Download or read book Principal Labs written by Megan Kortlandt and published by ASCD. This book was released on 2021-08-27 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: Megan Kortlandt, Carly Stone, and Samantha Keesling have developed a flexible structure for collaborative professional learning that they call the principal lab, in which K–12 principals learn with and from each other to become better instructional leaders. Each chapter walks through the foundational components of a successful principal lab—relationship building, anchoring experiences, observations, and feedback—and then discusses how to lay the groundwork, figure out logistics, and plan and structure labs. Principal Labs: Strengthening Instructional Leadership Through Shared Learning combines the latest research in adult learning with the authors' practical experience to discuss the qualities of a successful principal lab and provide the tools to build your own. It's easy to get started with downloadable reflection and observation templates based on the examples in each chapter. As a school principal you have many responsibilities, and finding time for your own professional development can be a challenge. The approach in this book will help you effectively use your time to connect with other principals, practice and develop feedback skills, and ultimately make informed decisions for instructional improvement in your school.

Download or read book Principal Labs written by Megan Kortlandt and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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Download or read book Principles and Labs for Deep Learning written by Shih-Chia Huang and published by Academic Press. This book was released on 2021-07-06 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles and Labs for Deep Learning provides the knowledge and techniques needed to help readers design and develop deep learning models. Deep Learning techniques are introduced through theory, comprehensively illustrated, explained through the TensorFlow source code examples, and analyzed through the visualization of results. The structured methods and labs provided by Dr. Huang and Dr. Le enable readers to become proficient in TensorFlow to build deep Convolutional Neural Networks (CNNs) through custom APIs, high-level Keras APIs, Keras Applications, and TensorFlow Hub. Each chapter has one corresponding Lab with step-by-step instruction to help the reader practice and accomplish a specific learning outcome. Deep Learning has been successfully applied in diverse fields such as computer vision, audio processing, robotics, natural language processing, bioinformatics and chemistry. Because of the huge scope of knowledge in Deep Learning, a lot of time is required to understand and deploy useful, working applications, hence the importance of this new resource. Both theory lessons and experiments are included in each chapter to introduce the techniques and provide source code examples to practice using them. All Labs for this book are placed on GitHub to facilitate the download. The book is written based on the assumption that the reader knows basic Python for programming and basic Machine Learning. - Introduces readers to the usefulness of neural networks and Deep Learning methods - Provides readers with in-depth understanding of the architecture and operation of Deep Convolutional Neural Networks - Demonstrates the visualization needed for designing neural networks - Provides readers with an in-depth understanding of regression problems, binary classification problems, multi-category classification problems, Variational Auto-Encoder, Generative Adversarial Network, and Object detection

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