Learn Faster Meta Learning

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Learn Faster Meta Learning

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Author : Naushad Sheikh
language : en
Publisher: Naushad Sheikh
Release Date : 2025-05-21

Learn Faster Meta Learning written by Naushad Sheikh and has been published by Naushad Sheikh this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-21 with Education categories.

What if you could master any skill in half the time—and actually remember it? In a world where change is constant and competition is fierce, your ability to learn faster is your most powerful advantage. Learn Faster is not just a book. It’s a science-backed system to rewire how you think, absorb, and apply knowledge in real life. Whether you’re a student, entrepreneur, career professional, or curious mind, this book will help you break learning plateaus, overcome frustration, and become unstoppable. Inside, you'll discover: 1. How to activate your brain’s natural “learning circuits” for faster recall and deeper understanding 2. Proven techniques like spaced repetition, active recall, and mental modeling — explained without jargon 3. How to stay motivated and focused even when learning gets tough 4. How to turn mistakes into superpowers using feedback loops and memory science 5. The meta-skills that top performers use to adapt in fast-changing industries — and how you can too 6. The latest tools in AI, neurotech, and personalized learning that are shaping the future of education Based on neuroscience, psychology, and real-world application, this book is a clear, conversational guide that feels like a personal mentor in your pocket. Whether you want to learn a new language, master coding, accelerate your career growth, or simply sharpen your brain — this book shows you how.

Automated Machine Learning

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Author : Frank Hutter
language : en
Publisher: Springer
Release Date : 2019-05-17

Automated Machine Learning written by Frank Hutter and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-17 with Computers categories.

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.

Neural Machine Translation

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Author : Philipp Koehn
language : en
Publisher: Cambridge University Press
Release Date : 2020-06-18

Neural Machine Translation written by Philipp Koehn 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 2020-06-18 with Computers categories.

Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.

Approaching Almost Any Machine Learning Problem

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Author : Abhishek Thakur
language : en
Publisher: Abhishek Thakur
Release Date : 2020-07-04

Approaching Almost Any Machine Learning Problem written by Abhishek Thakur and has been published by Abhishek Thakur this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-04 with Computers categories.

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

Learning How To Learn

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Author : Barbara Oakley, PhD
language : en
Publisher: Penguin
Release Date : 2018-08-07

Learning How To Learn written by Barbara Oakley, PhD and has been published by Penguin this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-07 with Juvenile Nonfiction categories.

A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course "Learning How to Learn" have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid "rut think" in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun.

Metalearning

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Author : Pavel Brazdil
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-11-26

Metalearning written by Pavel Brazdil 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 2008-11-26 with Computers categories.

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.

Metaskills

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Author : Marty Neumeier
language : en
Publisher: New Riders
Release Date : 2012-12-20

Metaskills written by Marty Neumeier and has been published by New Riders this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-20 with Business & Economics categories.

In a sweeping vision for the future of work, Neumeier shows that the massive problems of the 21st century are largely the consequence of a paradigm shift—a shuddering gear-change from the familiar Industrial Age to the unfamiliar “Robotic Age,” an era of increasing man-machine collaboration. This change is creating the “Robot Curve,” an accelerating waterfall of obsolescence and opportunity that is currently reshuffling the fortunes of workers, companies, and national economies. It demonstrates how the cost and value of a unit of work go down as it moves from creative to skilled to rote, and, finally, to robotic. While the Robot Curve is dangerous to those with brittle or limited skills, it offers unlimited potential to those with metaskills—master skills that enable other skills. Neumeier believes that the metaskills we need in a post-industrial economy are feeling (intuition and empathy), seeing (systems thinking), dreaming (applied imagination), making (design), and learning (autodidactics). These are not the skills we were taught in school. Yet they’re the skills we’ll need to harness the curve. In explaining each of the metaskills, he offers encouragement and concrete advice for mastering their intricacies. At the end of the book he lays out seven changes that education can make to foster these important talents. This is a rich, exciting book for forward-thinking educators, entrepreneurs, designers, artists, scientists, and future leaders in every field. It comes illustrated with clear diagrams and a 16-page color photo essay. Those who enjoy this book may be interested in its slimmer companion, The 46 Rules of Genius, also by Marty Neumeier. Things you’ll learn in Metaskills: - How to stay ahead of the “robot curve” - How to account for “latency” in your predictions - The 9 most common traps of systems behavior - How to distinguish among 4 types of originality - The 3 key steps in generating innovative solutions - 6 ways to think like Steve Jobs - How to recognize the 3 essential qualities of beauty - 24 aesthetic tools you can apply to any kind of work - 10 strategies to trigger breakthrough ideas - Why every team needs an X-shaped person - How to overcome the 5 forces arrayed against simplicity - 6 tests for measuring the freshness of a concept - How to deploy the 5 principles of “uncluding” - The 10 tests for measuring great work - How to sell an innovative concept to an organization - 12 principles for constructing a theory of learning - How to choose a personal mission for the real world - The 4 levels of professional achievement - 7 steps for revolutionizing education From the back cover "Help! A robot ate my job!" If you haven't heard this complaint yet, you will. Today's widespread unemployment is not a jobs crisis. It's a talent crisis. Technology is taking every job that doesn't need a high degree of creativity, humanity, or leadership. The solution? Stay on top of the Robot Curve--a constant waterfall of obsolescence and opportunity fed by competition and innovation. Neumeier presents five metaskills--feeling, seeing, dreaming, making, and learning--that will accelerate your success in the Robotic Age.

Learning To Learn

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Author : Sebastian Thrun
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Learning To Learn written by Sebastian Thrun 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 2012-12-06 with Computers categories.

Over the past three decades or so, research on machine learning and data mining has led to a wide variety of algorithms that learn general functions from experience. As machine learning is maturing, it has begun to make the successful transition from academic research to various practical applications. Generic techniques such as decision trees and artificial neural networks, for example, are now being used in various commercial and industrial applications. Learning to Learn is an exciting new research direction within machine learning. Similar to traditional machine-learning algorithms, the methods described in Learning to Learn induce general functions from experience. However, the book investigates algorithms that can change the way they generalize, i.e., practice the task of learning itself, and improve on it. To illustrate the utility of learning to learn, it is worthwhile comparing machine learning with human learning. Humans encounter a continual stream of learning tasks. They do not just learn concepts or motor skills, they also learn bias, i.e., they learn how to generalize. As a result, humans are often able to generalize correctly from extremely few examples - often just a single example suffices to teach us a new thing. A deeper understanding of computer programs that improve their ability to learn can have a large practical impact on the field of machine learning and beyond. In recent years, the field has made significant progress towards a theory of learning to learn along with practical new algorithms, some of which led to impressive results in real-world applications. Learning to Learn provides a survey of some of the most exciting new research approaches, written by leading researchers in the field. Its objective is to investigate the utility and feasibility of computer programs that can learn how to learn, both from a practical and a theoretical point of view.

Artificial Intelligence

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Author :
language : en
Publisher: BoD – Books on Demand
Release Date : 2021-09-01

Artificial Intelligence 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 2021-09-01 with Computers categories.

Artificial Intelligence (AI) is widely known as a knowledge field that aims to make computers, robots, or products that mimic the way humans think. In the current scientific community, AI is an intensively studied area composed of multiple branches. Historically, machine learning and optimization are two of the most studied fronts thanks to the development of novel and challenging research topics such as transfer optimization, swarm robotics, and drift detection and adaptation to evolving conditions in real-time. This book collects radically new theoretical insights, reporting recent developments and evincing innovative applications regarding AI methods in all fields of knowledge. It also presents works focused on new paradigms and novel branches of AI science.

Deep Reinforcement Learning

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Author : Hao Dong
language : en
Publisher: Springer Nature
Release Date : 2020-06-29

Deep Reinforcement Learning written by Hao Dong and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.

Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.