TMI’s Science and Exploration program has strengthened our climate change, conservation, and sustainable development programs through using integrated, applied field research. We developed applied climate change and conservation research programs, higher education resource materials, international photo exhibits, and public presentations. The Science and Exploration program co-managed the High Mountains Adaptation Partnership (HiMAP), funded by the US Agency for International Development (USAID). HiMAP was initiated in March 2012 following the successful 2009 “Adapting to a World without Glaciers” workshop in Peru and the 2011 “Andean-Asian Mountains Global Knowledge Exchange on Glaciers, Glacial Lakes, Water & Hazard Management” in the Mt. Everest region of Nepal. Created and implemented by The Mountain Institute (TMI) and the University of Texas at Austin (UT), HiMAP has actively strengthened scientific, social, and institutional capacity for climate change adaptation and resilient development, along with disaster risk mitigation. A specific focus has been management of potentially dangerous glacial lakes and other climate-related disasters in glaciated regions of Peru, Nepal, and elsewhere in the high mountain world. The Partnership has implemented a series of innovative and inter-related activities, including:
State of the art rapid reconnaissance methods for the evaluation of potentially dangerous glacial lakes. These have been developed and applied in Nepal and Peru, where they link the best of modern technologies with traditional on-the-ground field methods.
South-south collaboration and exchanges. HiMAP has connected Asian and Andean countries with similar needs in climate adaptation and glacial risk management.
Local Adaptation Plans for Action (LAPAs) for the Khumbu region of Nepal and the Cordillera Blanca of Peru. These local-scale climate-change adaptation plans are based on the principles of full community participation, climate smart development, ecosystem-based adaptation approaches, risk mitigation, and disaster management planning.
A high mountain glacial watershed “community of practice.” The HiMAP community of practice is actively sharing knowledge internationally between high mountain scientists and practitioners, undertaking comparative analyses, implementing climate change adaptation projects, and influencing high mountain policy.
A new generation of mountain-scientists. The young scientists supported by HiMAP are fluent in climate change and adaptation issues, field as well as laboratory methods, integrating traditional knowledge, active community engagement, and climate-smart development in high mountain glacial watersheds.
A portfolio of peer reviewed and popular publications, videos, public, and professional presentations.
Global awareness of the critical importance of high mountain glacial watersheds. Donors, international agencies, and governments are now more familiar with mountain issues thanks to HiMAP.
An Everest Alliance. A collaborative organization of Khumbu residents and visitors has been initiated that will help solve the social and environmental problems of the Mt. Everest region—and later of other iconic mountains worldwide.
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Since 1972 The Mountain Institute has partnered with remote mountain communities in the highest, longest and oldest mountains of the world. We work together to conserve ecosystems, develop sustainable livelihoods and protect unique mountain cultures.
Reinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs human-level performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library.
The book begins with getting you up and running with the concepts of reinforcement learning using Keras. You’ll learn how to simulate a random walk using Markov chains and select the best portfolio using dynamic programming (DP) and Python. You’ll also explore projects such as forecasting stock prices using Monte Carlo methods, delivering vehicle routing application using Temporal Distance (TD) learning algorithms, and balancing a Rotating Mechanical System using Markov decision processes.
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By the end of this book, you’ll not only have developed hands-on training on concepts, algorithms, and techniques of reinforcement learning but also be all set to explore the world of AI.
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We are sharing the knowledge for free of charge and help students and readers all over the world, especially third world countries who do not have money to buy e-Books, so we have launched this site.