Occupational therapist Jim Bauer tells us what it was like to grow up with undiagnosed dyslexia. Experience the pain and embarrassment this shy little boy felt as teachers and parents ignored his learning disability and simply encouraged him to "try harder." This is must reading for anyone who works with children.

After being nearly annihilated by something called the Darkness, mankind is rebuilding centuries later. Zander Harmony is the young lord of a growing domain in a city called Wellington. Though at first he does not wish to be a lord, he begins to trust and appreciate the people around him that look to him as their leader. As the darkness begins to reappear, and other lords begin plotting against him, Zander realizes there's more to being a lord than just getting pampered. With multiple forces threatening to take away everything he has built and everyone he loves, Zander quickly realizes among the chaos that the battles are going to get very bloody, and the romance is going to be sweet.

This book contains papers from the International Conference on Extreme Learning Machine 2021, which was held in virtual on December 15–16, 2021. Extreme learning machines (ELM) aims to enable pervasive learning and pervasive intelligence. As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view. ELM may be one of the fundamental `learning particles’ filling the gaps between machine learning and biological learning (of which activation functions are even unknown). ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated. ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc.) as long as they are nonlinear piecewise continuous, independent of training data and application environments. Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems. ELM theories and algorithms argue that “random hidden neurons” capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons. ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers. This conference provides a forum for academics, researchers, and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning. This book covers theories, algorithms, and applications of ELM. It gives readers a glance of the most recent advances of ELM.

A new collection of stories, including some that have never before been seen, from the New York Times best-selling author of the Silo trilogy Hugh Howey is known for crafting riveting and immersive page-turners of boundless imagination, spawning millions of fans worldwide, first with his best-selling novel Wool, and then with other enthralling works such as Sand and Beacon 23. Now comes Machine Learning, an impressive collection of Howey's science fiction and fantasy short fiction, including three stories set in the world of Wool, two never-before-published tales written exclusively for this volume, and fifteen additional stories collected here for the first time. These stories explore everything from artificial intelligence to parallel universes to video games, and each story is accompanied by an author's note exploring the background and genesis of each story. Howey's incisive mind makes Machine Learning: New and Collected Stories a compulsively readable and thought-provoking selection of short works--from a modern master at the top of his game.

Alfie likes hanging out at the airport - everyone has someone waiting for them and they all seems so happy when they arrive back from their holidays. He wishes he had someone as excited to see him. So when he finds Eric, a one-legged robot in need of a friend, at the airport Lost Property counter, he decides to take him home with him. A hilarious and heartwarming tale of friendship from Carnegie medal-winning author, Frank Cottrell-Boyce and illustrated by Steven Lenton.

"This reference offers a wide-ranging selection of key research in a complex field of study,discussing topics ranging from using machine learning to improve the effectiveness of agents and multi-agent systems to developing machine learning software for high frequency trading in financial markets"--Provided by publishe

Machine Learning Proceedings 1992

Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system. The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem. Features: Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems. Explains machine learning concepts as they arise in real-world case studies. Shows how to diagnose, understand and address problems with machine learning systems. Full source code available, allowing models and results to be reproduced and explored. Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.

Autonomous robots may become our closest companions in the near future. While the technology for physically building such machines is already available today, a problem lies in the generation of the behavior for such complex machines. Nature proposes a solution: young children and higher animals learn to master their complex brain-body systems by playing. Can this be an option for robots? How can a machine be playful? The book provides answers by developing a general principle---homeokinesis, the dynamical symbiosis between brain, body, and environment---that is shown to drive robots to self- determined, individual development in a playful and obviously embodiment- related way: a dog-like robot starts playing with a barrier, eventually jumping or climbing over it; a snakebot develops coiling and jumping modes; humanoids develop climbing behaviors when fallen into a pit, or engage in wrestling-like scenarios when encountering an opponent. The book also develops guided self-organization, a new method that helps to make the playful machines fit for fulfilling tasks in the real world. The book provides two levels of presentation. Students and scientific researchers interested in the field of robotics, self-organization and dynamical systems theory may be satisfied by the in-depth mathematical analysis of the principle, the bootstrapping scenarios, and the emerging behaviors. But the book additionally comes with a robotics simulator inviting also the non- scientific reader to simply enjoy the fabulous world of playful machines by performing the numerous experiments.

"Petrie has a preternatural talent for ratcheting up suspense."--New York Times Book Review When Peter Ash rescues a stranded woman, he finds she’s in far deeper trouble than he could ever imagine in the powerful new thriller in this bestselling and award-winning series. War veteran Peter Ash is driving through northern Nebraska when he encounters a young pregnant woman alone on a gravel road, her car dead. Peter offers her a lift, but what begins as an act of kindness soon turns into a deadly cat-and-mouse chase across the lonely highways with the woman’s vicious ex-cop husband hot on their trail. The pregnant woman has seen something she was never meant to see . . . but protecting her might prove to be more than Peter can handle. In order to save the woman and himself, Peter must use everything he has learned during his time as a Marine, including his knowledge of human nature, in order to escape a ruthless killer with instincts and skills that match—and perhaps exceed—Peter’s own.