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Author: Anand Vemula Publisher: Independently Published ISBN: Category : Computers Languages : en Pages : 0
Book Description
In the ever-evolving world of Natural Language Processing (NLP), "Master NLP with Hugging Face: A Fine-tuning Toolkit" equips you to unlock the power of pre-trained models from Hugging Face. This comprehensive guide empowers you to transform these powerful models into workhorses for your specific NLP tasks. Gone are the days of training complex NLP models from scratch. This book dives into the art of fine-tuning, a technique that leverages the vast knowledge pre-trained models have already acquired and tailors it to your specific needs. You'll delve into the fundamentals of fine-tuning, understanding how to take a pre-trained model and adjust its final layers to excel on your chosen NLP task, whether it's text classification, sentiment analysis, question answering, or summarization. The book doesn't just provide theory - it's a hands-on toolkit. You'll establish your NLP development environment, ensuring you have the necessary tools to get started. By following step-by-step guides, you'll navigate the treasure trove of pre-trained models on the Hugging Face Model Hub, selecting the perfect model for your project. Data is the fuel for fine-tuning, and this book equips you to prepare your data effectively. Learn essential data cleaning and pre-processing techniques to ensure your model receives high-quality input. Master the art of data splitting, creating distinct training, validation, and test sets to optimize your model's performance and generalization capabilities. As you venture into fine-tuning, the book equips you to tackle challenges like overfitting and data requirements. Explore techniques to mitigate these issues and ensure your fine-tuned model performs exceptionally well on unseen data. Moving beyond the basics, "Master NLP with Hugging Face" introduces you to advanced concepts like building custom pipelines for text processing and customizing training configurations for optimal performance. You'll also gain insights into evaluation metrics, allowing you to precisely measure the effectiveness of your fine-tuned model for your specific NLP task. This book is your gateway to the exciting world of fine-tuning Hugging Face Transformers. With its comprehensive guidance and practical approach, you'll be well on your way to building robust and efficient NLP applications that can handle real-world challenges.
Author: Anand Vemula Publisher: Independently Published ISBN: Category : Computers Languages : en Pages : 0
Book Description
In the ever-evolving world of Natural Language Processing (NLP), "Master NLP with Hugging Face: A Fine-tuning Toolkit" equips you to unlock the power of pre-trained models from Hugging Face. This comprehensive guide empowers you to transform these powerful models into workhorses for your specific NLP tasks. Gone are the days of training complex NLP models from scratch. This book dives into the art of fine-tuning, a technique that leverages the vast knowledge pre-trained models have already acquired and tailors it to your specific needs. You'll delve into the fundamentals of fine-tuning, understanding how to take a pre-trained model and adjust its final layers to excel on your chosen NLP task, whether it's text classification, sentiment analysis, question answering, or summarization. The book doesn't just provide theory - it's a hands-on toolkit. You'll establish your NLP development environment, ensuring you have the necessary tools to get started. By following step-by-step guides, you'll navigate the treasure trove of pre-trained models on the Hugging Face Model Hub, selecting the perfect model for your project. Data is the fuel for fine-tuning, and this book equips you to prepare your data effectively. Learn essential data cleaning and pre-processing techniques to ensure your model receives high-quality input. Master the art of data splitting, creating distinct training, validation, and test sets to optimize your model's performance and generalization capabilities. As you venture into fine-tuning, the book equips you to tackle challenges like overfitting and data requirements. Explore techniques to mitigate these issues and ensure your fine-tuned model performs exceptionally well on unseen data. Moving beyond the basics, "Master NLP with Hugging Face" introduces you to advanced concepts like building custom pipelines for text processing and customizing training configurations for optimal performance. You'll also gain insights into evaluation metrics, allowing you to precisely measure the effectiveness of your fine-tuned model for your specific NLP task. This book is your gateway to the exciting world of fine-tuning Hugging Face Transformers. With its comprehensive guidance and practical approach, you'll be well on your way to building robust and efficient NLP applications that can handle real-world challenges.
Author: Anand Vemula Publisher: Anand Vemula ISBN: Category : Computers Languages : en Pages : 44
Book Description
In the ever-evolving world of Natural Language Processing (NLP), "Master NLP with Hugging Face: A Fine-tuning Toolkit" equips you to unlock the power of pre-trained models from Hugging Face. This comprehensive guide empowers you to transform these powerful models into workhorses for your specific NLP tasks. Gone are the days of training complex NLP models from scratch. This book dives into the art of fine-tuning, a technique that leverages the vast knowledge pre-trained models have already acquired and tailors it to your specific needs. You'll delve into the fundamentals of fine-tuning, understanding how to take a pre-trained model and adjust its final layers to excel on your chosen NLP task, whether it's text classification, sentiment analysis, question answering, or summarization. The book doesn't just provide theory - it's a hands-on toolkit. You'll establish your NLP development environment, ensuring you have the necessary tools to get started. By following step-by-step guides, you'll navigate the treasure trove of pre-trained models on the Hugging Face Model Hub, selecting the perfect model for your project. Data is the fuel for fine-tuning, and this book equips you to prepare your data effectively. Learn essential data cleaning and pre-processing techniques to ensure your model receives high-quality input. Master the art of data splitting, creating distinct training, validation, and test sets to optimize your model's performance and generalization capabilities. As you venture into fine-tuning, the book equips you to tackle challenges like overfitting and data requirements. Explore techniques to mitigate these issues and ensure your fine-tuned model performs exceptionally well on unseen data. Moving beyond the basics, "Master NLP with Hugging Face" introduces you to advanced concepts like building custom pipelines for text processing and customizing training configurations for optimal performance. You'll also gain insights into evaluation metrics, allowing you to precisely measure the effectiveness of your fine-tuned model for your specific NLP task. This book is your gateway to the exciting world of fine-tuning Hugging Face Transformers. With its comprehensive guidance and practical approach, you'll be well on your way to building robust and efficient NLP applications that can handle real-world challenges.
Author: Rick Spair Publisher: Rick Spair ISBN: Category : Computers Languages : en Pages : 888
Book Description
In the rapidly evolving landscape of artificial intelligence, Generative AI stands out as a transformative force with the potential to revolutionize industries and reshape our understanding of creativity and automation. From its inception, Generative AI has captured the imagination of researchers, developers, and entrepreneurs, offering unprecedented capabilities in generating new data, simulating complex systems, and solving intricate problems that were once considered beyond the reach of machines. This book, "200 Tips for Mastering Generative AI," is a comprehensive guide designed to empower you with the knowledge and practical insights needed to harness the full potential of Generative AI. Whether you are a seasoned AI practitioner, a curious researcher, a forward-thinking entrepreneur, or a passionate enthusiast, this book provides valuable tips and strategies to navigate the vast and intricate world of Generative AI. We invite you to explore, experiment, and innovate with the knowledge you gain from this book. Together, we can unlock the full potential of Generative AI and shape a future where intelligent machines and human creativity coexist and collaborate in unprecedented ways. Welcome to "200 Tips for Mastering Generative AI." Your journey into the fascinating world of Generative AI begins here.
Author: Darwish, Dina Publisher: IGI Global ISBN: Category : Computers Languages : en Pages : 403
Book Description
In the field of information retrieval, the challenge lies in the speed and accuracy with which users can access relevant data. With the increasing complexity of digital interactions, the need for a solution that transcends traditional methods becomes evident. Human involvement and manual investigation are not only time-consuming but also prone to errors, hindering the seamless exchange of information in various sectors. Design and Development of Emerging Chatbot Technology emerges as a comprehensive solution to the predicament posed by traditional information retrieval methods. Focusing on the transformative power of chatbots, it delves into the intricacies of their operation, applications, and development. Designed for academic scholars across diverse disciplines, the book serves as a beacon for those seeking a deeper understanding of chatbots and their potential to revolutionize information retrieval in customer service, education, healthcare, e-commerce, and more.
Author: Denis Rothman Publisher: Packt Publishing Ltd ISBN: 1803243481 Category : Computers Languages : en Pages : 603
Book Description
OpenAI's GPT-3, ChatGPT, GPT-4 and Hugging Face transformers for language tasks in one book. Get a taste of the future of transformers, including computer vision tasks and code writing and assistance. Purchase of the print or Kindle book includes a free eBook in PDF format Key Features Improve your productivity with OpenAI’s ChatGPT and GPT-4 from prompt engineering to creating and analyzing machine learning models Pretrain a BERT-based model from scratch using Hugging Face Fine-tune powerful transformer models, including OpenAI's GPT-3, to learn the logic of your data Book DescriptionTransformers 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 DALL-E 2, ChatGPT, and GPT-4. 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 learn Discover new techniques to investigate complex language problems Compare and contrast the results of GPT-3 against T5, GPT-2, and BERT-based transformers Carry out sentiment analysis, text summarization, casual speech analysis, machine translations, and more using TensorFlow, PyTorch, and GPT-3 Find out how ViT and CLIP label images (including blurry ones!) and create images from a sentence using DALL-E Learn the mechanics of advanced prompt engineering for ChatGPT and GPT-4 Who 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 to help guide you on your transformers journey!
Author: Jeremy Howard Publisher: O'Reilly Media ISBN: 1492045497 Category : Computers Languages : en Pages : 624
Book Description
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Author: Yuxi (Hayden) Liu Publisher: Packt Publishing Ltd ISBN: 183508222X Category : Computers Languages : en Pages : 519
Book Description
Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandas Key Features Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling Includes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutions Implement ML models, such as neural networks and linear and logistic regression, from scratch Purchase of the print or Kindle book includes a free PDF copy Book DescriptionThe fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.What you will learn Follow machine learning best practices throughout data preparation and model development Build and improve image classifiers using convolutional neural networks (CNNs) and transfer learning Develop and fine-tune neural networks using TensorFlow and PyTorch Analyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIP Build classifiers using support vector machines (SVMs) and boost performance with PCA Avoid overfitting using regularization, feature selection, and more Who this book is for This expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.
Author: Raymond S. T. Lee Publisher: Springer Nature ISBN: 9819919991 Category : Computers Languages : en Pages : 454
Book Description
This textbook presents an up-to-date and comprehensive overview of Natural Language Processing (NLP), from basic concepts to core algorithms and key applications. Further, it contains seven step-by-step NLP workshops (total length: 14 hours) offering hands-on practice with essential Python tools like NLTK, spaCy, TensorFlow Kera, Transformer and BERT. The objective of this book is to provide readers with a fundamental grasp of NLP and its core technologies, and to enable them to build their own NLP applications (e.g. Chatbot systems) using Python-based NLP tools. It is both a textbook and NLP tool-book intended for the following readers: undergraduate students from various disciplines who want to learn NLP; lecturers and tutors who want to teach courses or tutorials for undergraduate/graduate students on NLP and related AI topics; and readers with various backgrounds who want to learn NLP, and more importantly, to build workable NLP applications after completing its 14 hours of Python-based workshops.
Author: Denis Rothman Publisher: Packt Publishing Ltd ISBN: 1800568630 Category : Computers Languages : en Pages : 385
Book Description
Publisher's Note: A new edition of this book is out now that includes working with GPT-3 and comparing the results with other models. It includes even more use cases, such as casual language analysis and computer vision tasks, as well as an introduction to OpenAI's Codex. Key FeaturesBuild and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning modelsGo through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machineTest transformer models on advanced use casesBook Description The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets. What you will learnUse the latest pretrained transformer modelsGrasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer modelsCreate language understanding Python programs using concepts that outperform classical deep learning modelsUse a variety of NLP platforms, including Hugging Face, Trax, and AllenNLPApply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and moreMeasure the productivity of key transformers to define their scope, potential, and limits in productionWho this book is for Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers. Readers who can benefit the most from this book include experienced deep learning & NLP practitioners and data analysts & data scientists who want to process the increasing amounts of language-driven data.
Author: Nicole Forsgren, PhD Publisher: IT Revolution ISBN: 1942788355 Category : Business & Economics Languages : en Pages : 244
Book Description
Winner of the Shingo Publication Award Accelerate your organization to win in the marketplace. How can we apply technology to drive business value? For years, we've been told that the performance of software delivery teams doesn't matter―that it can't provide a competitive advantage to our companies. Through four years of groundbreaking research to include data collected from the State of DevOps reports conducted with Puppet, Dr. Nicole Forsgren, Jez Humble, and Gene Kim set out to find a way to measure software delivery performance―and what drives it―using rigorous statistical methods. This book presents both the findings and the science behind that research, making the information accessible for readers to apply in their own organizations. Readers will discover how to measure the performance of their teams, and what capabilities they should invest in to drive higher performance. This book is ideal for management at every level.
Author: Anand Vemula
Author: Anand Vemula
Author: Anand Vemula
Author: Rick Spair
Author: Darwish, Dina
Author: Denis Rothman
Author: Jeremy Howard
Author: Yuxi (Hayden) Liu
Author: Raymond S. T. Lee
Author: Denis Rothman
Author: Nicole Forsgren, PhD