Transformers In Deep Learning Architecture

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Transformers In Deep Learning Architecture

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-05-28

Transformers In Deep Learning Architecture written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-28 with Computers categories.

"Transformers in Deep Learning Architecture" "Transformers in Deep Learning Architecture" presents a comprehensive and rigorous exploration of the transformer paradigm—the foundational architecture that has revolutionized modern artificial intelligence. The book opens by situating transformers within the historical context of neural sequence models, methodically tracing their evolution from recurrent neural networks to the self-attention mechanisms that address their predecessor’s limitations. Early chapters lay a strong mathematical and conceptual foundation, introducing key terminology, theoretical principles, and detailed comparisons with alternative architectures to prepare readers for a deep technical dive. At its core, the book delivers an in-depth analysis of the architectural details and operational intricacies that underpin transformer models. Subsequent chapters dissect the encoder-decoder framework, decompose self-attention and multi-head attention mechanisms, and discuss design choices such as positional encodings, feedforward networks, normalization strategies, and scaling laws. Readers also encounter a nuanced treatment of advanced attention variants—including efficient, sparse, and cross-modal extensions—along with proven paradigms for pretraining, transfer learning, and domain adaptation. Rich case studies illustrate the extraordinary performance of transformers in natural language processing, vision, audio, and multimodal tasks, highlighting both established applications and emerging frontiers. Beyond technical mastery, the book addresses the practical dimensions and responsible deployment of large transformer models. It guides practitioners through scalable training, distributed modernization, and infrastructure optimization, while confronting contemporary challenges in interpretability, robustness, ethics, and privacy. The final chapters forecast the transformative future of the field with discussions on long-context modeling, symbolic integration, neuromorphic and quantum-inspired approaches, and the profound societal implications of widespread transformer adoption. Altogether, this volume stands as both an authoritative reference and a visionary roadmap for researchers and engineers working at the cutting edge of deep learning.

Applied Machine Learning And Deep Learning Architectures And Techniques

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Author : Nitin Liladhar Rane
language : en
Publisher: Deep Science Publishing
Release Date : 2024-10-13

Applied Machine Learning And Deep Learning Architectures And Techniques written by Nitin Liladhar Rane and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-13 with Computers categories.

This book provides an extensive overview of recent advances in machine learning (ML) and deep learning (DL). It starts with a comprehensive introduction to the latest architectural and design practices, with an overview of basic techniques and optimization algorithms and methodologies that are fundamental to modern ML/DL development followed by the tools and frameworks that are driving innovation in ML/DL. The presentation then points to the central position of ML and DL in developing generative AI like ChatGPT. Then look at different industrial applications such as explaining the real-world impacts of each. This includes challenges around corroborate artificial Intelligence (AI), and trustworthy AI, and so on. Finally, the book presents a futuristic vision on the potentials and implications of future ML and DL architectures, making it an ideal guide for researchers, practitioners and industry professionals. This book will be a significant resource for comprehending present advancements, addressing encounter challenges, and traversing the ML and DL landscape in future, making it an indispensable reference for anyone interested in applying these technologies across sectors.

The Deep Learning Architect S Handbook

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Author : Ee Kin Chin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-12-29

The Deep Learning Architect S Handbook written by Ee Kin Chin 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 2023-12-29 with Computers categories.

Harness the power of deep learning to drive productivity and efficiency using this practical guide covering techniques and best practices for the entire deep learning life cycle Key Features Interpret your models’ decision-making process, ensuring transparency and trust in your AI-powered solutions Gain hands-on experience in every step of the deep learning life cycle Explore case studies and solutions for deploying DL models while addressing scalability, data drift, and ethical considerations Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDeep learning enables previously unattainable feats in automation, but extracting real-world business value from it is a daunting task. This book will teach you how to build complex deep learning models and gain intuition for structuring your data to accomplish your deep learning objectives. This deep learning book explores every aspect of the deep learning life cycle, from planning and data preparation to model deployment and governance, using real-world scenarios that will take you through creating, deploying, and managing advanced solutions. You’ll also learn how to work with image, audio, text, and video data using deep learning architectures, as well as optimize and evaluate your deep learning models objectively to address issues such as bias, fairness, adversarial attacks, and model transparency. As you progress, you’ll harness the power of AI platforms to streamline the deep learning life cycle and leverage Python libraries and frameworks such as PyTorch, ONNX, Catalyst, MLFlow, Captum, Nvidia Triton, Prometheus, and Grafana to execute efficient deep learning architectures, optimize model performance, and streamline the deployment processes. You’ll also discover the transformative potential of large language models (LLMs) for a wide array of applications. By the end of this book, you'll have mastered deep learning techniques to unlock its full potential for your endeavors.What you will learn Use neural architecture search (NAS) to automate the design of artificial neural networks (ANNs) Implement recurrent neural networks (RNNs), convolutional neural networks (CNNs), BERT, transformers, and more to build your model Deal with multi-modal data drift in a production environment Evaluate the quality and bias of your models Explore techniques to protect your model from adversarial attacks Get to grips with deploying a model with DataRobot AutoML Who this book is for This book is for deep learning practitioners, data scientists, and machine learning developers who want to explore deep learning architectures to solve complex business problems. Professionals in the broader deep learning and AI space will also benefit from the insights provided, applicable across a variety of business use cases. Working knowledge of Python programming and a basic understanding of deep learning techniques is needed to get started with this book.

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.

Advanced Deep Learning Architectures Beyond Cnns And Lstms

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Author : Mrs. Shubhashree Sahoo
language : en
Publisher: JEC PUBLICATION
Release Date :

Advanced Deep Learning Architectures Beyond Cnns And Lstms written by Mrs. Shubhashree Sahoo and has been published by JEC PUBLICATION this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

Deep learning has fundamentally transformed the landscape of artificial intelligence, powering breakthroughs in computer vision, natural language processing, speech recognition, and beyond. Over the past decade, Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) emerged as the dominant architectures for tackling structured image data and sequential information, respectively. CNNs, with their hierarchical feature extraction and local receptive fields, revolutionized image classification, object detection, and semantic segmentation, enabling systems to achieve near-human or even superhuman performance on benchmarks such as ImageNet. LSTMs, on the other hand, overcame the limitations of traditional recurrent neural networks, effectively addressing the vanishing gradient problem and enabling long-range temporal dependencies to be learned, thereby advancing language modeling, machine translation, and speech synthesis. However, as datasets expanded to billions of samples, tasks grew increasingly complex, and the need for generalization across heterogeneous domains intensified, the inherent limitations of CNNs and LSTMs became apparent. CNNs struggle with modeling long-range dependencies, processing irregular graph-structured data, or integrating multimodal inputs, while LSTMs face challenges in parallelization, handling very long sequences efficiently, and scaling to massive model sizes. Moreover, simply increasing depth, width, or training data does not always guarantee proportional improvements in performance, highlighting the plateauing effect of these architectures in cutting-edge AI applications. This book, Advanced Deep Learning Architectures: Beyond CNNs and LSTMs, is designed to chart the evolution of next-generation architectures that address these limitations and define the future of artificial intelligence. It offers a comprehensive journey through the design principles, mathematical foundations, and practical implementations of state-of-the-art models. The scope spans transformers — including BERT, GPT, and Vision Transformers — which leverage selfattention to model long-range dependencies and multimodal relationships; graph neural networks, which enable AI systems to reason about relational and non-Euclidean data; capsule networks, which improve spatial hierarchies and pose awareness; neural ordinary differential equations, which introduce continuous-time modeling; and diffusion models, which have redefined generative AI through probabilistic modeling and denoising frameworks. The book also delves into hybrid and multimodal architectures, advanced reinforcement learning frameworks, and emerging paradigms such as neuromorphic computing, quantum deep learning, and evolutionary algorithms. Importantly, the text bridges theory and practice. Each chapter combines rigorous mathematical derivations — from linear algebra and tensor calculus to probabilistic modeling and information-theoretic analysis — with hands-on Python implementations using libraries such as PyTorch, TensorFlow, and NumPy. Readers are guided through step-by-step coding exercises, practical tips for optimization, and demonstrations of applying these models to real-world datasets. This dual approach ensures a deep understanding not just of why these architectures work, but how to implement, experiment, and extend them. Beyond technical mastery, the book emphasizes the broader implications of advanced AI: multimodal intelligence, ethical alignment, interpretability, and the path toward general-purpose AI systems.

Mastering Transformers

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Author : X. Y. WANG
language : en
Publisher: Independently Published
Release Date : 2025-07-15

Mastering Transformers written by X. Y. WANG and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-15 with Computers categories.

"Mastering Transformers: Architecture and Applications in Deep Learning" is an authoritative and meticulously organized guide to the foundational theories, advanced architectures, and real-world implementations of transformer models. Starting with the theoretical underpinnings, the book demystifies self-attention, positional encoding, normalization strategies, and the transformative scalability of transformers compared to RNNs and CNNs. Readers are led through a progression of architectures, encompassing the original encoder-decoder frameworks to pivotal variants such as BERT, GPT, and T5, as well as cutting-edge solutions for handling long sequences, improving efficiency, and integrating hybrid models. Delving deep into machine learning workflows, the book systematically covers pretraining strategies-including masked and causal language modeling, contrastive and multimodal objectives, and robust denoising tasks-before advancing to expert-level discussions on fine-tuning, domain adaptation, and in-context learning. Detailed chapters on scalability and optimization feature state-of-the-art distributed training, memory management, regularization, and hyperparameter tuning techniques. Visualizations, probing methods, bias mitigation strategies, and model compression are explored to ensure interpretability and resilience in both research and production environments. The final sections offer a panoramic view of practical applications, from natural language processing and computer vision to code intelligence, biology, and edge deployments. With comprehensive insights into deployment, monitoring, security, and the exciting frontiers of research including large language models, lifelong learning, and responsible AI governance, this book is an indispensable reference for engineers, researchers, and practitioners seeking to master and innovate in the rapidly evolving transformer landscape.

Artificial Intelligence In Performance Driven Design

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Author : Narjes Abbasabadi
language : en
Publisher: John Wiley & Sons
Release Date : 2024-04-17

Artificial Intelligence In Performance Driven Design written by Narjes Abbasabadi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-17 with Architecture categories.

A definitive, interdisciplinary reference to using artificial intelligence technology and data-driven methodologies for sustainable design Artificial Intelligence in Performance-Driven Design: Theories, Methods, and Tools explores the application of artificial intelligence (AI), specifically machine learning (ML), for performance modeling within the built environment. This work develops the theoretical foundations and methodological frameworks for utilizing AI/ML, with an emphasis on multi-scale modeling encompassing energy flows, environmental quality, and human systems. The book examines relevant practices, case studies, and computational tools that harness AI’s capabilities in modeling frameworks, enhancing the efficiency, accuracy, and integration of physics-based simulation, optimization, and automation processes. Furthermore, it highlights the integration of intelligent systems and digital twins throughout the lifecycle of the built environment, to enhance our understanding and management of these complex environments. This book also: Incorporates emerging technologies into practical ideas to improve performance analysis and sustainable design Presents data-driven methodologies and technologies that integrate into modeling and design platforms Shares valuable insights and tools for developing decarbonization pathways in urban buildings Includes contributions from expert researchers and educators across a range of related fields Artificial Intelligence in Performance-Driven Design is ideal for architects, engineers, planners, and researchers involved in sustainable design and the built environment. It’s also of interest to students of architecture, building science and technology, urban design and planning, environmental engineering, and computer science and engineering.

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.

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.