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Author: Erik M. Francis
Publisher: Solution Tree Press
ISBN: 1951075161
Category : Education
Languages : en
Pages : 177
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Book Description
Depth of knowledge (DOK) has become a priority for many schools. But if your understanding of DOK is a little cloudy, you're not alone. This resource is your one-stop-shop for learning what it is, who it's for, and how to use and sustain it. Ultimately, you will discover how to plan and provide learning experiences that are academically rigorous, socially and emotionally supportive, and student responsive. Learn how DOK is a different, deeper way of approaching teaching and learning. Explore the different DOK levels and how they relate to instruction. Understand DOK's relationship with standards and assessment. Designate correct levels based on learning needs. Acquire strategies for helping students engage with DOK on a deeper level. Contents: Introduction: What Depth of Knowledge Is Not Chapter 1: What Exactly Is Depth of Knowledge? Chapter 2: What Are DOK Teaching and Learning Experiences? Chapter 3: How to Teach and Learn for Depth of Knowledge Chapter 4: How to Use Webb's DOK Levels as a Multi-Tiered System of Support Chapter 5: How to Deconstruct Learning Intentions for Depth of Knowledge Chapter 6: How to Designate the Depth of Knowledge Level Demanded Chapter 7: How to Construct DOK Learning Targets and Success Criteria Chapter 8: How to Ask and Address Good Questions for Depth of Knowledge Chapter 9: Let's Make a DOK! Conclusion References and Resources Index
Author: Erik M. Francis
Publisher: Solution Tree Press
ISBN: 1951075161
Category : Education
Languages : en
Pages : 177
Get Book Here
Book Description
Depth of knowledge (DOK) has become a priority for many schools. But if your understanding of DOK is a little cloudy, you're not alone. This resource is your one-stop-shop for learning what it is, who it's for, and how to use and sustain it. Ultimately, you will discover how to plan and provide learning experiences that are academically rigorous, socially and emotionally supportive, and student responsive. Learn how DOK is a different, deeper way of approaching teaching and learning. Explore the different DOK levels and how they relate to instruction. Understand DOK's relationship with standards and assessment. Designate correct levels based on learning needs. Acquire strategies for helping students engage with DOK on a deeper level. Contents: Introduction: What Depth of Knowledge Is Not Chapter 1: What Exactly Is Depth of Knowledge? Chapter 2: What Are DOK Teaching and Learning Experiences? Chapter 3: How to Teach and Learn for Depth of Knowledge Chapter 4: How to Use Webb's DOK Levels as a Multi-Tiered System of Support Chapter 5: How to Deconstruct Learning Intentions for Depth of Knowledge Chapter 6: How to Designate the Depth of Knowledge Level Demanded Chapter 7: How to Construct DOK Learning Targets and Success Criteria Chapter 8: How to Ask and Address Good Questions for Depth of Knowledge Chapter 9: Let's Make a DOK! Conclusion References and Resources Index
Author: Jeremy Howard
Publisher: O'Reilly Media
ISBN: 1492045497
Category : Computers
Languages : en
Pages : 624
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Book Description
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Author: Joel Smith
Publisher: Createspace Independent Publishing Platform
ISBN: 9781720694625
Category :
Languages : en
Pages : 342
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Book Description
In training speed, the most elusive athletic quality, what really works, and what doesn't? What are the components of sprinting itself that elites do, where amateurs falter? And how can we arrange all of this in a training medium that facilitates high performance? Speed Strength is the product of hundreds of athletes trained, thousands of hours of study, dozens of expert mentors, and an open mind. If you are interested in the answer to these questions, then Speed Strength is the book that holds the answers you are looking for, and much more.Joel Smith is the host of the Just Fly Performance Podcast and a track and strength coach of 13 years. As a track coach to national champions and Olympic champions as a strength coach, Joel has seen the elements of speed inside and out. Featuring the insight of over 25 field leaders, Speed Strength touches on the most important trainable elements of sprinting in a holistic and thorough manner.
Author: Barbara Oakley, PhD
Publisher: Penguin
ISBN: 052550446X
Category : Juvenile Nonfiction
Languages : en
Pages : 258
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Book Description
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.
Author: Matthew F. Dixon
Publisher: Springer Nature
ISBN: 3030410684
Category : Business & Economics
Languages : en
Pages : 565
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Book Description
This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
Author: De-Shuang Huang
Publisher: Springer Nature
ISBN: 981975612X
Category :
Languages : en
Pages : 513
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Book Description
Author: Dave Mearns
Publisher: SAGE
ISBN: 1526416913
Category : Psychology
Languages : en
Pages : 237
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Book Description
Eagerly awaited by many counsellors and psychotherapists, this new edition includes an updated preface, new content on recent research and new developments and debates around relational depth, and new case studies. This groundbreaking text goes to the very heart of the therapeutic meeting between therapist and client. Focusing on the concept of ′relational depth′, the authors describe a form of encounter in which therapist and client experience profound feelings of contact and engagement with each other, and in which the client has an opportunity to explore whatever is experienced as most fundamental to her or his existence. The book has helped thousands of trainees and practitioners understand how to facilitate a relationally-deep encounter, identify the personal ‘blocks’ that may be encountered along the way, and consider new therapeutic concepts – such as ′holistic listening′ – that help them to meet their clients at this level. This classic text remains a source of fresh thinking and stimulating ideas about the therapeutic encounter which is relevant to trainees and practitioners of all orientations.
Author: Eric Goodman
Publisher: HarperCollins
ISBN: 006299610X
Category : Health & Fitness
Languages : en
Pages : 286
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Book Description
The Founder of the proven Foundation Training program takes his teaching to the next phase, showing us how to utilize our body’s built-in systems for healing and introducing a new program that offers a perpetual inner core of wellness and adaptability. Dr. Eric Goodman’s innovative approach to self-healing—Foundation Training—has helped athletes, first responders, celebrities, and regular folks around the world. The heart of Foundation Training is a unique form of biomechanics—a series of postures, poses, and movements designed to teach the body’s individual muscles to act within strong, flexible chains, shifting the burden of support away from sensitive joints. Foundations of Health builds on this core program, going deep into its principles to help us understand how to maintain a healthy body, even when the mechanics eventually break down. Our bodies are built to heal themselves—without surgeries and prescriptive medications. The protocols expand on the original Foundation Training concepts, focusing on the endogenous cannabinoid stimulators—part of an extraordinary built-in endocannabinoid system that profoundly affects our central, enteric, and peripheral nervous systems and helps to regulate numerous responses in our body. Dr. Goodman explains the science behind the endogenous cannabinoid system and how it can be stimulated in natural and healthy ways, including heat, breath work, and movement—techniques that will help guide and maintain the state of balance the body needs to function optimally with stability and harmony. He recommends foods, herbs, and supplements likely to ease pain, lower stress, and boost mental and physical function. He addresses the notable medicinal benefits of CBD, THC, and the many terpenes associated with cannabis’s reputation for healing, and teaches how to be a smart consumer of cannabinoids. Foundations of Health provides a unique understanding and approach to healing that will forever change the way we think of our bodies and our physical health.
Author: Eli Stevens
Publisher: Manning Publications
ISBN: 1617295264
Category : Computers
Languages : en
Pages : 518
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Book Description
Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun. Summary Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun. Foreword by Soumith Chintala, Cocreator of PyTorch. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Although many deep learning tools use Python, the PyTorch library is truly Pythonic. Instantly familiar to anyone who knows PyData tools like NumPy and scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s excellent for building quick models, and it scales smoothly from laptop to enterprise. Because companies like Apple, Facebook, and JPMorgan Chase rely on PyTorch, it’s a great skill to have as you expand your career options. It’s easy to get started with PyTorch. It minimizes cognitive overhead without sacrificing the access to advanced features, meaning you can focus on what matters the most - building and training the latest and greatest deep learning models and contribute to making a dent in the world. PyTorch is also a snap to scale and extend, and it partners well with other Python tooling. PyTorch has been adopted by hundreds of deep learning practitioners and several first-class players like FAIR, OpenAI, FastAI and Purdue. About the book Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. After covering the basics, the book will take you on a journey through larger projects. The centerpiece of the book is a neural network designed for cancer detection. You'll discover ways for training networks with limited inputs and start processing data to get some results. You'll sift through the unreliable initial results and focus on how to diagnose and fix the problems in your neural network. Finally, you'll look at ways to improve your results by training with augmented data, make improvements to the model architecture, and perform other fine tuning. What's inside Training deep neural networks Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Exploring code samples in Jupyter Notebooks About the reader For Python programmers with an interest in machine learning. About the author Eli Stevens had roles from software engineer to CTO, and is currently working on machine learning in the self-driving-car industry. Luca Antiga is cofounder of an AI engineering company and an AI tech startup, as well as a former PyTorch contributor. Thomas Viehmann is a PyTorch core developer and machine learning trainer and consultant. consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production
Author: Ian Goodfellow
Publisher: MIT Press
ISBN: 0262337371
Category : Computers
Languages : en
Pages : 801
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Book Description
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.
