Angebote zu "Python" (10 Treffer)

Deep Learning for NLP and Speech Recognition
87,99 € *
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This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.

Anbieter: buecher.de
Stand: 19.06.2019
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Text Analytics with Python
29,99 € *
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Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird´s eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. What You Will Learn: Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern Who This Book Is For : IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data

Anbieter: buecher.de
Stand: 19.06.2019
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Text Analytics with Python, Second Edition
24,99 € *
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Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. You´ll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well. Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques. There is also a chapter dedicated to semantic analysis where you´ll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release. What You´ll Learn - Understand NLP and text syntax, semantics and structure - Discover text cleaning and feature engineering - Review text classification and text clustering - Assess text summarization and topic models - Study deep learning for NLP Who This Book Is For IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.

Anbieter: buecher.de
Stand: 19.06.2019
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Applied Natural Language Processing with Python
21,99 € *
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Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment. What You Will Learn Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim Manipulate and preprocess raw text data in formats such as .txt and .pdf Strengthen your skills in data science by learning both the theory and the application of various algorithms Who This Book Is For You should be at least a beginner in ML to get the most out of this text, but you needn´t feel that you need be an expert to understand the content.

Anbieter: buecher.de
Stand: 19.06.2019
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Advanced Data Analytics Using Python als Buch v...
37,49 € *
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Advanced Data Analytics Using Python:With Machine Learning, Deep Learning and NLP Examples. 1st ed Sayan Mukhopadhyay

Anbieter: Hugendubel.de
Stand: 20.06.2019
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Advanced Data Analytics Using Python als eBook ...
36,99 € *
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(36,99 € / in stock)

Advanced Data Analytics Using Python:With Machine Learning, Deep Learning and NLP Examples. 1st ed Sayan Mukhopadhyay

Anbieter: Hugendubel.de
Stand: 04.06.2019
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Hands-On Natural Language Processing with Pytho...
35,99 € *
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(35,99 € / in stock)

Hands-On Natural Language Processing with Python:A practical guide to applying deep learning architectures to your NLP applications Arumugam Rajesh Arumugam, Shanmugamani Rajalingappaa Shanmugamani

Anbieter: Hugendubel.de
Stand: 04.06.2019
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Natural Language Processing with Python: Natura...
9,95 € *
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This audiobook is a perfect beginner´s guide to natural language processing. It is offering an easy-to-understand guide to implementing NLP techniques using Python. Natural language processing has been around for more than 50 years, but just recently, with greater amounts of data present and better computational powers, it has gained a greater popularity.Given the importance of data, there is no wonder why natural language processing is on the rise. If you are interested in learning more, this audiobook will serve as your best companion on this journey introducing you to this challenging yet extremely engaging world of automatic manipulation of our human language.It covers all the basics you need to know before you dive deeper into NLP and solving more complex NLP tasks in Python.Here is a preview of what you´ll learn here....The main challenges of natural language processingThe history of natural language processingHow natural langauge processing actually worksThe main natural language processing applicationsText preprocessing and noise removal Feature engineering and syntactic parsingPart of speech tagging and named entity extractionTopic modeling and word embedding Text classification problems Working with text data using NLTK Text summarization and sentiment analysis And much, much more....PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio. 1. Language: English. Narrator: Jon Wilkins. Audio sample: http://samples.audible.de/bk/acx0/112216/bk_acx0_112216_sample.mp3. Digital audiobook in aax.

Anbieter: Audible
Stand: 18.04.2019
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Deep Learning for Natural Language Processing
25,99 € *
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Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory networks, and sequence-2-sequence models. You´ll start by covering the mathematical prerequisites and the fundamentals of deep learning and NLP with practical examples. The first three chapters of the book cover the basics of NLP, starting with word-vector representation before moving onto advanced algorithms. The final chapters focus entirely on implementation, and deal with sophisticated architectures such as RNN, LSTM, and Seq2seq, using Python tools: TensorFlow, and Keras. Deep Learning for Natural Language Processing follows a progressive approach and combines all the knowledge you have gained to build a question-answer chatbot system. This book is a good starting point for people who want to get started in deep learning for NLP. All the code presented in the book will be available in the form of IPython notebooks and scripts, which allow you to try out the examples and extend them in interesting ways. What You Will Learn Gain the fundamentals of deep learning and its mathematical prerequisites Discover deep learning frameworks in Python Develop a chatbot Implement a research paper on sentiment classification Who This Book Is For Software developers who are curious to try out deep learning with NLP.

Anbieter: buecher.de
Stand: 19.06.2019
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