Transfer Learning
DOWNLOAD
Download Transfer Learning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Transfer Learning book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Transfer Learning
DOWNLOAD
Author : Qiang Yang
language : en
Publisher: Cambridge University Press
Release Date : 2020-02-13
Transfer Learning written by Qiang Yang and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-13 with Computers categories.
This in-depth tutorial for students, researchers, and developers covers foundations, plus applications ranging from search to multimedia.
Hands On Transfer Learning With Python
DOWNLOAD
Author : Dipanjan Sarkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-31
Hands On Transfer Learning With Python written by Dipanjan Sarkar 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 2018-08-31 with Computers categories.
Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Key Features Build deep learning models with transfer learning principles in Python implement transfer learning to solve real-world research problems Perform complex operations such as image captioning neural style transfer Book Description Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLP Who this book is for Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.
Transfer Learning For Natural Language Processing
DOWNLOAD
Author : Paul Azunre
language : en
Publisher: Simon and Schuster
Release Date : 2021-08-31
Transfer Learning For Natural Language Processing written by Paul Azunre and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-31 with Computers categories.
Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems. Summary In Transfer Learning for Natural Language Processing you will learn: Fine tuning pretrained models with new domain data Picking the right model to reduce resource usage Transfer learning for neural network architectures Generating text with generative pretrained transformers Cross-lingual transfer learning with BERT Foundations for exploring NLP academic literature Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you’ll save on training time and computational costs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. About the book Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can practice your new skills immediately. As you go, you’ll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications. What's inside Fine tuning pretrained models with new domain data Picking the right model to reduce resource use Transfer learning for neural network architectures Generating text with pretrained transformers About the reader For machine learning engineers and data scientists with some experience in NLP. About the author Paul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. Table of Contents PART 1 INTRODUCTION AND OVERVIEW 1 What is transfer learning? 2 Getting started with baselines: Data preprocessing 3 Getting started with baselines: Benchmarking and optimization PART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS) 4 Shallow transfer learning for NLP 5 Preprocessing data for recurrent neural network deep transfer learning experiments 6 Deep transfer learning for NLP with recurrent neural networks PART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES 7 Deep transfer learning for NLP with the transformer and GPT 8 Deep transfer learning for NLP with BERT and multilingual BERT 9 ULMFiT and knowledge distillation adaptation strategies 10 ALBERT, adapters, and multitask adaptation strategies 11 Conclusions
Transfer Of Learning
DOWNLOAD
Author : Robert E. Haskell
language : en
Publisher: Academic Press
Release Date : 2001
Transfer Of Learning written by Robert E. Haskell and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Education categories.
This text addresses the problem of how our past or current learning influences, is generalised and is applied or adapted to similar or new situations. It illustrates how transfer of learning can be promoted in the classroom and everyday life.
Introduction To Transfer Learning
DOWNLOAD
Author : Jindong Wang
language : en
Publisher: Springer Nature
Release Date : 2023-03-30
Introduction To Transfer Learning written by Jindong Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-30 with Computers categories.
Transfer learning is one of the most important technologies in the era of artificial intelligence and deep learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics have attracted the interest of the research and application community: transfer learning, pre-training and fine-tuning, domain adaptation, domain generalization, and meta-learning. This book offers a comprehensive tutorial on an overview of transfer learning, introducing new researchers in this area to both classic and more recent algorithms. Most importantly, it takes a “student’s” perspective to introduce all the concepts, theories, algorithms, and applications, allowing readers to quickly and easily enter this area. Accompanying the book, detailed code implementations are provided to better illustrate the core ideas of several important algorithms, presenting good examples for practice.
Learning That Transfers
DOWNLOAD
Author : Julie Stern
language : en
Publisher: Corwin Press
Release Date : 2021-03-30
Learning That Transfers written by Julie Stern and has been published by Corwin Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-30 with Education categories.
"It is a pleasure to have a full length treatise on this most important topic, and may this focus on transfer become much more debated, taught, and valued in our schools." - John Hattie Teach students to use their learning to unlock new situations. How do you prepare your students for a future that you can’t see? And how do you do it without exhausting yourself? Teachers need a framework that allows them to keep pace with our rapidly changing world without having to overhaul everything they do. Learning That Transfers empowers teachers and curriculum designers alike to harness the critical concepts of traditional disciplines while building students’ capacity to navigate, interpret, and transfer their learning to solve novel and complex modern problems. Using a backwards design approach, this hands-on guide walks teachers step-by-step through the process of identifying curricular goals, establishing assessment targets, and planning curriculum and instruction that facilitates the transfer of learning to new and challenging situations. Key features include Thinking prompts to spur reflection and inform curricular planning and design. Next-day strategies that offer tips for practical, immediate action in the classroom. Design steps that outline critical moments in creating curriculum for learning that transfers. Links to case studies, discipline-specific examples, and podcast interviews with educators. A companion website that hosts templates, planning guides, and flexible options for adapting current curriculum documents. Using a framework that combines standards and the best available research on how we learn, design curriculum and instruction that prepares your students to meet the challenges of an uncertain future, while addressing the unique needs of your school community.
Transfer Learning
DOWNLOAD
Author :
language : en
Publisher: BoD – Books on Demand
Release Date : 2025-03-12
Transfer Learning written by and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-12 with Computers categories.
This edited volume explores the potential of transfer learning in advancing artificial intelligence (AI) across diverse domains. Transfer learning enables AI systems to leverage knowledge gained from one task to enhance performance in another, significantly reducing data requirements and training time while improving model efficiency. The book presents the latest approaches for implementing transfer learning in various contexts, from telecommunications and brain-computer interfaces to quantum computing applications. Readers will discover innovative techniques for domain adaptation, cross-domain knowledge transfer, and hybrid classical-quantum implementations. The text addresses critical challenges in making transfer learning more explainable, reliable, and scalable, particularly concerning privacy preservation and computational efficiency. Key topics include AI-native networks, neural network transfer learning, domain adaptation strategies, and quantum machine learning integration. Both theoretical frameworks and practical implementations are discussed, making this book valuable for researchers, practitioners, and students interested in developing more efficient and capable AI systems. The content bridges the gap between theoretical understanding and practical application, offering insights into how transfer learning can be effectively deployed in real-world scenarios. By examining transfer learning through multiple lenses, from traditional neural networks to quantum computing, this volume provides a unique perspective on the future of AI development and its potential to revolutionize various technological sectors.
Federated And Transfer Learning
DOWNLOAD
Author : Roozbeh Razavi-Far
language : en
Publisher: Springer Nature
Release Date : 2022-09-30
Federated And Transfer Learning written by Roozbeh Razavi-Far and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-30 with Technology & Engineering categories.
This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.
Transfer Learning Through Embedding Spaces
DOWNLOAD
Author : Mohammad Rostami
language : en
Publisher: CRC Press
Release Date : 2021-06-28
Transfer Learning Through Embedding Spaces written by Mohammad Rostami and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-28 with Computers categories.
Recent progress in artificial intelligence (AI) has revolutionized our everyday life. Many AI algorithms have reached human-level performance and AI agents are replacing humans in most professions. It is predicted that this trend will continue and 30% of work activities in 60% of current occupations will be automated. This success, however, is conditioned on availability of huge annotated datasets to training AI models. Data annotation is a time-consuming and expensive task which still is being performed by human workers. Learning efficiently from less data is a next step for making AI more similar to natural intelligence. Transfer learning has been suggested a remedy to relax the need for data annotation. The core idea in transfer learning is to transfer knowledge across similar tasks and use similarities and previously learned knowledge to learn more efficiently. In this book, we provide a brief background on transfer learning and then focus on the idea of transferring knowledge through intermediate embedding spaces. The idea is to couple and relate different learning through embedding spaces that encode task-level relations and similarities. We cover various machine learning scenarios and demonstrate that this idea can be used to overcome challenges of zero-shot learning, few-shot learning, domain adaptation, continual learning, lifelong learning, and collaborative learning.
Transfer Learning For Natural Language Processing
DOWNLOAD
Author : Paul Azunre
language : en
Publisher: Simon and Schuster
Release Date : 2021-08-31
Transfer Learning For Natural Language Processing written by Paul Azunre and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-31 with Computers categories.
Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems. Summary In Transfer Learning for Natural Language Processing you will learn: Fine tuning pretrained models with new domain data Picking the right model to reduce resource usage Transfer learning for neural network architectures Generating text with generative pretrained transformers Cross-lingual transfer learning with BERT Foundations for exploring NLP academic literature Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you’ll save on training time and computational costs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. About the book Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can practice your new skills immediately. As you go, you’ll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications. What's inside Fine tuning pretrained models with new domain data Picking the right model to reduce resource use Transfer learning for neural network architectures Generating text with pretrained transformers About the reader For machine learning engineers and data scientists with some experience in NLP. About the author Paul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. Table of Contents PART 1 INTRODUCTION AND OVERVIEW 1 What is transfer learning? 2 Getting started with baselines: Data preprocessing 3 Getting started with baselines: Benchmarking and optimization PART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS) 4 Shallow transfer learning for NLP 5 Preprocessing data for recurrent neural network deep transfer learning experiments 6 Deep transfer learning for NLP with recurrent neural networks PART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES 7 Deep transfer learning for NLP with the transformer and GPT 8 Deep transfer learning for NLP with BERT and multilingual BERT 9 ULMFiT and knowledge distillation adaptation strategies 10 ALBERT, adapters, and multitask adaptation strategies 11 Conclusions