Metalearning

Author : Pavel Brazdil
Publisher : Springer Science & Business Media
ISBN 13 : 3540732624
Total Pages : 182 pages
Book Rating : 4.24/5 ( download)

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Download or read book Metalearning written by Pavel Brazdil and published by Springer Science & Business Media. This book was released on 2008-11-26 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.

Author : Sudharsan Ravichandiran
Publisher : Packt Publishing Ltd
ISBN 13 : 1789537029
Total Pages : 218 pages
Book Rating : 4.24/5 ( download)

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Download or read book Hands-On Meta Learning with Python written by Sudharsan Ravichandiran and published by Packt Publishing Ltd. This book was released on 2018-12-31 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore a diverse set of meta-learning algorithms and techniques to enable human-like cognition for your machine learning models using various Python frameworks Key FeaturesUnderstand the foundations of meta learning algorithmsExplore practical examples to explore various one-shot learning algorithms with its applications in TensorFlowMaster state of the art meta learning algorithms like MAML, reptile, meta SGDBook Description Meta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster. Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning. By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models. What you will learnUnderstand the basics of meta learning methods, algorithms, and typesBuild voice and face recognition models using a siamese networkLearn the prototypical network along with its variantsBuild relation networks and matching networks from scratchImplement MAML and Reptile algorithms from scratch in PythonWork through imitation learning and adversarial meta learningExplore task agnostic meta learning and deep meta learningWho this book is for Hands-On Meta Learning with Python is for machine learning enthusiasts, AI researchers, and data scientists who want to explore meta learning as an advanced approach for training machine learning models. Working knowledge of machine learning concepts and Python programming is necessary.

Author : Frank Hutter
Publisher : Springer
ISBN 13 : 3030053180
Total Pages : 223 pages
Book Rating : 4.85/5 ( download)

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Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Author : Krzysztof Grąbczewski
Publisher : Springer
ISBN 13 : 3319009605
Total Pages : 349 pages
Book Rating : 4.05/5 ( download)

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Download or read book Meta-Learning in Decision Tree Induction written by Krzysztof Grąbczewski and published by Springer. This book was released on 2013-09-11 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book focuses on different variants of decision tree induction but also describes the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimental methodology and evaluation framework is provided. Meta-learning is discussed in great detail in the second half of the book. The exposition starts by presenting a comprehensive review of many meta-learning approaches explored in the past described in literature, including for instance approaches that provide a ranking of algorithms. The approach described can be related to other work that exploits planning whose aim is to construct data mining workflows. The book stimulates interchange of ideas between different, albeit related, approaches.

Author : Timothy Ferriss
Publisher : Houghton Mifflin Harcourt
ISBN 13 : 0547884591
Total Pages : 677 pages
Book Rating : 4.92/5 ( download)

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Download or read book The 4-hour Chef written by Timothy Ferriss and published by Houghton Mifflin Harcourt. This book was released on 2012 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building upon Timothy Ferriss's internationally successful "4-hour" franchise, The 4-Hour Chef transforms the way we cook, eat, and learn. Featuring recipes and cooking tricks from world-renowned chefs, and interspersed with the radically counterintuitive advice Ferriss's fans have come to expect, The 4-Hour Chef is a practical but unusual guide to mastering food and cooking, whether you are a seasoned pro or a blank-slate novice.

Author : Christian Rudolf Köpf
Publisher :
ISBN 13 :
Total Pages : 274 pages
Book Rating : 4.82/5 ( download)

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Download or read book Meta-learning written by Christian Rudolf Köpf and published by . This book was released on 2006 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Lan Zou
Publisher : Elsevier
ISBN 13 : 0323903703
Total Pages : 404 pages
Book Rating : 4.07/5 ( download)

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Download or read book Meta-Learning written by Lan Zou and published by Elsevier. This book was released on 2022-11-05 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep neural networks (DNNs) with their dense and complex algorithms provide real possibilities for Artificial General Intelligence (AGI). Meta-learning with DNNs brings AGI much closer: artificial agents solving intelligent tasks that human beings can achieve, even transcending what they can achieve. Meta-Learning: Theory, Algorithms and Applications shows how meta-learning in combination with DNNs advances towards AGI. Meta-Learning: Theory, Algorithms and Applications explains the fundamentals of meta-learning by providing answers to these questions: What is meta-learning?; why do we need meta-learning?; how are self-improved meta-learning mechanisms heading for AGI ?; how can we use meta-learning in our approach to specific scenarios? The book presents the background of seven mainstream paradigms: meta-learning, few-shot learning, deep learning, transfer learning, machine learning, probabilistic modeling, and Bayesian inference. It then explains important state-of-the-art mechanisms and their variants for meta-learning, including memory-augmented neural networks, meta-networks, convolutional Siamese neural networks, matching networks, prototypical networks, relation networks, LSTM meta-learning, model-agnostic meta-learning, and the Reptile algorithm. The book takes a deep dive into nearly 200 state-of-the-art meta-learning algorithms from top tier conferences (e.g. NeurIPS, ICML, CVPR, ACL, ICLR, KDD). It systematically investigates 39 categories of tasks from 11 real-world application fields: Computer Vision, Natural Language Processing, Meta-Reinforcement Learning, Healthcare, Finance and Economy, Construction Materials, Graphic Neural Networks, Program Synthesis, Smart City, Recommended Systems, and Climate Science. Each application field concludes by looking at future trends or by giving a summary of available resources. Meta-Learning: Theory, Algorithms and Applications is a great resource to understand the principles of meta-learning and to learn state-of-the-art meta-learning algorithms, giving the student, researcher and industry professional the ability to apply meta-learning for various novel applications. A comprehensive overview of state-of-the-art meta-learning techniques and methods associated with deep neural networks together with a broad range of application areas Coverage of nearly 200 state-of-the-art meta-learning algorithms, which are promoted by premier global AI conferences and journals, and 300 to 450 pieces of key research Systematic and detailed exploration of the most crucial state-of-the-art meta-learning algorithm mechanisms: model-based, metric-based, and optimization-based Provides solutions to the limitations of using deep learning and/or machine learning methods, particularly with small sample sizes and unlabeled data Gives an understanding of how meta-learning acts as a stepping stone to Artificial General Intelligence in 39 categories of tasks from 11 real-world application fields

Author : Abhishek Thakur
Publisher : Abhishek Thakur
ISBN 13 : 8269211508
Total Pages : 300 pages
Book Rating : 4.04/5 ( download)

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Download or read book Approaching (Almost) Any Machine Learning Problem written by Abhishek Thakur and published by Abhishek Thakur. This book was released on 2020-07-04 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is not a traditional book. The book has a lot of code. If you don't like the code first approach do not buy this book. Making code available on Github is not an option. This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along. Table of contents: - Setting up your working environment - Supervised vs unsupervised learning - Cross-validation - Evaluation metrics - Arranging machine learning projects - Approaching categorical variables - Feature engineering - Feature selection - Hyperparameter optimization - Approaching image classification & segmentation - Approaching text classification/regression - Approaching ensembling and stacking - Approaching reproducible code & model serving There are no sub-headings. Important terms are written in bold. I will be answering all your queries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book. To ask questions/doubts, visit this link: https://bit.ly/aamlquestions And Subscribe to my youtube channel: https://bit.ly/abhitubesub

Author : Philipp Koehn
Publisher : Cambridge University Press
ISBN 13 : 1108497322
Total Pages : 409 pages
Book Rating : 4.29/5 ( download)

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Download or read book Neural Machine Translation written by Philipp Koehn and published by Cambridge University Press. This book was released on 2020-06-18 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.

Author : Dr Arundhati Hoskeri
Publisher : Dr Arundhati G Hoskeri
ISBN 13 :
Total Pages : 140 pages
Book Rating : 4./5 ( download)

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Download or read book Master Meta Learning written by Dr Arundhati Hoskeri and published by Dr Arundhati G Hoskeri. This book was released on 2024-08-15 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you think Meta-learning is only useful in artificial intelligence and machine learning? Are you feeling overwhelmed that, in spite of your best efforts, you are unable to retain all the information? Do you think meta-learning techniques are only relevant for high-achievers? Is meta-learning only applicable to academic subjects and students? Many believe that meta-learning is time-consuming and difficult to implement and it is only useful for students. But the reality is that in meta-learning, you will learn to address common challenges and turn them into opportunities crucial for success in any field. No doubt, meta-learning has recently gained more popularity, but it is an age-old concept that explores optimizing learning strategies and improving educational and learning outcomes. In this book, you'll discover the secrets to learning faster, remembering more, and mastering any subject with ease. Say goodbye to boring study sessions and hello to a whole new world of accelerated learning! It will take you on a journey to becoming a master of learning. Knowing how to learn is crucial for lifelong growth and adaptability. Meta-learning is not just a method but an art and science of Learning how to learn. It's about understanding how you learn, adapting to different situations, and constantly improving.