Deep Learning with Python

Along with updating all chapters and Python code examples, the second edition of this bestseller includes new chapters on Gaussian processes, Boltzmann machines, and deep belief networks. It also revi

This book introduces artificial neural networks to students and professionals. It covers the theory and applications in statistical learning methods with concrete Python code examples.

A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.

Accurate, robust and fast image reconstruction is a critical task in many scientific, industrial and medical applications. Over the last decade, image reconstruction has been revolutionized by the rise of compressive imaging. It has fundamentally changed the way modern image reconstruction is performed. This in-depth treatment of the subject commences with a practical introduction to compressive imaging, supplemented with examples and downloadable code, intended for readers without extensive background in the subject. Next, it introduces core topics in compressive imaging – including compressed sensing, wavelets and optimization – in a concise yet rigorous way, before providing a detailed treatment of the mathematics of compressive imaging. The final part is devoted to recent trends in compressive imaging: deep learning and neural networks. With an eye to the next decade of imaging research, and using both empirical and mathematical insights, it examines the potential benefits and the

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

What type of questioning invigorates and sustains productive discussions?That's what Jackie Acree Walsh and Beth Dankert Sattes ask as they begin a passionate exploration of questioning as the beating heart of thoughtful discussions. Questioning and discussion are important components of classroom instruction that work in tandem to push learning forward and move students from passive participants to active meaning-makers.Walsh and Sattes argue that the skills students develop through questioning and discussion are critical to academic achievement, career success, and active citizenship in a democratic society. They also have great potential to engage students at the highest levels of thinking and learning.The extent to which this potential is realized, of course, depends on individual teachers who embrace these practices, make them their own, and realize that this process requires a true partnership with students.With that in mind, Questioning for Classroom Discussion presents and anal

Immerse yourself in fascinating facts about water! This comprehensive yet accessible exploration of water will help young readers understand many aspects of one of our planet's most precious resources - and how they can protect it. A friendly water droplet character guides children through topics ranging from melting and freezing to the ways in which water literally shapes the Earth. Tales by storytellers from around the world are sprinkled through the book, highlighting the variety of ways in which global cultures value water. The engaging format includes gatefolds and booklets with hands-on activity ideas for learning about and protecting water. Topics covered include: The importance of water to life How much of the planet is made of water Where in the world water is located Freezing, melting and evaporation The water cycle Why animals and plants need water Salt water versus fresh water environments Uses for water, including water as a source of renewable energy Water conservation an

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficien

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an onli

With chapters based on real situations outlining practical strategies, this guide shows you how formative teaching, learning and assessment can enrich day-to-day teaching, and ensure deep and sustaine

With chapters based on real situations outlining practical strategies, this guide shows you how formative teaching, learning and assessment can enrich day-to-day teaching, and ensure deep and sustaine