Transfer Learning

Download or read book Transfer Learning written by Qiang Yang and published by Cambridge University Press. This book was released on 2020-02-13 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.

Download or read book Hands-On Transfer Learning with Python written by Dipanjan Sarkar and published by Packt Publishing Ltd. This book was released on 2018-08-31 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Transfer Learning for Natural Language Processing written by Paul Azunre and published by Simon and Schuster. This book was released on 2021-08-31 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Transfer of Learning written by Robert E. Haskell and published by Academic Press. This book was released on 2001 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Learning That Transfers written by Julie Stern and published by Corwin Press. This book was released on 2021-03-30 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: "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.

Download or read book Transfer of Learning written by Charles Hohensee and published by Springer Nature. This book was released on 2021-04-09 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a common language for and makes connections between transfer research in mathematics education and transfer research in related fields. It generates renewed excitement for and increased visibility of transfer research, by showcasing and aggregating leading-edge research from the transfer research community. This book also helps to establish transfer as a sub-field of research within mathematics education and extends and refines alternate perspectives on the transfer of learning. The book provides an overview of current knowledge in the field as well as informs future transfer research.

Download or read book Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques written by Olivas, Emilio Soria and published by IGI Global. This book was released on 2009-08-31 with total page 852 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book investiges machine learning (ML), one of the most fruitful fields of current research, both in the proposal of new techniques and theoretic algorithms and in their application to real-life problems"--Provided by publisher.