Learning In Action

REAL CHANGE IN REAL TIME--THE ULTIMATE GUIDE TO WORKING WITH PEOPLE'S STORIES IN COACHING This is a rare book; it is grounded in both a deep academic rigor and a deep personal understanding of how people change. It is a treasure chest of information and insights based in over twenty years of experience. It will enable you to get to the crux of people's issues in less time and help them make significant shifts in the moment. This book is an indispensable resource for anyone who works with people's stories and wants to develop themselves so they have more impact. The tools and models are presented in simple and clear language. However, there is a depth here that offers a limitless guide for your learning. Narrative Coaching is timely because it works at the level of identities, addresses the collective narratives that shape our stories, and expands the roles and modalities we can use to bring about transformational change with individuals and teams. What is new in this edition: It goes deeper into attachment theory and applied mindfulness It offers design thinking as a framework for adult development It shows how change is a naturally human and integrative process It offers more examples and cases, e.g., how to coach without goals This book will both challenge you and inspire you to think in new ways about what is possible in your life and in your practice.

Shift to blended learning to transform education Blended learning has the power to reinvent education, but the transition requires a new approach to learning and a new skillset for educators. Loaded with research and examples, Blended Learning in Action demonstrates the advantages a blended model has over traditional instruction when technology is used to engage students both inside the classroom and online. Readers will find: Breakdowns of the most effective classroom setups for blended learning Tips for leaders Ideas for personalizing and differentiating instruction using technology Strategies for managing devices in schools Questions to facilitate professional development and deeper learning

Most managers today understand the value of building a learning organization. Their goal is to leverage knowledge and make it a key corporate asset, yet they remain uncertain about how best to get started. What they lack are guidelines and tools that transform abstract theory—the learning organization as an ideal—into hands-on implementation. For the first time in Learning in Action, David Garvin helps managers make the leap from theory to proven practice. Garvin argues that at the heart of organizational learning lies a set of processes that can be designed, deployed, and led. He starts by describing the basic steps in every learning process—acquiring, interpreting, and applying knowledge—then examines the critical challenges facing managers at each of these stages and the various ways the challenges can be met. Drawing on decades of scholarship and a wealth of examples from a wide range of fields, Garvin next introduces three modes of learning—intelligence gathering, experience, and experimentation—and shows how each mode is most effectively deployed. These approaches are brought to life in complete, richly detailed case studies of learning in action at organizations such as Xerox, L. L. Bean, the U. S. Army, and GE. The book concludes with a discussion of the leadership role that senior executives must play to make learning a day-to-day reality in their organizations.

Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce

Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's inside Building and training DRL networks The most popular DRL algorithms for learning and problem solving Evolutionary algorithms for curiosity and multi-agent learning All examples available as Jupyter Notebooks About the reader For readers with intermediate skills in Python and deep learning. About the author Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger. Table of Contents PART 1 - FOUNDATIONS 1. What is reinforcement learning? 2. Modeling reinforcement learning problems: Markov decision processes 3. Predicting the best states and actions: Deep Q-networks 4. Learning to pick the best policy: Policy gradient methods 5. Tackling more complex problems with actor-critic methods PART 2 - ABOVE AND BEYOND 6. Alternative optimization methods: Evolutionary algorithms 7. Distributional DQN: Getting the full story 8.Curiosity-driven exploration 9. Multi-agent reinforcement learning 10. Interpretable reinforcement learning: Attention and relational models 11. In conclusion: A review and roadmap

Recently at the Visible Learning Conference, Professor John Hattie stood up in his opening address and said, "I’m looking at you all and thinking ‘What if I got this wrong?’" I feel the same way when educators ask to visit and I always end up in the same place – that Keilor Views is a living, breathing example that he didn’t. -- Charles Branciforte, Principal of Keilor Views Primary School, Melbourne, Australia Visible Learning into Action takes the next step in the evolving Visible Learning story. It translates one of the biggest and most critically acclaimed education research projects ever undertaken into case studies of actual success stories, implementing John Hattie’s ideas in the classrooms of schools all around the world. The evidenced case studies presented in this book describe the Visible Learning journeys of fifteen schools from Australia, USA, Hong Kong, UK, Sweden, New Zealand and Norway and are representative of the VL international community of schools in their quest to ensure all of their students exceed their potential for academic success. Each school’s story will inform and inspire, bringing to life the discussions, actions and reflections from leaders, teachers, students and families. This book features extensive, interactive appendices containing study guide questions to encourage critical thinking, annotated endnotes with recommendations for further reading and links to YouTube and relevant websites. Drawing on the latest research into the major principles and strategies of learning, this essential resource is structured into five parts: Know thy impact; Effective feedback; Visible learners; Inspired and passionate teachers; The Visible Learning School. Visible Learning into Action is aimed at any student, teacher or parent requiring an up-to-date commentary on how research into human learning processes can inform our teaching and what goes on in our schools.

Marquardt brings together the six essential elements with realistic advice, practical wisdom, and such tools as checklists and a comprehensive glossary of terms. Readers can learn to leverage action learning to solve problems, develop employees, enhance personal growth, and create organizational learning.

Since the concept of lifelong learning came to prominence much excellent work has been undertaken but, as Professor Longworth's new book shows, major change in some areas is still needed if the concept of learning from cradle to grave is to become a true reality. Using his unique vantage point from consulting with schools, universities, local, governmental and global authorities, Professor Longworth brings the development of lifelong learning bang up-to-date with a complete survey of the principles of lifelong learning including examples from around the world and crucial information on the impact of lifelong learning on 21st century schools.