Introduction To Transfer Learning
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Introduction To Transfer Learning
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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.
Transfer Learning For Natural Language Processing
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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 In Reinforcement Learning Domains
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Author : Matthew Taylor
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-06-05
Transfer In Reinforcement Learning Domains written by Matthew Taylor and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-05 with Computers categories.
In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity with the development of algorithms capable of mastering increasingly complex problems, but learning difficult tasks is often slow or infeasible when RL agents begin with no prior knowledge. The key insight behind "transfer learning" is that generalization may occur not only within tasks, but also across tasks. While transfer has been studied in the psychological literature for many years, the RL community has only recently begun to investigate the benefits of transferring knowledge. This book provides an introduction to the RL transfer problem and discusses methods which demonstrate the promise of this exciting area of research. The key contributions of this book are: Definition of the transfer problem in RL domains Background on RL, sufficient to allow a wide audience to understand discussed transfer concepts Taxonomy for transfer methods in RL Survey of existing approaches In-depth presentation of selected transfer methods Discussion of key open questions By way of the research presented in this book, the author has established himself as the pre-eminent worldwide expert on transfer learning in sequential decision making tasks. A particular strength of the research is its very thorough and methodical empirical evaluation, which Matthew presents, motivates, and analyzes clearly in prose throughout the book. Whether this is your initial introduction to the concept of transfer learning, or whether you are a practitioner in the field looking for nuanced details, I trust that you will find this book to be an enjoyable and enlightening read. Peter Stone, Associate Professor of Computer Science
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.
The Basis Of Transfer In Perceptual Skill Learning
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Author : Alfred Anthony Vieira
language : en
Publisher:
Release Date : 1991
The Basis Of Transfer In Perceptual Skill Learning written by Alfred Anthony Vieira and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Learning, Psychology of categories.
Transfer And Translation In Language Learning And Teaching
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Author : Franz Eppert
language : en
Publisher: 17 Q Q * *
Release Date : 1983
Transfer And Translation In Language Learning And Teaching written by Franz Eppert and has been published by 17 Q Q * * this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with Language Arts & Disciplines categories.
Introduction To Heat Transfer
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Author : Vedat S. Arpaci
language : en
Publisher: Pearson
Release Date : 1999
Introduction To Heat Transfer written by Vedat S. Arpaci and has been published by Pearson this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Science categories.
The philosophy of the text is based on the development of an inductive approach to the formulation and solution of applied problems. Explores the principle that heat transfer rests on, but goes beyond, thermodynamics. Ideal as an introduction to engineering heat transfer.
Unsupervised And Transfer Learning Challenges In Machine Learning
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Author : Isabelle Guyon
language : en
Publisher:
Release Date : 2013-06
Unsupervised And Transfer Learning Challenges In Machine Learning written by Isabelle Guyon and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06 with Computers categories.
From the Foreword: This book is a result of an international challenge on Unsupervised and Transfer Learning (UTL) that culminated in a workshop of the same name at the ICML-2011 conference in Bellevue, Washington, on July 2, 2011; it captures the best of the challenge findings and the most recent research presented at the workshop. The book is targeted for machine learning researchers and data mining practitioners interested in "lifelong machine learning systems" that retain the knowledge from prior learning to create more accurate models for new learning problems. Such systems will be of fundamental importance to intelligent software agents and robotics in the 21st century. The articles include new theories and new theoretically grounded algorithms applied to practical problems. It addressed an audience of experienced researchers in the field as well as Masters and Doctoral students undertaking research in machine learning. The book is organized in three major sections that can be read independently of each other. The introductory chapter is a survey on the state of the art of the field of unsupervised and transfer learning providing an overview of the book articles. The first section includes papers related to theoretical advances in deep learning, model selection and clustering. The second section presents articles by the challenge winners. The final section consists of the best articles from the ICML-2011 workshop; covering various approaches to and applications of unsupervised and transfer learning.
Introduction To Psychology
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Author : Ernest Ropiequet Hilgard
language : en
Publisher: Houghton Mifflin Harcourt P
Release Date : 1975
Introduction To Psychology written by Ernest Ropiequet Hilgard and has been published by Houghton Mifflin Harcourt P this book supported file pdf, txt, epub, kindle and other format this book has been release on 1975 with Psychology categories.
Introduces contemporary psychology to the beginning student.
Learning Organization Dimensions And Motivation To Transfer Learning In Large Firm Information Technology Employees
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Author : Toby Marshall Egan
language : en
Publisher:
Release Date : 2002
Learning Organization Dimensions And Motivation To Transfer Learning In Large Firm Information Technology Employees written by Toby Marshall Egan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with categories.