Take a problem-solving approach to learning all about transformers and get up and running in no time by implementing methodologies that will build the future of NLP Key Features • Explore quick prototyping with up-to-date Python libraries to create effective solutions to industrial problems • Solve advanced NLP problems such as named-entity recognition, information extraction, language generation, and conversational AI • Monitor your model's performance with the help of BertViz, exBERT, and TensorBoard Book Description Transformer-based language models have dominated natural language processing (NLP) studies and have now become a new paradigm. With this book, you'll learn how to build various transformer-based NLP applications using the Python Transformers library. The book gives you an introduction to Transformers by showing you how to write your first hello-world program. You'll then learn how a tokenizer works and how to train your own tokenizer. As you advance, you'll explore the architecture of autoencoding models, such as BERT, and autoregressive models, such as GPT. You'll see how to train and fine-tune models for a variety of natural language understanding (NLU) and natural language generation (NLG) problems, including text classification, token classification, and text representation. This book also helps you to learn efficient models for challenging problems, such as long-context NLP tasks with limited computational capacity. You'll also work with multilingual and cross-lingual problems, optimize models by monitoring their performance, and discover how to deconstruct these models for interpretability and explainability. Finally, you'll be able to deploy your transformer models in a production environment. By the end of this NLP book, you'll have learned how to use Transformers to solve advanced NLP problems using advanced models. What you will learn • Explore state-of-the-art NLP solutions with the Transformers library • Train a language model in any language with any transformer architecture • Fine-tune a pre-trained language model to perform several downstream tasks • Select the right framework for the training, evaluation, and production of an end-to-end solution • Get hands-on experience in using TensorBoard and Weights & Biases • Visualize the internal representation of transformer models for interpretability Who this book is for This book is for deep learning researchers, hands-on NLP practitioners, as well as ML/NLP educators and students who want to start their journey with Transformers. Beginner-level machine learning knowledge and a good command of Python will help you get the best out of this book. Table of Contents • From Bag-of-Words to the Transformers • A Hands-On Introduction to the Subject • Autoencoding Language Models • Autoregressive and Other Language Models • Fine-Tuning Language Models for Text Classification • Fine-Tuning Language Models for Token Classification • Text Representation • Working with Efficient Transformers • Cross-Lingual and Multilingual Language Modeling • Serving Transformer Models • Attention Visualization and Experiment Tracking Review "Transformers rule for a lot of NLP tasks now, and this is a great book about them. Beginners will appreciate clear explanations and experienced programmers have plenty of examples how to use Transformers even for complex tasks. Code examples are well selected and I did like that they use both Tensorflow and PyTorch." -- Andrzej Jankowski, AI Sales Engineer at Intel and Business AI Postgraduate Course Leader at Kozminski University
Build end-to-end industrial-strength NLP models using advanced morphological and syntactic features in spaCy to create real-world applications with ease Key FeaturesGain an overview of what spaCy offers for natural language processingLearn details of spaCy's features and how to use them effectivelyWork through practical recipes using spaCyBook Description spaCy is an industrial-grade, efficient NLP Python library. It offers various pre-trained models and ready-to-use features. Mastering spaCy provides you with end-to-end coverage of spaCy's features and real-world applications. You'll begin by installing spaCy and downloading models, before progressing to spaCy's features and prototyping real-world NLP apps. Next, you'll get familiar with visualizing with spaCy's popular visualizer displaCy. The book also equips you with practical illustrations for pattern matching and helps you advance into the world of semantics with word vectors. Statistical information extraction methods are also explained in detail. Later, you'll cover an interactive business case study that shows you how to combine all spaCy features for creating a real-world NLP pipeline. You'll implement ML models such as sentiment analysis, intent recognition, and context resolution. The book further focuses on classification with popular frameworks such as TensorFlow's Keras API together with spaCy. You'll cover popular topics, including intent classification and sentiment analysis, and use them on popular datasets and interpret the classification results. By the end of this book, you'll be able to confidently use spaCy, including its linguistic features, word vectors, and classifiers, to create your own NLP apps. What you will learnInstall spaCy, get started easily, and write your first Python scriptUnderstand core linguistic operations of spaCyDiscover how to combine rule-based components with spaCy statistical modelsBecome well-versed with named entity and keyword extractionBuild your own ML pipelines using spaCyApply all the knowledge you've gained to design a chatbot using spaCyWho this book is for This book is for data scientists and machine learners who want to excel in NLP as well as NLP developers who want to master spaCy and build applications with it. Language and speech professionals who want to get hands-on with Python and spaCy and software developers who want to quickly prototype applications with spaCy will also find this book helpful. Beginner-level knowledge of the Python programming language is required to get the most out of this book. A beginner-level understanding of linguistics such as parsing, POS tags, and semantic similarity will also be useful.
Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key FeaturesUnderstand how to use PyTorch 1.x to build advanced neural network modelsLearn to perform a wide range of tasks by implementing deep learning algorithms and techniquesGain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much moreBook Description Deep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models. What you will learnImplement text and music generating models using PyTorchBuild a deep Q-network (DQN) model in PyTorchExport universal PyTorch models using Open Neural Network Exchange (ONNX)Become well-versed with rapid prototyping using PyTorch with fast.aiPerform neural architecture search effectively using AutoMLEasily interpret machine learning (ML) models written in PyTorch using CaptumDesign ResNets, LSTMs, Transformers, and more using PyTorchFind out how to use PyTorch for distributed training using the torch.distributed APIWho this book is for This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.
Major Label Mastering: Professional Mastering Process distills 25 years of mastering experience at Capitol Records into practical understandings and reliable systems. Containing unparalleled insights, this book reveals the mastering tricks and techniques used by Evren Göknar at one of the world’s most notable record labels. Beginning with the requisite competencies every Mastering Engineer must develop, Major Label Mastering delves into the particulars of the mastering studio, as well as fundamental mastering tools. Included among these tools is The Five Step Mastering Process, a rigorously tested system that equips the practitioner to successfully and confidently master a project to exacting standards of audio fidelity. Covering all bases, the book discusses both macro and micro considerations: from mindset approach and connecting with clients down to detailed guidelines for processing audio, advanced methods, and audio restoration. Each chapter ends with exercises intended to deepen understanding and skill, or to supplement course study. Suitable for all levels, this is a unique resource for students, artists, and recording and Mastering Engineers alike. Major Label Mastering is supplemented by digital resources including audio examples and video tutorials.
If you’re like most R users, you have deep knowledge and love for statistics. But as your organization continues to collect huge amounts of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems. Authors Javier Luraschi, Kevin Kuo, and Edgar Ruiz show you how to use R with Spark to solve different data analysis problems. This book covers relevant data science topics, cluster computing, and issues that should interest even the most advanced users. Analyze, explore, transform, and visualize data in Apache Spark with R Create statistical models to extract information and predict outcomes; automate the process in production-ready workflows Perform analysis and modeling across many machines using distributed computing techniques Use large-scale data from multiple sources and different formats with ease from within Spark Learn about alternative modeling frameworks for graph processing, geospatial analysis, and genomics at scale Dive into advanced topics including custom transformations, real-time data processing, and creating custom Spark extensions
By reading this book on audio, individuals will learn how to create a truly quality sound within their final audio mix. Individuals will also discover everything required to become an accomplished audio wage earner. Its a practical information book on audio that takes the mystery out of audio recording studios, audio reinforcement, and room acoustics issues. Explained in detail are also many problems individuals will encounter in practical recording sessions and how to overcome issues. The purpose of this audio awareness guidebook is to guide individuals to become a respectable recording studio technician, managing live sound for their band and having a better understanding of audio.
Audio Mastering: The Artists collects more than twenty interviews, drawn from more than 60 hours of discussions, with many of the world’s leading mastering engineers.? In these exclusive and often intimate interviews, engineers consider the audio mastering process as they, themselves, experience and shape it as the leading artists in their field.? Each interview covers how engineers got started in the recording industry, what prompted them to pursue mastering, how they learned about the process, which tools and techniques they routinely use when they work, and a host of other particulars of their crafts.? We also spoke with mix engineers, and craftsmen responsible for some of the more iconic mastering tools now on the market, to gain a broader perspective on their work. ? This book is the first to provide such a comprehensive overview of the audio mastering process told from the point-of-view of the artists who engage in it.? In so doing, it pulls the curtain back on a crucial, but seldom heard from, agency in record production at large.
Learn how to use and implement transformers with Hugging Face and OpenAI (and others) by reading, running examples, investigating issues, asking the author questions, and interacting with our AI/ML community Key FeaturesPretrain a BERT-based model from scratch using Hugging FaceFine-tune powerful transformer models, including OpenAI's GPT-3, to learn the logic of your dataPerform root cause analysis on hard NLP problemsBook Description Transformers are...well...transforming the world of AI. There are many platforms and models out there, but which ones best suit your needs? Transformers for Natural Language Processing, 2nd Edition, guides you through the world of transformers, highlighting the strengths of different models and platforms, while teaching you the problem-solving skills you need to tackle model weaknesses. You'll use Hugging Face to pretrain a RoBERTa model from scratch, from building the dataset to defining the data collator to training the model. If you're looking to fine-tune a pretrained model, including GPT-3, then Transformers for Natural Language Processing, 2nd Edition, shows you how with step-by-step guides. The book investigates machine translations, speech-to-text, text-to-speech, question-answering, and many more NLP tasks. It provides techniques to solve hard language problems and may even help with fake news anxiety (read chapter 13 for more details). You'll see how cutting-edge platforms, such as OpenAI, have taken transformers beyond language into computer vision tasks and code creation using Codex. By the end of this book, you'll know how transformers work and how to implement them and resolve issues like an AI detective! What you will learnFind out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-EDiscover new techniques to investigate complex language problemsCompare and contrast the results of GPT-3 against T5, GPT-2, and BERT-based transformersCarry out sentiment analysis, text summarization, casual speech analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3Measure the productivity of key transformers to define their scope, potential, and limits in productionWho this book is for If you want to learn about and apply transformers to your natural language (and image) data, this book is for you. You'll need a good understanding of Python and deep learning and a basic understanding of NLP to benefit most from this book. Many platforms covered in this book provide interactive user interfaces, which allow readers with a general interest in NLP and AI to follow several chapters. And, don't worry if you get stuck or have questions; this book gives you direct access to our AI/ML community and author, Denis Rothman. So, he'll be there to guide you on your transformers journey!
MASTERING THE ART OF TIME MANAGEMENT “Until we can manage time, we can manage nothing else.” This famous quote rightly points out that every aspect of your life is determined by how you use your time. That’s because your life is made up of time. Time is your most precious commodity and it’s limited. Yet you may be easily losing time due to failure to manage it or by wasting it on unnecessary activities. On the other hand, you may be working hard and yet fail to achieve the desired results. This book will help you to master your time and conquer the results. “I don’t have time.” Is this your complaint too? This statement indicates you are experiencing poverty of time. This book will help you become time-rich with the aid of effective and practical methods. You may have heard about some time management techniques, but here you will come to know exactly how to apply them in your everyday life. Below are some of the pertinent topics presented in this book, which will reveal how to: • Manage time using the techniques of priorities, deadlines, and the 80/20 Principle • Save time with the art of delegation • Kill your time-killers • Attain freedom from the mental burden of work • Say “no” and save your time • Save time by enhancing your energy • Complete work in less time Once you master your time, everything else will fall in line and you will be able to achieve your highest potential.
Power and Energy Engineering are important and pressing topics globally, covering issues such as shifting paradigms of energy generation and consumption, intelligent grids, green energy and environmental protection. The 11th Asia-Pacific Power and Energy Engineering Conference (APPEEC 2019) was held in Xiamen, China from April 19 to 21, 2019. APPEEC has been an annual conference since 2009 and has been successfully held in Wuhan (2009 & 2011), Chengdu (2010 & 2017), Shanghai (2012 & 2014), Beijing (2013 & 2015), Suzhou (2016) and Guilin (2018), China. The objective of APPEEC 2019 was to provide scientific and professional interactions for the advancement of the fields of power and energy engineering. APPEEC 2019 facilitated the exchange of insights and innovations between industry and academia. A group of excellent speakers have delivered keynote speeches on emerging technologies in the field of power and energy engineering. Attendees were given the opportunity to give oral and poster presentations and to interface with invited experts.
Sure, you can import and mix a track in Pro Tools. You can work with MIDI and you know your way around the Edit window. The UI is as familiar as your most broken-in pair of jeans. We get it—you don’t need another button-pushing guide starting from the ground floor. Get uniquely in-depth coverage instead with In the Box Music Production: Advanced Tools and Techniques for Pro Tools. Author Mike Collins splits the book into three distinct sections covering how you use Pro Tools now—whether you’re working with the synths and samplers or loops and beats of a dance or hip-hop project, the soaring vocals of the next pop sensation, or the lush layers of an instrumental world music track. Use Pro Tools to its full potential with advice on studio techniques and full exploration of its internal capabilities. Learn to leverage Pro Tools and make it work for you with this guide that is fully grounded in real-world applications and process. This book assumes that the user has some music production experience and has worked through the basics in Pro Tools.