Education

Successful Learning Simplified

Successful Learning Simplified

Author: Fiona McPherson

Publisher: Wayz Press

ISBN: 9781927166222

Category: Education

Page: 205

View: 674

Being smart helps. Working hard helps more. But working effectively helps most of all. There's a great deal that research can now tell us about how to study effectively. For 20 years, I've been reporting on research into memory and learning, and explaining how these findings can be translated into practical advice. In earlier books, I have presented a lot of detail on strategies in specific areas: note-taking, mnemonics, practice and revision. In this guide, I attempt to provide an overview of how to approach your learning, and the strategies you should consider using. The book covers: * preparing for learning * approaching a text * reading * taking notes * building understanding * navigating non-linear environments * dealing with lectures * memorizing * revising * building expertise in skills * how specific subjects differ in their needs & demands. The book can be used as an adjunct and quick reference for those who have the more in-depth workbooks, or as a simplified guide for those who want the bottom-line without the detail. This is not a book for students who want a magic bullet, who want a five-minute 'answer' to effortless study! But students who want to know that their time and effort are being used wisely, that their diligence will be rewarded with better marks and more long-lasting learning, this guide may be the answer they've long been looking for. Keywords: best study strategies for college students, textbook reading strategies for college, study skills, college study, successful learning

Successful Learning Simplified : a Visual Guide

Successful Learning Simplified : a Visual Guide

Author:

Publisher:

ISBN: OCLC:957299137

Category:

Page:

View: 634

Study Skills Box Set

Study Skills Box Set

Author: Fiona McPherson

Publisher:

ISBN: OCLC:1309297962

Category:

Page:

View: 383

A successful student uses effective strategies. This box-set includes 4 workbooks on study skills. The first looks at taking notes -- a broad category that encompasses many strategies, not simply the obvious ones such as how to format your notes, use headings and highlighting, how to summarize, how to review your notes, but also the more complex ones of how to evaluate text to work out which strategy is appropriate, and how to ask the right questions. The second book in the series looks at the use of mnemonics in study, for when memorization of specific details is required. The third explains how to effectively revise, and cement your learning. The fourth provides an overview of how to approach your learning, and the strategies you should consider using. You can find out more about each of these books under their individual titles.
Education

How to Learn

How to Learn

Author: Fiona McPherson

Publisher: Wayz Press

ISBN: 9781927166123

Category: Education

Page: 246

View: 707

Working ‘hard’ is not enough. To be an effective student, you need to work ‘smart’. This book is for students who are serious about being successful in study, and teachers who want to know how best to help their students learn. For being a successful student is far more about being a smart user of effective strategies than about being 'smart'. In Effective Notetaking and Mnemonics for Study, Dr McPherson showed readers many strategies for improving understanding and memory. But these on their own can only take you so far, if you don’t know how to cement that information into your brain for the long term. In this new book, Dr McPherson explains the 10 principles of effective practice and revision. Few students know how to revise effectively, which is why they waste so much time going over and over material, as they try to hammer it into their heads. But you don’t need to spend all that time, and you don’t need to endure such boredom. What you need to do is understand how to review your learning in the most effective way. Using examples from science, math, history, foreign languages, and skill learning, that is what this book aims to teach you. This book will tell you * what you should practice or revise * how you should practice * how often you should practice * how far apart you should schedule your sessions * different strategies you can use in your practice * how skill learning differs from 'fact' learning and more. As always with the Mempowered books, this book uses the latest cognitive and educational research to show you what to do to maximize your learning. Keywords: how to revise effectively, deliberate practice book, deliberate practice in education, best study strategies for college students, learning a skill
Computers

scikit-learn : Machine Learning Simplified

scikit-learn : Machine Learning Simplified

Author: Raul Garreta

Publisher: Packt Publishing Ltd

ISBN: 9781788831529

Category: Computers

Page: 530

View: 151

Implement scikit-learn into every step of the data science pipeline About This Book Use Python and scikit-learn to create intelligent applications Discover how to apply algorithms in a variety of situations to tackle common and not-so common challenges in the machine learning domain A practical, example-based guide to help you gain expertise in implementing and evaluating machine learning systems using scikit-learn Who This Book Is For If you are a programmer and want to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this is the course for you. No previous experience with machine-learning algorithms is required. What You Will Learn Review fundamental concepts including supervised and unsupervised experiences, common tasks, and performance metrics Classify objects (from documents to human faces and flower species) based on some of their features, using a variety of methods from Support Vector Machines to Naive Bayes Use Decision Trees to explain the main causes of certain phenomena such as passenger survival on the Titanic Evaluate the performance of machine learning systems in common tasks Master algorithms of various levels of complexity and learn how to analyze data at the same time Learn just enough math to think about the connections between various algorithms Customize machine learning algorithms to fit your problem, and learn how to modify them when the situation calls for it Incorporate other packages from the Python ecosystem to munge and visualize your dataset Improve the way you build your models using parallelization techniques In Detail Machine learning, the art of creating applications that learn from experience and data, has been around for many years. Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility; moreover, within the Python data space, scikit-learn is the unequivocal choice for machine learning. The course combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. The course starts by walking through different methods to prepare your data—be it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, you'll learn different techniques aligned with different objectives—be it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, you'll learn how to polish your algorithm to ensure that it's both accurate and resilient to new datasets. You will learn to incorporate machine learning in your applications. Ranging from handwritten digit recognition to document classification, examples are solved step-by-step using scikit-learn and Python. By the end of this course you will have learned how to build applications that learn from experience, by applying the main concepts and techniques of machine learning. Style and Approach Implement scikit-learn using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach. This is a practical course, which analyzes compelling data about life, health, and death with the help of tutorials. It offers you a useful way of interpreting the data that's specific to this course, but that can also be applied to any other data. This course is designed to be both a guide and a reference for moving beyond the basics of scikit-learn.
Education

Mnemonics for Study (2nd ed.)

Mnemonics for Study (2nd ed.)

Author: Fiona McPherson

Publisher: Wayz Press

ISBN: 9781927166444

Category: Education

Page: 247

View: 274

Dr McPherson explains how to effectively use mnemonic strategies when studying, based on the latest cognitive and educational research. This 2nd edition includes a lengthy and in-depth case study showing step by step how to apply mnemonics to a study topic.
Computers

Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design

Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design

Author: Nan Zheng

Publisher: John Wiley & Sons

ISBN: 9781119507383

Category: Computers

Page: 296

View: 240

Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities—and provides co-design and co-optimization methodologies for building hardware neural networks that can learn. Presenting a complete picture from high-level algorithm to low-level implementation details, Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design also covers many fundamentals and essentials in neural networks (e.g., deep learning), as well as hardware implementation of neural networks. The book begins with an overview of neural networks. It then discusses algorithms for utilizing and training rate-based artificial neural networks. Next comes an introduction to various options for executing neural networks, ranging from general-purpose processors to specialized hardware, from digital accelerator to analog accelerator. A design example on building energy-efficient accelerator for adaptive dynamic programming with neural networks is also presented. An examination of fundamental concepts and popular learning algorithms for spiking neural networks follows that, along with a look at the hardware for spiking neural networks. Then comes a chapter offering readers three design examples (two of which are based on conventional CMOS, and one on emerging nanotechnology) to implement the learning algorithm found in the previous chapter. The book concludes with an outlook on the future of neural network hardware. Includes cross-layer survey of hardware accelerators for neuromorphic algorithms Covers the co-design of architecture and algorithms with emerging devices for much-improved computing efficiency Focuses on the co-design of algorithms and hardware, which is especially critical for using emerging devices, such as traditional memristors or diffusive memristors, for neuromorphic computing Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design is an ideal resource for researchers, scientists, software engineers, and hardware engineers dealing with the ever-increasing requirement on power consumption and response time. It is also excellent for teaching and training undergraduate and graduate students about the latest generation neural networks with powerful learning capabilities.
Computers

Connectionist Models of Development

Connectionist Models of Development

Author: Philip T. Quinlan

Publisher: Taylor & Francis

ISBN: 1841692689

Category: Computers

Page: 386

View: 203

Connectionist Models of Development is an edited collection of essays on the current work concerning connectionist or neural network models of human development. The brain comprises millions of nerve cells that share myriad connections, and this book looks at how human development in these systems is typically characterised as adaptive changes to the strengths of these connections. The traditional accounts of connectionist learning, based on adaptive changes to weighted connections, are explored alongside the dynamic accounts in which networks generate their own structures as learning proceeds. Unlike most connectionist accounts of psychological processes which deal with the fully-mature system, this text brings to the fore a discussion of developmental processes. To investigate human cognitive and perceptual development, connectionist models of learning and representation are adopted alongside various aspects of language and knowledge acquisition. There are sections on artificial intelligence and how computer programs have been designed to mimic the development processes, as well as chapters which describe what is currently known about how real brains develop. This book is a much-needed addition to the existing literature on connectionist development as it includes up-to-date examples of research on current controversies in the field as well as new features such as genetic connectionism and biological theories of the brain. It will be invaluable to academic researchers, post-graduates and undergraduates in developmental psychology and those researching connectionist/neural networks as well as those in related fields such as psycholinguistics.
Education

Teaching for Effective Learning in Higher Education

Teaching for Effective Learning in Higher Education

Author: N. Hativa

Publisher: Springer Science & Business Media

ISBN: 079236662X

Category: Education

Page: 380

View: 785

This book identifies strategies that are consistently associated with good teaching and presents them within a theoretical framework that explains how they promote students' active and meaningful learning. The book promotes teachers' pedagogical knowledge and their perception of teaching as scholarly, intellectual work, and provides extensive practical advice.
Education

Learning Theories Simplified

Learning Theories Simplified

Author: Bob Bates

Publisher: SAGE

ISBN: 9781526468635

Category: Education

Page: 384

View: 319

Are you struggling to get your head around John Dewey’s educational pragmatism? What exactly is Jean Piaget saying about cognitive development? Maybe you’re running out of time and patience making sense of Carol Dweck’s mindsets? Have you reached breaking point reading Daniel T. Willingham on educational neuroscience? Written for busy teachers, trainers, managers and students, this 'dip-in, dip-out' guide makes theories of learning accessible and practical. It explores 130 classic and contemporary learning theorists in an easy-to-use, bite-sized format with clear relevant illustrations on how each theory will benefit teaching and learning. Each model or theory is explained in less than 350 words, followed by a 'how to use it' section. What's new to this edition: A new early childhood theorists section A new communication theories section Additional ‘on trend’ theorists throughout New ‘critical view’ features added to each entry.
Education

Online Tutor 2.0: Methodologies and Case Studies for Successful Learning

Online Tutor 2.0: Methodologies and Case Studies for Successful Learning

Author: García-Peñalvo, Francisco José

Publisher: IGI Global

ISBN: 9781466658332

Category: Education

Page: 384

View: 123

After centuries of rethinking education and learning, the current theory is based on technology’s approach to and affect on the planned interaction between knowledge trainers and trainees. Online Tutor 2.0: Methodologies and Case Studies for Successful Learning demonstrates, through the exposure of successful cases in online education and training, the necessity of the human factor, particularly in teaching/tutoring roles, for ensuring the development of quality and excellent learning activities. The didactic patterns derived from these experiences and methodologies will provide a basis for a more powerful and efficient new generation of technology-based learning solutions for high school teachers, university professors, researchers, and students at all levels of education.
Self-Help

Make Your Own Memory Journal

Make Your Own Memory Journal

Author: Fiona McPherson

Publisher: Wayz Press

ISBN: 9781927166321

Category: Self-Help

Page: 44

View: 394

A digital version of My Memory Journal, enabling you to turn a blank notebook into a memory journal, with all the memory tips and focal topics