DeepHack.Game 5vision team
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Идеи и результаты команды 5vision, занявшей 1-е место на Хакатоне DeepHack.Game. Задача: улучшить опубликованный Google DeepMind алгоритм обучения в играх Atari 2600 The ideas and results of 5vision team - 1st place in the Hackathon DeepHack.Game. Task was to improve learning algorithm for playing Atari 2600 games, which was published by Google DeepMind
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The primary targets of this project are Westerlund 1 and 2, the closest supermassive star clusters to the Sun.
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Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
在线办理(salfor毕业证书)索尔福德大学毕业证毕业完成信一模一样
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Applied Science: Thermodynamics, Laws & Methodology.pdf
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
Randomised Optimisation Algorithms in DAPHNE
Slides from talk:
Aleš Zamuda: Randomised Optimisation Algorithms in DAPHNE .
Austrian-Slovenian HPC Meeting 2024 – ASHPC24, Seeblickhotel Grundlsee in Austria, 10–13 June 2024
https://ashpc.eu/
Immersive Learning That Works: Research Grounding and Paths Forward
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
8.Isolation of pure cultures and preservation of cultures.pdf
Isolation of pure culture, its various method.
SAR of Medicinal Chemistry 1st by dk.pdf
In this presentation include the prototype drug SAR on thus or with their examples .
Syllabus of Second Year B. Pharmacy
2019 PATTERN.
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DeepHack.Game 5vision team
- 1. Deep Neural Networks for Atari 2600 games 5 vision team
- 2. Atari 2600 games The Atari 2600 is a home video game console released on 1977
- 3. Deep Q-learning Neural Networks for Atari games (DQN) Images and Scores as inputs No prior knowledge about game rules
- 4. Google results • DQN outperforms the best existing methods on 43 games • DQN outperforms human professional human on 29 games
- 5. • Google DQN • Pre-training stage(ISA) • Reward normalization • Minibatch sampling • Reduced fully connected layers • Drop-outs • Actor-critic (1 net for actor 1 net for critic) Our Solution
- 6. Pre-training stage • Motion images sampling • Insertion of extracted images • Subsidiary NN training • Extraction of middle layer weights • Input the weights into first layer of convolution NN
- 7. Reward normalization Reward score used for training Google Our solution Positive reward +1 In range 0.7 : 1 Zero reward 0 0 Negative reward -1 In range -1 : -0.7
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