Science loving Adam "Einstein" Anderson is back! Einstein and his best friend Paloma try to stump each other and foil the ever-scheming Stanley as they solve science mysteries. Help solve the interactive mysteries and explore the science with hands-on experiments. Raise some ants, see sound waves in action and find out how to make a compass out of an ordinary watch!
"Science loving Adam "Einstein" Anderson is back - and more contemporary than ever! Einstein and his best friend Paloma try to stump each other and foil the ever-scheming Stanley as they solve science mysteries."--Amazon website.
Match wits with 12 year old Einstein Anderson and his best friend, Paloma Fuentes, who use their science knowledge to unravel tricky puzzles and solve mysteries. Plus, do their science experiments, and become a science geek too!
When Cricket decides to start cooking to cure her summer blues, it's not long before Lucas, Julio, and friends-- the stars of Johanna Hurwitz's popular Class Clown books--get involved and "stir up" the action. From "Zucchini Houdini" to "April Food's Day," here are four zesty stories and sixteen easy recipes to dig into for a fiesta of reading,cooking, and eating--just desserts!
Match wits with 12-year-old Einstein Anderson and his best friend, Paloma Fuentes, who use their science knowledge to unravel tricky puzzles and solve mysteries. In Book 5 of the series, Paloma and Einstein use their knowledge about hurricanes and cold blooded animals to solve some tricky cases -- and you can build your own barometer or see a frog hibernate. Amaze your friends by balancing a quarter on the edge of a dollar bill!
Solve design, planning, and control problems using modern AI techniques. Optimization problems are everywhere in daily life. What’s the fastest route from one place to another? How do you calculate the optimal price for a product? How should you plant crops, allocate resources, and schedule surgeries? Optimization Algorithms introduces the AI algorithms that can solve these complex and poorly-structured problems. In Optimization Algorithms: AI techniques for design, planning, and control problems you will learn: • The core concepts of search and optimization • Deterministic and stochastic optimization techniques • Graph search algorithms • Trajectory-based optimization algorithms • Evolutionary computing algorithms • Swarm intelligence algorithms • Machine learning methods for search and optimization problems • Efficient trade-offs between search space exploration and exploitation • State-of-the-art Python libraries for search and optimization Inside this comprehensive guide, you’ll find a wide range of optimization methods, from deterministic search algorithms to stochastic derivative-free metaheuristic algorithms and machine learning methods. Don’t worry—there’s no complex mathematical notation. You’ll learn through in-depth case studies that cut through academic complexity to demonstrate how each algorithm works in the real world. Plus, get hands-on experience with practical exercises to optimize and scale the performance of each algorithm. About the technology Every time you call for a rideshare, order food delivery, book a flight, or schedule a hospital appointment, an algorithm works behind the scenes to find the optimal result. Blending modern AI methods with classical search and optimization techniques can deliver incredible results, especially for the messy problems you encounter in the real world. This book shows you how. About the book Optimization Algorithms explains in clear language how optimization algorithms work and what you can do with them. This engaging book goes beyond toy examples, presenting detailed scenarios that use actual industry data and cutting-edge AI techniques. You will learn how to apply modern optimization algorithms to real-world problems like pricing products, matching supply with demand, balancing assembly lines, tuning parameters, coordinating mobile networks, and cracking smart mobility challenges. What's inside • Graph search algorithms • Metaheuristic algorithms • Machine learning methods • State-of-the-art Python libraries for optimization • Efficient trade-offs between search space exploration and exploitation About the reader Requires intermediate Python and machine learning skills. About the author Dr. Alaa Khamis is an AI and smart mobility technical leader at General Motors and a lecturer at the University of Toronto. The technical editor on this book was Frances Buontempo. Table of Contents PART 1 1 Introduction to search and optimization 2 A deeper look at search and optimization 3 Blind search algorithms 4 Informed search algorithms PART 2 5 Simulated annealing 6 Tabu search PART 3 7 Genetic algorithms 8 Genetic algorithm variants PART 4 9 Particle swarm optimization 10 Other swarm intelligence algorithms to explore PART 5 11 Supervised and unsupervised learning 12 Reinforcement learning Appendix A Appendix B Appendix C
This book is one of the first to explore how Chinese companies are feeling the impulse of emerging business trends and seizing opportunities brought by technology innovation. It consists case studies of 7 Chinese companies: 3DMed, Wechat from Tencent, Shanghai GM, CP Group, Alibaba, AutoNavi, and ICBC. Each Chinese company has its unique perspectives and different ways to make transformation and business model adjustments. The book helps fill the gap between the global interest in “Innovate in China” and the limited availability of cases on innovations in the country. It is a valuable reference resource for readers in China and beyond wishing to address challenges in the context of growing digital technologies and overwhelming business trends.
