Transformers For Natural Language Processing And Computer Vision
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Transformers For Natural Language Processing
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Author : Denis Rothman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-03-25
Transformers For Natural Language Processing written by Denis Rothman and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-25 with Computers categories.
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!
Transformers For Natural Language Processing And Computer Vision
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Author : Denis Rothman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-02-29
Transformers For Natural Language Processing And Computer Vision written by Denis Rothman and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-29 with Computers categories.
The definitive guide to LLMs, from architectures, pretraining, and fine-tuning to Retrieval Augmented Generation (RAG), multimodal AI, risk mitigation, and practical implementations with ChatGPT, Hugging Face, and Vertex AI Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Compare and contrast 20+ models (including GPT, BERT, and Llama) and multiple platforms and libraries to find the right solution for your project Apply RAG with LLMs using customized texts and embeddings Mitigate LLM risks, such as hallucinations, using moderation models and knowledge bases Book DescriptionTransformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, practical applications, and popular platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV). The book guides you through a range of transformer architectures from foundation models and generative AI. You’ll pretrain and fine-tune LLMs and work through different use cases, from summarization to question-answering systems leveraging embedding-based search. You'll also implement Retrieval Augmented Generation (RAG) to enhance accuracy and gain greater control over your LLM outputs. Additionally, you’ll understand common LLM risks, such as hallucinations, memorization, and privacy issues, and implement mitigation strategies using moderation models alongside rule-based systems and knowledge integration. Dive into generative vision transformers and multimodal architectures, and build practical applications, such as image and video classification. Go further and combine different models and platforms to build AI solutions and explore AI agent capabilities. This book provides you with an understanding of transformer architectures, including strategies for pretraining, fine-tuning, and LLM best practices.What you will learn Breakdown and understand the architectures of the Transformer, BERT, GPT, T5, PaLM, ViT, CLIP, and DALL-E Fine-tune BERT, GPT, and PaLM models Learn about different tokenizers and the best practices for preprocessing language data Pretrain a RoBERTa model from scratch Implement retrieval augmented generation and rules bases to mitigate hallucinations Visualize transformer model activity for deeper insights using BertViz, LIME, and SHAP Go in-depth into vision transformers with CLIP, DALL-E, and GPT Who this book is for This book is ideal for NLP and CV engineers, data scientists, machine learning practitioners, software developers, and technical leaders looking to advance their expertise in LLMs and generative AI or explore latest industry trends. Familiarity with Python and basic machine learning concepts will help you fully understand the use cases and code examples. However, hands-on examples involving LLM user interfaces, prompt engineering, and no-code model building ensure this book remains accessible to anyone curious about the AI revolution.
What Fuels Transformers In Computer Vision Unraveling Vit S Advantages
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Author : Tolga Topal
language : en
Publisher: GRIN Verlag
Release Date : 2024-01-11
What Fuels Transformers In Computer Vision Unraveling Vit S Advantages written by Tolga Topal and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-11 with Computers categories.
Master's Thesis from the year 2022 in the subject Computer Sciences - Artificial Intelligence, grade: 7.50, Universidad de Alcalá, course: Artificial Intelligence and Deep Learning, language: English, abstract: Vision Transformers (ViT) are neural model architectures that compete and exceed classical convolutional neural networks (CNNs) in computer vision tasks. ViT's versatility and performance is best understood by proceeding with a backward analysis. In this study, we aim to identify, analyse and extract the key elements of ViT by backtracking on the origin of Transformer neural architectures (TNA). We hereby highlight the benefits and constraints of the Transformer architecture, as well as the foundational role of self- and multi-head attention mechanisms. We now understand why self-attention might be all we need. Our interest of the TNA has driven us to consider self-attention as a computational primitive. This generic computation framework provides flexibility in the tasks that can be performed by the Transformer. After a good grasp on Transformers, we went on to analyse their vision-applied counterpart, namely ViT, which is roughly a transposition of the initial Transformer architecture to an image-recognition and -processing context. When it comes to computer vision, convolutional neural networks are considered the go to paradigm. Because of their proclivity for vision, we naturally seek to understand how ViT compared to CNN. It seems that their inner workings are rather different. CNNs are built with a strong inductive bias, an engineering feature that provides them with the ability to perform well in vision tasks. ViT have less inductive bias and need to learn this (convolutional filters) by ingesting enough data. This makes Transformer-based architecture rather data-hungry and more adaptable. Finally, we describe potential enhancements on the Transformer with a focus on possible architectural extensions. We discuss some exciting learning approaches in machine learning. Our last part analysis leads us to ponder on the flexibility of Transformer-based neural architecture. We realize and argue that this feature might possibility be linked to their Turing-completeness.
Mastering Transformers
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Author : Savaş Yıldırım
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-06-03
Mastering Transformers written by Savaş Yıldırım and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-03 with Computers categories.
Explore transformer-based language models from BERT to GPT, delving into NLP and computer vision tasks, while tackling challenges effectively Key Features Understand the complexity of deep learning architecture and transformers architecture Create solutions to industrial natural language processing (NLP) and computer vision (CV) problems Explore challenges in the preparation process, such as problem and language-specific dataset transformation Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionTransformer-based language models such as BERT, T5, GPT, DALL-E, and ChatGPT have dominated NLP studies and become a new paradigm. Thanks to their accurate and fast fine-tuning capabilities, transformer-based language models have been able to outperform traditional machine learning-based approaches for many challenging natural language understanding (NLU) problems. Aside from NLP, a fast-growing area in multimodal learning and generative AI has recently been established, showing promising results. Mastering Transformers will help you understand and implement multimodal solutions, including text-to-image. Computer vision solutions that are based on transformers are also explained in the book. You’ll get started by understanding various transformer models before learning how to train different autoregressive language models such as GPT and XLNet. The book will also get you up to speed with boosting model performance, as well as tracking model training using the TensorBoard toolkit. In the later chapters, you’ll focus on using vision transformers to solve computer vision problems. Finally, you’ll discover how to harness the power of transformers to model time series data and for predicting. By the end of this transformers book, you’ll have an understanding of transformer models and how to use them to solve challenges in NLP and CV.What you will learn Focus on solving simple-to-complex NLP problems with Python Discover how to solve classification/regression problems with traditional NLP approaches Train a language model and explore how to fine-tune models to the downstream tasks Understand how to use transformers for generative AI and computer vision tasks Build transformer-based NLP apps with the Python transformers library Focus on language generation such as machine translation and conversational AI in any language Speed up transformer model inference to reduce latency Who this book is for This book is for deep learning researchers, hands-on practitioners, and ML/NLP researchers. Educators, as well as students who have a good command of programming subjects, knowledge in the field of machine learning and artificial intelligence, and who want to develop apps in the field of NLP as well as multimodal tasks will also benefit from this book’s hands-on approach. Knowledge of Python (or any programming language) and machine learning literature, as well as a basic understanding of computer science, are required.
Transformers For Natural Language Processing
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Author : Denis Rothman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-01-29
Transformers For Natural Language Processing written by Denis Rothman and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-29 with Computers categories.
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.
Transformers For Machine Learning
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Author : Uday Kamath
language : en
Publisher: CRC Press
Release Date : 2022-05-24
Transformers For Machine Learning written by Uday Kamath and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-24 with Computers categories.
Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations, resulting in newer techniques and methods. Transformers for Machine Learning: A Deep Dive is the first comprehensive book on transformers. Key Features: A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers. 60+ transformer architectures covered in a comprehensive manner. A book for understanding how to apply the transformer techniques in speech, text, time series, and computer vision. Practical tips and tricks for each architecture and how to use it in the real world. Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries, all ready to run in Google Colab. The theoretical explanations of the state-of-the-art transformer architectures will appeal to postgraduate students and researchers (academic and industry) as it will provide a single entry point with deep discussions of a quickly moving field. The practical hands-on case studies and code will appeal to undergraduate students, practitioners, and professionals as it allows for quick experimentation and lowers the barrier to entry into the field.
Natural Language Processing For Computer Vision
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Author : Thomas Strader
language : en
Publisher: Independently Published
Release Date : 2025-06-09
Natural Language Processing For Computer Vision written by Thomas Strader and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-09 with Computers categories.
Natural Language Processing for Computer Vision: Unlocking Multimodal AI Applications This book offers a comprehensive and practical guide to the fast-growing intersection of Natural Language Processing (NLP) and Computer Vision. As multimodal AI becomes essential for real-world applications-ranging from image captioning to visual question answering and autonomous systems-understanding how language and vision models work together is critical for today's AI developers, researchers, and enthusiasts. In Natural Language Processing for Computer Vision, you'll explore the foundations and advanced techniques that power modern multimodal systems. From pretrained transformers and vision-language models to building custom pipelines and fine-tuning strategies, this book covers the essential tools, libraries, and hands-on projects that help bring intelligent visual-linguistic systems to life. Blending theory with application, this book walks you through step-by-step implementations of real-world tasks like image captioning, visual search, and vision-based question answering. You'll gain insights into pretrained multimodal models like CLIP, BLIP, and Flamingo, while learning how to fine-tune them on your own datasets. With a strong focus on interpretability, ethical AI, and resource optimization, the book not only teaches how to build systems but also how to build them responsibly. Key Features of This Book End-to-end coverage of multimodal AI: vision, language, and their integration Practical implementation using Hugging Face, PyTorch, and TensorFlow Step-by-step projects including image captioning, VQA, and model fine-tuning Discussions on zero-shot learning, prompt engineering, and attention mechanisms Ethical AI insights: fairness, bias mitigation, and responsible deployment Future-focused chapters on robotics, vision-language agents, and emerging tech This book is ideal for data scientists, machine learning engineers, AI researchers, and graduate students who want to dive into multimodal AI. If you're already familiar with either NLP or computer vision and want to explore how they combine, this book is your go-to resource. Unlock the full potential of multimodal AI by mastering the fusion of language and vision. Whether you're building smart assistants, content moderation tools, or next-gen robotics, Natural Language Processing for Computer Vision equips you with the skills and insights to innovate with confidence. Start your journey into the future of AI-get your copy today.
Multi Modal Machine Learning An Introduction To Bert Pre Trained Visio Linguistic Models
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Author : Johanna Garthe
language : en
Publisher: GRIN Verlag
Release Date : 2023-12-13
Multi Modal Machine Learning An Introduction To Bert Pre Trained Visio Linguistic Models written by Johanna Garthe and has been published by GRIN Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-13 with Computers categories.
Seminar paper from the year 2021 in the subject Computer Sciences - Computational linguistics, grade: 1,3, University of Trier (Computerlinguistik und Digital Humanities), course: Mathematische Modellierung, language: English, abstract: In the field of multi-modal machine learning, where the fusion of various sensory inputs shapes learning paradigms, this paper provides an introduction to BERT-based pre-trained visio-linguistic models by specifically summarizing and analyzing two approaches: ViLBERT and VL-BERT, aiming to highlight and discuss their distinctive characteristics. The paper is structured into five chapters as follows. Chapter 2 lays the fundamental principles by introducing the characteristics of the Transformer encoder and BERT. Chapter 3 presents the selected visual-linguistic models, ViLBERT and VL-BERT. The objective of chapter 4 is to summarize and discuss both models. The paper concludes with an outlook in chapter 5. Transfer learning is a powerful technique in the field of deep learning. At first, a model is pre-trained on a specific task. Then fine-tuning is performed by taking the trained network as the basis of a new purpose-specific model to apply it on a separate task. In this way, transfer learning helps to reduce the need to develop new models for new tasks from scratch and hence saves time for training and verification. Nowadays, there are different such pre-trained models in computer vision, natural language processing (NLP) and recently for visio-linguistic tasks. The pre-trained models presented later in this paper are both based on and use BERT. BERT, which stands for Bidirectional Encoder Representations from Transformers, is a popular training technique for NLP, which is based on the architecture of a Transformer.
Ultimate Transformer Models Using Pytorch 2 0
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Author : Abhiram Ravikumar
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2025-09-04
Ultimate Transformer Models Using Pytorch 2 0 written by Abhiram Ravikumar and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-04 with Computers categories.
TAGLINE Build Real-World AI with Transformers Powered by PyTorch 2.0. KEY FEATURES ● Complete hands-on projects spanning NLP, vision, and speech AI. ● Interactive Jupyter Notebooks with real-world industry scenarios. ● Build a professional AI portfolio ready for career advancement. DESCRIPTION Transformer models have revolutionized AI across natural language processing, computer vision, and speech recognition. "Ultimate Transformer Models Using PyTorch 2.0" bridges theory and practice, guiding you from fundamentals to advanced implementations with hands-on projects that build a professional AI portfolio. This comprehensive journey spans 11 chapters, beginning with transformer foundations and PyTorch 2.0 setup. With this book, you will master self-attention mechanisms, tackle NLP tasks such as text classification and translation, and then expand into computer vision and speech processing. Advanced topics include BERT and GPT models, the Hugging Face ecosystem, training strategies, and deployment techniques. Each chapter features practical exercises that reinforce learning through real-world applications. By the end of this book, you will be able to confidently design, implement, and optimize transformer models for diverse challenges. So, whether revolutionizing language understanding, advancing computer vision, or innovating speech recognition, you will possess both theoretical knowledge and practical expertise to deploy solutions effectively across industries like healthcare, finance, and social media, positioning yourself at the AI revolution's forefront. WHAT WILL YOU LEARN ● Build custom transformer architectures from scratch, using PyTorch 2.0. ● Fine-tune BERT, GPT, and T5 models for specific applications. ● Deploy production-ready AI models across NLP, vision, and speech domains. ● Master Hugging Face ecosystem for rapid model development and deployment. ● Optimize transformer performance, using advanced training techniques and hyperparameters. ● Create a professional portfolio showcasing real-world transformer implementations. WHO IS THIS BOOK FOR? This book is designed for data scientists, ML engineers, AI practitioners, and computer science students with intermediate Python Programming skills and basic machine learning knowledge. Readers should have foundational understanding of neural networks and deep learning principles, though prior transformer or PyTorch 2.0 experience is not required. TABLE OF CONTENTS 1. Understanding the Evolution of Neural Networks 2. Fundamentals of Transformer Architecture 3. Getting Started with PyTorch 2.0 4. Natural Language Processing with Transformers 5. Computer Vision with Transformers 6. Speech Processing with Transformers 7. Advanced Transformer Models 8. Using HuggingFace with PyTorch 9. Training and Fine-Tuning Transformers 10. Deploying Transformer Models 11. Transformers in Real-World Applications Index
Learning Deep Learning
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Author : Magnus Ekman
language : en
Publisher:
Release Date : 2021
Learning Deep Learning written by Magnus Ekman and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
NVIDIA's Full-Color Guide to Deep Learning with TensorFlow: All You Need to Get Started and Get Results Deep learning is a key component of today's exciting advances in machine learning and artificial intelligence. Learning Deep Learning is a complete guide to deep learning with TensorFlow, the #1 Python library for building these breakthrough applications. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others--including those with no prior machine learning or statistics experience. After introducing the essential building blocks of deep neural networks, Magnus Ekman shows how to use fully connected feedforward networks and convolutional networks to solve real problems, such as predicting housing prices or classifying images. You'll learn how to represent words from a natural language, capture semantics, and develop a working natural language translator. With that foundation in place, Ekman then guides you through building a system that inputs images and describes them in natural language. Throughout, Ekman provides concise, well-annotated code examples using TensorFlow and the Keras API. (For comparison and easy migration between frameworks, complementary PyTorch examples are provided online.) He concludes by previewing trends in deep learning, exploring important ethical issues, and providing resources for further learning. Master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation See how frameworks make it easier to develop more robust and useful neural networks Discover how convolutional neural networks (CNNs) revolutionize classification and analysis Use recurrent neural networks (RNNs) to optimize for text, speech, and other variable-length sequences Master long short-term memory (LSTM) techniques for natural language generation and other applications Move further into natural language-processing (NLP), including understanding and translation.