James Whitaker – författare

The explosive love story and full details behind Diana and Charles''s relationship have never been revealed ... until now. Their breakup sent shock waves around the world, as growing suspicions were officially confirmed: Their fairy-tale marriage was just that - a fairy tale. Not only was the lily-white veil stripped away from their private lives, but the British monarchy itself was suddenly under scathing scrutiny. After all the headlines, the whispers, the rumors, the damaging claims and counterclaims by intimate friends of each, the full inside story of Diana, Charles, and their doomed union is now revealed - by the one person uniquely qualified to tell the lurid and fascinating tale of royal romance, rivalry, and scandal.James Whitaker is famous in England as the authority on the Royal Family - the only journalist to have had access to all the members of the House of Windsor for more than two decades of exclusive reportage. At the same time, he knew the teenage Lady Diana Spencer long before the royal wedding bells.With vivid personality profiles of an awe-inspiring gallery of characters, with unblushing and authentic depictions of how the Royals live and love, with utter honesty about both the monarchy and the girl now exiled from its charmed circle, Diana vs. Charles offers the most staggering, far-reaching disclosures about the greatest personal crisis ever to shake the Royal Family.

A Message to My Black People: Stop Being the Nigga in America, first of all, is not meant to anger or disparage anyone. I wrote this book to help my people understand that we can do things a whole lot better as a people in order to earn and gain the respect as a people that we feel we deserve. Our slave ancestors endured too much for too long for us not to respect and reflect upon that and to make ourselves better. Our people and others devoted to the cause during the civil rights era fought tirelessly to earn equal rights and respect for our people for that to be disregarded. I do not want our people to take certain rights and privileges for granted and not understand the full purpose behind them. What I say in this book is how a lot of our people feel but are probably afraid to say, especially in a public forum. A lot of us may fall into certain categories mentioned in the book or know someone who does. The goal is not to insult anyone but to bring to light that he or she or they can do things better to uplift our people and help remove all the negative stereotypes placed upon us by other races. Please take the time and read each section thoroughly to get my full message. If you agree with my points, great. If not, try to really evaluate why and see if there is anything you can do to at least understand where I am coming from when I mention the things that I do. It is time for us as a people to take responsibility of our actions and better ourselves, improve our image, and stop being the scapegoat for problems in America. It is time to stop being the nigga in America!

Abigail the Alien is about a girl on a distant planet that loves doing her favorite things. This book is designed to aid in the learning of the alphabet and the improvement of memorization skills. The book focuses on Abigail doing her favorite things and encourages the reader to engage the child in the learning process. When reading this book, show the child the letter of the alphabet and continue reading the run-on style to help with memorizing the things Abigail likes. Soon, the child will start to recognize the letters and associate them with everyday things found around the house. Encourage the child to remember Abigail's favorite things in order to learn the order of the alphabet. After the order is established, try to replace the things in the book with your child's favorite things.

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 IntroductionThe 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 BasicsThe 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 SpeechThe 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.

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. 

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 IntroductionThe 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 BasicsThe 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 SpeechThe 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.