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.
Build software that combines Python?s expressivity with the performance and control of C (and C++). It?s possible with Cython, the compiler and hybrid programming language used by foundational packages such as NumPy, and prominent in projects including Pandas, h5py, and scikits-learn. In this practical guide, you?ll learn how to use Cython to improve Python?s performance?up to 3000x? and to wrap C and C++ libraries in Python with ease. Author Kurt Smith takes you through Cython?s capabilities, with sample code and in-depth practice exercises. If you?re just starting with Cython, or want to go deeper, you?ll learn how this language is an essential part of any performance-oriented Python programmer?s arsenal. * Use Cython?s static typing to speed up Python code * Gain hands-on experience using Cython features to boost your numeric-heavy Python * Create new types with Cython?and see how fast object-oriented programming in Python can be * Effectively organize Cython code into separate modules and packages without sacrificing performance * Use Cython to give Pythonic interfaces to C and C++ libraries * Optimize code with Cython?s runtime and compile-time profiling tools * Use Cython?s prange function to parallelize loops transparently with OpenMP
Programming Massively Parallel Processors: A Hands-on Approach, Third Edition shows both student and professional alike the basic concepts of parallel programming and GPU architecture, exploring, in detail, various techniques for constructing parallel programs. Case studies demonstrate the development process, detailing computational thinking and ending with effective and efficient parallel programs. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in-depth. For this new edition, the authors have updated their coverage of CUDA, including coverage of newer libraries, such as CuDNN, moved content that has become less important to appendices, added two new chapters on parallel patterns, and updated case studies to reflect current industry practices. Teaches computational thinking and problem-solving techniques that facilitate high-performance parallel computing Utilizes CUDA version 7.5, NVIDIA´s software development tool created specifically for massively parallel environments Contains new and updated case studies Includes coverage of newer libraries, such as CuDNN for Deep Learning
Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business challenges. This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark. Machine Learning with PySpark shows you how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You´ll also see unsupervised machine learning models such as K-means and hierarchical clustering. A major portion of the book focuses on feature engineering to create useful features with PySpark to train the machine learning models. The natural language processing section covers text processing, text mining, and embedding for classification. After reading this book, you will understand how to use PySpark´s machine learning library to build and train various machine learning models. Additionally you´ll become comfortable with related PySpark components, such as data ingestion, data processing, and data analysis, that you can use to develop data-driven intelligent applications. What You Will Learn Build a spectrum of supervised and unsupervised machine learning algorithms Implement machine learning algorithms with Spark MLlib libraries Develop a recommender system with Spark MLlib libraries Handle issues related to feature engineering, class balance, bias and variance, and cross validation for building an optimal fit model Who This Book Is For Data science and machine learning professionals.
Real agilists don´t weigh themselves down with libraries of books, they keep their important information handy with them at all times. Jeff and Tim pack over two decades of experience coaching and doing agile into Agile in a Flash, a unique deck of index cards that fit neatly in your pocket and tack easily onto the wall. Agile in a Flash cards run the gamut of agile, covering customer, planning, team, and developer concepts to help you succeed on agile projects. You can use cards from the deck in many ways: as references, reminders, teaching tools, and conversation pieces. Why not get sets for your entire team or organization? This comprehensive set of cards is an indispensable resource for agile teams. The deck of Agile in a Flash cards teaches leadership, teamwork, clean programming, agile approaches to problem solving, and tips for coaching agile teams. Team members can use the cards as reference material, ice breakers for conversations, reminders (taped to a wall or monitor), and sources of useful tips and hard-won wisdom. The cards are: Bite-sized! Read one practice or aspect at a time in a couple of minutes. Smart! Each card has years of practical experience behind it. Portable! Cards fit easily in your pocket or backpack. An indispensable tool for any agile team, and a must-have for every agile coach or Scrum Master. The Agile in a Flash deck is broken into four areas: planning, team, coding, and agile concepts. The front of each card is a quick list - a summary of the things you want to know and remember. The back provides further detail on each of the bullet points, and offers sage nuggets of knowledge based on extensive professional experience. Tape the cards to your wall, stick them on your monitor, and get agile fast.
Deep learning is one of today´s hottest fields. This approach to machine learning is achieving breakthrough results in some of today´s highest profile applications, in organizations ranging from Google to Tesla, Facebook to Apple. Thousands of technical professionals and students want to start leveraging its power, but previous books on deep learning have often been non-intuitive, inaccessible, and dry. In Deep Learning Illustrated, three world-class instructors and practitioners present a uniquely visual, intuitive, and accessible high-level introduction to the techniques and applications of deep learning. Packed with vibrant, full-color illustrations, it abstracts away much of the complexity of building deep learning models, making the field more fun to learn and accessible to a far wider audience. Part I´s high-level overview explains what Deep Learning is, why it has become so ubiquitous, and how it relates to concepts and terminology such as Artificial Intelligence, Machine Learning, Artificial Neural Networks, and Reinforcement Learning. These opening chapters are replete with vivid illustrations, easy-to-grasp analogies, and character-focused narratives. Building on this foundation, the authors then offer a practical reference and tutorial for applying a wide spectrum of proven deep learning techniques. Essential theory is covered with as little mathematics as possible and is illuminated with hands-on Python code. Theory is supported with practical ´´run-throughs´´ available in accompanying Jupyter notebooks, delivering a pragmatic understanding of all major deep learning approaches and their applications: machine vision, natural language processing, image generation, and videogaming. To help readers accomplish more in less time, the authors feature several of today´s most widely used and innovative deep learning libraries, including TensorFlow and its high-level API, Keras; PyTorch; and the recently released, high-level Coach, a TensorFlow API that abstracts away the complexity typically associated with building Deep Reinforcement Learning algorithms. * Ideal for software developers, data scientists, and analysts at all levels of experience * Teaches through simple visuals, accessible Python code examples, character-driven narratives, and intuitive analogies * Covers today´s leading applications, including machine vision, natural language processing, image generation, and videogames * Introduces four powerful Deep Learning libraries: TensorFlow, Keras, PyTorch, and Coach * Carefully designed to minimize mathematical formulae and avoid unnecessary complexity The first full-color, illustrated, hands-on guide to the fundamentals of modern, deep-learning AI: simply the most intuitive, practical way to get started * Ideal for software developers, data scientists, and analysts at all levels of experience * Teaches through simple visuals, accessible Python code examples, character-driven narratives, and intuitive analogies * Covers today´s leading applications, including machine vision, natural language processing, image generation, and videogames * Introduces four powerful Deep Learning libraries: TensorFlow, Keras, PyTorch, and Coach * Carefully designed to minimize mathematical formulae and avoid unnecessary complexity
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.