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 ljvmiranda921.github.io
Gym-Lattice: RL environment for the protein folding problem
| | www.v7labs.com
Deep Reinforcement Learning: Definition, Algorithms & Uses
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| | Deep reinforcement learning (DRL) combines reinforcement learning with deep learning. This guide covers the basics of DRL and how to use it.
| | www.exxactcorp.com
What You Need to Know About Deep Reinforcement Learning | Exxact Blog
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| | [AI summary] The text provides an in-depth overview of Deep Reinforcement Learning (DRL), focusing on its key components, challenges, and applications. It explains how DRL combines reinforcement learning (RL) with deep learning to handle complex decision-making tasks. The article discusses the limitations of traditional Q-learning, such as the need for a Q-table and the issue of unstable target values. It introduces Deep Q-Networks (DQNs) as a solution, highlighting the use of experience replay and target networks to stabilize training. Additionally, the text highlights real-world applications like AlphaGo, Atari game playing, and oil and gas industry use cases. It concludes by emphasizing DRL's potential for scalable, human-compatible AI systems and its rol...
| | www.blopig.com
Protein Property Prediction Using Graph Neural Networks | Oxford Protein Informatics Group
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| | [AI summary] The article discusses the application of graph neural networks (GNNs) in protein property prediction, highlighting their ability to model protein structures and interactions, the integration of pre-trained protein language models like ESM, and the use of residual layers to address oversmoothing challenges.
| | tiao.io
A Tutorial on Variational Autoencoders with a Concise Keras Implementation | Louis Tiao
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| An in-depth practical guide to variational encoders from a probabilistic perspective.