Informal Learning Basics

Informal Learning Basics

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  • Author: Saul Carliner
  • Publisher: Association for Talent Development
  • ISBN: 1607287862
  • Category : Business & Economics
  • Languages : en
  • Pages : 239

Informal Learning Basics provides training and development professionals with guidance and practical lessons on harnessing the vast potential of informal learning in their organizations. While formal training has been the focus of many corporate training programs for the past century or more, much of the actual knowledge and many of the skills workers use in performing their jobs are nonetheless developed informally. Informal Learning Basics will assist you in recognizing and utilizing the informal learning possibilities in your company, and will show you how to create a framework of highly cost-effective training opportunities and a culture in which your employees are able to learn and grow in an efficient and unobtrusive way. In addition to providing an in-depth study of the concepts of informal learning, Informal Learning Basics also offers: -an analysis of how workers develop much of the knowledge for their jobs informally -real-world case examples of informal learners -an examination of the nine principles which govern informal learning in the workplace -suggestions on how to blend formal and informal learning in your organization -descriptions of specific activities for both group and individual informal learning opportunities - a discussion of the importance of support personnel in creating and maintaining effective informal learning programs - an exploration of the significant role played by technology in informal learning - information on the importance of providing a codified framework for informal learning in your organization - a consideration of the fact that traditional approaches to evaluating training are often ineffective when evaluating informal learning, and suggestions on how to best evaluate informal learning programs. In an era where organizations of all shapes and sizes are increasingly focused on cutting budgets and maximizing the return on their training investment, incorporating informal learning opportunities into your training programs will result in competent and knowledgeable employees, and great ROI for your company. With its wealth of insight and information on capturing the potential of informal learning and using it to your organization’s advantage, Informal Learning Basics is essential reading for every training and development professional.


Deep Learning

Deep Learning

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  • Author: Ian Goodfellow
  • Publisher: MIT Press
  • ISBN: 0262337371
  • Category : Computers
  • Languages : en
  • Pages : 801

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.


Guide to Deep Learning Basics

Guide to Deep Learning Basics

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  • Author: Sandro Skansi
  • Publisher: Springer Nature
  • ISBN: 3030375919
  • Category : Computers
  • Languages : en
  • Pages : 140

This stimulating text/reference presents a philosophical exploration of the conceptual foundations of deep learning, presenting enlightening perspectives that encompass such diverse disciplines as computer science, mathematics, logic, psychology, and cognitive science. The text also highlights select topics from the fascinating history of this exciting field, including the pioneering work of Rudolf Carnap, Warren McCulloch, Walter Pitts, Bulcsú László, and Geoffrey Hinton. Topics and features: Provides a brief history of mathematical logic, and discusses the critical role of philosophy, psychology, and neuroscience in the history of AI Presents a philosophical case for the use of fuzzy logic approaches in AI Investigates the similarities and differences between the Word2vec word embedding algorithm, and the ideas of Wittgenstein and Firth on linguistics Examines how developments in machine learning provide insights into the philosophical challenge of justifying inductive inferences Debates, with reference to philosophical anthropology, whether an advanced general artificial intelligence might be considered as a living being Investigates the issue of computational complexity through deep-learning strategies for understanding AI-complete problems and developing strong AI Explores philosophical questions at the intersection of AI and transhumanism This inspirational volume will rekindle a passion for deep learning in those already experienced in coding and studying this discipline, and provide a philosophical big-picture perspective for those new to the field.


Learning How to Learn

Learning How to Learn

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  • Author: Barbara Oakley, PhD
  • Publisher: Penguin
  • ISBN: 052550446X
  • Category : Juvenile Nonfiction
  • Languages : en
  • Pages : 256

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.


Learn to Earn

Learn to Earn

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  • Author: Peter Lynch
  • Publisher: Simon and Schuster
  • ISBN: 1476712034
  • Category : Business & Economics
  • Languages : en
  • Pages : 272

Mutual-fund superstar Peter Lynch and author John Rothchild explain the basic principles of the stock market and business in an investing guide that will enlighten and entertain anyone who is high-school age or older. Many investors, including some with substantial portfolios, have only the sketchiest idea of how the stock market works. The reason, say Lynch and Rothchild, is that the basics of investing—the fundamentals of our economic system and what they have to do with the stock market—aren’t taught in school. At a time when individuals have to make important decisions about saving for college and 401(k) retirement funds, this failure to provide a basic education in investing can have tragic consequences. For those who know what to look for, investment opportunities are everywhere. The average high-school student is familiar with Nike, Reebok, McDonald’s, the Gap, and the Body Shop. Nearly every teenager in America drinks Coke or Pepsi, but only a very few own shares in either company or even understand how to buy them. Every student studies American history, but few realize that our country was settled by European colonists financed by public companies in England and Holland—and the basic principles behind public companies haven’t changed in more than three hundred years. In Learn to Earn, Lynch and Rothchild explain in a style accessible to anyone who is high-school age or older how to read a stock table in the daily newspaper, how to understand a company annual report, and why everyone should pay attention to the stock market. They explain not only how to invest, but also how to think like an investor.


Deep Learning

Deep Learning

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  • Author: Andrew Glassner
  • Publisher: No Starch Press
  • ISBN: 1718500734
  • Category : Computers
  • Languages : en
  • Pages : 1239

A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You'll learn how to use key deep learning algorithms without the need for complex math. Ever since computers began beating us at chess, they've been getting better at a wide range of human activities, from writing songs and generating news articles to helping doctors provide healthcare. Deep learning is the source of many of these breakthroughs, and its remarkable ability to find patterns hiding in data has made it the fastest growing field in artificial intelligence (AI). Digital assistants on our phones use deep learning to understand and respond intelligently to voice commands; automotive systems use it to safely navigate road hazards; online platforms use it to deliver personalized suggestions for movies and books - the possibilities are endless. Deep Learning: A Visual Approach is for anyone who wants to understand this fascinating field in depth, but without any of the advanced math and programming usually required to grasp its internals. If you want to know how these tools work, and use them yourself, the answers are all within these pages. And, if you're ready to write your own programs, there are also plenty of supplemental Python notebooks in the accompanying Github repository to get you going. The book's conversational style, extensive color illustrations, illuminating analogies, and real-world examples expertly explain the key concepts in deep learning, including: • How text generators create novel stories and articles • How deep learning systems learn to play and win at human games • How image classification systems identify objects or people in a photo • How to think about probabilities in a way that's useful to everyday life • How to use the machine learning techniques that form the core of modern AI Intellectual adventurers of all kinds can use the powerful ideas covered in Deep Learning: A Visual Approach to build intelligent systems that help us better understand the world and everyone who lives in it. It's the future of AI, and this book allows you to fully envision it. Full Color Illustrations


Beyond the Basic Stuff with Python

Beyond the Basic Stuff with Python

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  • Author: Al Sweigart
  • Publisher: No Starch Press
  • ISBN: 1593279663
  • Category : Computers
  • Languages : en
  • Pages : 385

BRIDGE THE GAP BETWEEN NOVICE AND PROFESSIONAL You've completed a basic Python programming tutorial or finished Al Sweigart's bestseller, Automate the Boring Stuff with Python. What's the next step toward becoming a capable, confident software developer? Welcome to Beyond the Basic Stuff with Python. More than a mere collection of advanced syntax and masterful tips for writing clean code, you'll learn how to advance your Python programming skills by using the command line and other professional tools like code formatters, type checkers, linters, and version control. Sweigart takes you through best practices for setting up your development environment, naming variables, and improving readability, then tackles documentation, organization and performance measurement, as well as object-oriented design and the Big-O algorithm analysis commonly used in coding interviews. The skills you learn will boost your ability to program--not just in Python but in any language. You'll learn: Coding style, and how to use Python's Black auto-formatting tool for cleaner code Common sources of bugs, and how to detect them with static analyzers How to structure the files in your code projects with the Cookiecutter template tool Functional programming techniques like lambda and higher-order functions How to profile the speed of your code with Python's built-in timeit and cProfile modules The computer science behind Big-O algorithm analysis How to make your comments and docstrings informative, and how often to write them How to create classes in object-oriented programming, and why they're used to organize code Toward the end of the book you'll read a detailed source-code breakdown of two classic command-line games, the Tower of Hanoi (a logic puzzle) and Four-in-a-Row (a two-player tile-dropping game), and a breakdown of how their code follows the book's best practices. You'll test your skills by implementing the program yourself. Of course, no single book can make you a professional software developer. But Beyond the Basic Stuff with Python will get you further down that path and make you a better programmer, as you learn to write readable code that's easy to debug and perfectly Pythonic Requirements: Covers Python 3.6 and higher


Deep Learning from the Basics

Deep Learning from the Basics

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  • Author: Koki Saitoh
  • Publisher: Packt Publishing Ltd
  • ISBN: 180020972X
  • Category : Computers
  • Languages : en
  • Pages : 317

Discover ways to implement various deep learning algorithms by leveraging Python and other technologies Key FeaturesLearn deep learning models through several activitiesBegin with simple machine learning problems, and finish by building a complex system of your ownTeach your machines to see by mastering the technologies required for image recognitionBook Description Deep learning is rapidly becoming the most preferred way of solving data problems. This is thanks, in part, to its huge variety of mathematical algorithms and their ability to find patterns that are otherwise invisible to us. Deep Learning from the Basics begins with a fast-paced introduction to deep learning with Python, its definition, characteristics, and applications. You'll learn how to use the Python interpreter and the script files in your applications, and utilize NumPy and Matplotlib in your deep learning models. As you progress through the book, you'll discover backpropagation—an efficient way to calculate the gradients of weight parameters—and study multilayer perceptrons and their limitations, before, finally, implementing a three-layer neural network and calculating multidimensional arrays. By the end of the book, you'll have the knowledge to apply the relevant technologies in deep learning. What you will learnUse Python with minimum external sources to implement deep learning programsStudy the various deep learning and neural network theoriesLearn how to determine learning coefficients and the initial values of weightsImplement trends such as Batch Normalization, Dropout, and AdamExplore applications like automatic driving, image generation, and reinforcement learningWho this book is for Deep Learning from the Basics is designed for data scientists, data analysts, and developers who want to use deep learning techniques to develop efficient solutions. This book is ideal for those who want a deeper understanding as well as an overview of the technologies. Some working knowledge of Python is a must. Knowledge of NumPy and pandas will be beneficial, but not essential.


Education: The Basics

Education: The Basics

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  • Author: Kay Wood
  • Publisher: Taylor & Francis
  • ISBN: 1136673687
  • Category : Education
  • Languages : en
  • Pages : 193

@text: Everyone knows that education is important, we are confronted daily by discussion of it in the media and by politicians, but how much do we really know about education? Education: The Basics is a lively and engaging introduction to education as an academic subject, taking into account both theory and practice. Covering the schooling system, the nature of knowledge and methods of teaching, this book analyses the viewpoints of both teachers and pupils. Key questions are answered, including: What is education and what is it for? Where does education take place? How do we learn? Who are the students? What is being taught in schools and universities and why? What is the state of education across the world? With further reading throughout, Education: The Basics is essential for all those embarking on undergraduate courses in Education and Education Studies, and for those with an involvement in teaching at all levels.


Basics of Language for Language Learners, 2nd Edition

Basics of Language for Language Learners, 2nd Edition

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  • Author: Peter W. Culicover
  • Publisher:
  • ISBN: 9780814254431
  • Category : Foreign Language Study
  • Languages : en
  • Pages : 264

Basics of Language for Language Learners, 2nd edition, by Peter W. Culicover and Elizabeth V. Hume, systematically explores all the aspects of language central to second language learning: the sounds of language, the different grammatical structures, the tools and strategies for learning, the social functions of communication, and the psychology of language learning and use.