThe second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. Found inside – Page iUsing a problem-solution approach, this book makes deep learning and machine learning accessible to everyday developers, by providing a combination of tools such as cognitive services APIs, machine learning platforms, and libraries. Found inside – Page iiThis book: Provides complete coverage of the major concepts and techniques of natural language processing (NLP) and text analytics Includes practical real-world examples of techniques for implementation, such as building a text ... Found inside – Page 131For Gensim, we used the word2vec word embedding technique based on a pre-trained ... with an average score of 94.21% across the 50 GitHub projects sampled. Endorsed by top AI authors, academics and industry leaders, The Hundred-Page Machine Learning Book is the number one bestseller on Amazon and the most recommended book for starters and experienced professionals alike. Found inside – Page 163[1] Gensim Dictionary class: ... [5] Recipes and FAQ: https://github.com/RaRe-Technologies/gensim/wiki/Recipes-&-FAQ [6] Term Topics Jupyter notebook: ... Found inside – Page 377The resulting dictionary was caped at |W| = 100000 most frequent words. Both Gensim and VW.LDA represents the resulting topic model as Dirichlet ... 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 inside – Page iWho This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Johannes Hellrich investigated this problem both empirically and theoretically and found some variants of SVD-based algorithms to be unaffected. Found inside – Page iIn the course of telling these stories, Scott touches on a wide variety of subjects: public disorder and riots, desertion, poaching, vernacular knowledge, assembly-line production, globalization, the petty bourgeoisie, school testing, ... Found inside – Page 213... id2word=dictionary, num_topics=4, passes=20) To save the resulting model to disk, use its save() function, like this: lda.save('lkml.gensim') You can ... If you are a Scala, Java, or Python developer with an interest in machine learning and data analysis and are eager to learn how to apply common machine learning techniques at scale using the Spark framework, this is the book for you. Found inside – Page iThis book is a good starting point for people who want to get started in deep learning for NLP. Found inside – Page iBridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. 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 ... Found insideBecome an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data ... Robert Graham is the editor of Anarchism: A Documentary History of Libertarian Ideas, Volume One: From Anarchy to Anarchism (300CE to 1939). The MATLAB toolkit available online, 'MATCOM', contains implementations of the major algorithms in the book and will enable students to study different algorithms for the same problem, comparing efficiency, stability, and accuracy. 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