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.
Are you looking to take back control of your life? Fed up of being controlled by certain people, often leaving you feeling defenceless? Finally come to that point in life where you really want to do something about it, but just not sure how?If this sounds like you, then don’t worry; you’re not alone.You see, Dark Psychology is used to mentally and emotionally manipulate us in our everyday lives. But what many people don’t even realize is that we actually leave ourselves exposed and vulnerable to attack without even realizing.But here’s why.The problem is we just simply lack the recognition of what signs to look out for and how to handle certain situations and types of people.But don’t worry. Because In The Unknown Science of Dark Psychology, you will learn how to out-manipulate and overcome the worst of control freaks without becoming a monster yourself.Here’s just a small fraction of what you’ll discover inside:The three Dark traits you may have, and how to identify themProven techniques and methods you can use to out manipulate a manipulatorThe six absolute essential techniques you must know when it comes to mastering persuasion (and why you’ll fail if you don’t use them)Five ways to tell if your mind is being controlled, even if you don’t realise.The exact five steps that are used to brainwash people (and how to avoid becoming a victim of them)How you can effectively spot a liarLearn how to hypnotize someone in just four steps, (and why others will always fail at hypnotism)And much, much more!So, if you want to learn the exact same methods people are using to manipulate and get what they want in life, why not scroll up and click ´´Buy Now”. 1. Language: English. Narrator: Steve Ferrari. Audio sample: http://samples.audible.de/bk/acx0/153651/bk_acx0_153651_sample.mp3. Digital audiobook in aax.
Artificial intelligence and machine learning are one area of life that will continue to interest and surprise us with new topics, products, innovations, and new ideas. What was considered at a certain time as dumb machines have become smarter to the point where people can communicate with them on a human level.Two audiobooks in one:Machine Learning and Artificial IntelligenceSo, what will you learn from this audiobook? The key topics that we are going to cover in the following chapters are:What machine learning and artificial intelligence are.How to use artificial intelligence to improve business processes and the bottom line.Types of machine learningThe concept of neural networksMachine learning modelsMachine learning and roboticsAlgorithmsUsing the probability and statistics to help with machine learningThe building blocks needed for machine learningMachine learning and the internet of thingsBusiness processes with artificial intelligenceRobots and Artificial intelligenceThe world of computing and artificial intelligenceSelf-driving carsArtificial intelligence and the job marketReasons why industry experts are warning us about Artificial intelligenceArtificial intelligence in trading and financial investingReinforcement learningOur daily lives with Artificial intelligenceArtificial intelligence and decision making machineArtificial intelligence and creativityHow advanced forms of machine learning are applied to drive artificial intelligence applications such as object recognition and language translation.The current limitations of machine learning and artificial intelligence.This is not the end of AI; there is still more to learn from AI. Who knows what AI can perform for us in the future? Maybe it will be a 1. Language: English. Narrator: Michael Reece. Audio sample: http://samples.audible.de/bk/acx0/148781/bk_acx0_148781_sample.mp3. Digital audiobook in aax.
In recent years, deep learning has fundamentally changed the landscapes of a number of areas in artificial intelligence, including speech, vision, natural language, robotics, and game playing. In particular, the striking success of deep learning in a wide variety of natural language processing (NLP) applications has served as a benchmark for the advances in one of the most important tasks in artificial intelligence. This book reviews the state of the art of deep learning research and its successful applications to major NLP tasks, including speech recognition and understanding, dialogue systems, lexical analysis, parsing, knowledge graphs, machine translation, question answering, sentiment analysis, social computing, and natural language generation from images. Outlining and analyzing various research frontiers of NLP in the deep learning era, it features self-contained, comprehensive chapters written by leading researchers in the field. A glossary of technical terms and commonly used acronyms in the intersection of deep learning and NLP is also provided. The book appeals to advanced undergraduate and graduate students, post-doctoral researchers, lecturers and industrial researchers, as well as anyone interested in deep learning and natural language processing.
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.
Power your C# and .NET applications with exciting machine learning models and modular projectsKey FeaturesProduce classification, regression, association, and clustering modelsExpand your understanding of machine learning and C# Get to grips with C# packages such as Accord.net, LiveCharts, and DeedleBook DescriptionMachine learning is applied in almost all kinds of real-world surroundings and industries, right from medicine to advertising; from finance to scientifc research. This book will help you learn how to choose a model for your problem, how to evaluate the performance of your models, and how you can use C# to build machine learning models for your future projects. You will get an overview of the machine learning systems and how you, as a C# and .NET developer, can apply your existing knowledge to the wide gamut of intelligent applications, all through a project-based approach. You will start by setting up your C# environment for machine learning with the required packages, Accord.NET, LiveCharts, and Deedle. We will then take you right from building classifcation models for spam email fltering and applying NLP techniques to Twitter sentiment analysis, to time-series and regression analysis for forecasting foreign exchange rates and house prices, as well as drawing insights on customer segments in e-commerce. You will then build a recommendation model for music genre recommendation and an image recognition model for handwritten digits. Lastly, you will learn how to detect anomalies in network and credit card transaction data for cyber attack and credit card fraud detections.By the end of this book, you will be putting your skills in practice and implementing your machine learning knowledge in real projects.What you will learnSet up the C# environment for machine learning with required packagesBuild classification models for spam email filteringGet to grips with feature engineering using NLP techniques for Twitter sentiment analysisForecast foreign exchange rates using continuous and time-series dataMake a recommendation model for music genre recommendationFamiliarize yourself with munging image data and Neural Network models for handwritten-digit recognitionUse Principal Component Analysis (PCA) for cyber attack detectionOne-Class Support Vector Machine for credit card fraud detectionWho this book is forIf you´re a C# or .NET developer with good knowledge of C#, then this book is perfect for you to get Machine Learning into your projects and make smarter applications.