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 danieltakeshi.github.io
Going Deeper Into Reinforcement Learning: Understanding Deep-Q-Networks
| | www.neuralnet.ai
Asynchronous Deep Reinforcement Learning - NeuralNet.ai
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| | [AI summary] The article explores asynchronous deep reinforcement learning as an alternative to experience replay, explaining how parallel agents can break state correlations and detailing the implementation of the A3C algorithm.
| | www.mlpowered.com
Reinforcement Learning from scratch ยท mlpowered
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| | Blog posts and other information
| | neuralnetworksanddeeplearning.com
Neural networks and deep learning
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| | [AI summary] The text provides an in-depth explanation of the backpropagation algorithm in neural networks. It starts by discussing the concept of how small changes in weights propagate through the network to affect the final cost, leading to the derivation of the partial derivatives required for gradient descent. The explanation includes a heuristic argument based on tracking the perturbation of weights through the network, resulting in a chain of partial derivatives. The text also touches on the historical context of how backpropagation was discovered, emphasizing the process of simplifying complex proofs and the role of using weighted inputs (z-values) as intermediate variables to streamline the derivation. Finally, it concludes with a citation and licens...
| | comsci.blog
Step-by-Step Guide to Image Classification with Vision Transformers (ViT) | ML and robotics notes
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| In this blog post, we will learn about vision transformers (ViT), and implement an MNIST classifier with it. We will go step-by-step and understand every part of the vision transformers clearly, and you will see the motivations of the authors of the original paper in some of the parts of the architecture.