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Dive Into Deep Learning

2019-07-15 Education
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Release : 2019-07-15
Genre : Education
Kind : eBook
Book Rating : 404/5 ( reviews)

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Book Synopsis Dive Into Deep Learning by : Joanne Quinn

Download or read book Dive Into Deep Learning written by Joanne Quinn. This book was released on 2019-07-15. Available in PDF, EPUB and Kindle. Book excerpt: The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the World Change the World. This hands-on guide provides a roadmap for building capacity in teachers, schools, districts, and systems to design deep learning, measure progress, and assess conditions needed to activate and sustain innovation. Dive Into Deep Learning: Tools for Engagement is rich with resources educators need to construct and drive meaningful deep learning experiences in order to develop the kind of mindset and know-how that is crucial to becoming a problem-solving change agent in our global society. Designed in full color, this easy-to-use guide is loaded with tools, tips, protocols, and real-world examples. It includes: • A framework for deep learning that provides a pathway to develop the six global competencies needed to flourish in a complex world — character, citizenship, collaboration, communication, creativity, and critical thinking. • Learning progressions to help educators analyze student work and measure progress. • Learning design rubrics, templates and examples for incorporating the four elements of learning design: learning partnerships, pedagogical practices, learning environments, and leveraging digital. • Conditions rubrics, teacher self-assessment tools, and planning guides to help educators build, mobilize, and sustain deep learning in schools and districts. Learn about, improve, and expand your world of learning. Put the joy back into learning for students and adults alike. Dive into deep learning to create learning experiences that give purpose, unleash student potential, and transform not only learning, but life itself.

Deep Learning on Graphs

2021-09-23 Computers
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Release : 2021-09-23
Genre : Computers
Kind : eBook
Book Rating : 745/5 ( reviews)

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Book Synopsis Deep Learning on Graphs by : Yao Ma

Download or read book Deep Learning on Graphs written by Yao Ma. This book was released on 2021-09-23. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare.

Deep Learning

2017-11-06 Education
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Release : 2017-11-06
Genre : Education
Kind : eBook
Book Rating : 59X/5 ( reviews)

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Book Synopsis Deep Learning by : Michael Fullan

Download or read book Deep Learning written by Michael Fullan. This book was released on 2017-11-06. Available in PDF, EPUB and Kindle. Book excerpt: New Pedagogies for Deep Learning (NDPL) provides a comprehensive strategy for systemwide transformation. Using the 6 competencies of NDPL and a wealth of vivid examples, Fullan re-defines and re-examines what deep learning is and identifies the practical strategies for revolutionizing learning and leadership.

Generative Deep Learning

2019-06-28 Computers
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Release : 2019-06-28
Genre : Computers
Kind : eBook
Book Rating : 890/5 ( reviews)

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Book Synopsis Generative Deep Learning by : David Foster

Download or read book Generative Deep Learning written by David Foster. This book was released on 2019-06-28. Available in PDF, EPUB and Kindle. Book excerpt: Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Programming Machine Learning

2020-03-31 Computers
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Release : 2020-03-31
Genre : Computers
Kind : eBook
Book Rating : 710/5 ( reviews)

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Book Synopsis Programming Machine Learning by : Paolo Perrotta

Download or read book Programming Machine Learning written by Paolo Perrotta. This book was released on 2020-03-31. Available in PDF, EPUB and Kindle. Book excerpt: You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty. Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system. Start from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. Apart from that, you'll only need your computer, and your code-adept brain.

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