This document provides an introduction to deep Q-networks (DQN) for beginners. It explains that DQNs can be used to learn optimal actions in video games by collecting data on screen states, player actions, rewards, and next states without knowing the game's rules. The key idea is to approximate a "Q function" that represents the total expected rewards if optimal actions are taken from each state onward. A deep neural network is used as the candidate function, and its parameters are adjusted using an error function to satisfy the Q-learning equation. To collect the necessary state-action data, the game is played with a mix of random exploration and exploiting the current best actions from the Q-network.
Introducton to Convolutional Nerural Network with TensorFlowEtsuji Nakai
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Explaining basic mechanism of the Convolutional Neural Network with sample TesnsorFlow codes.
Sample codes: https://github.com/enakai00/cnn_introduction
The document discusses information architecture (IA) and its role on the web. It provides context that IA involves organizing and labeling websites in a way that allows users to find what they need. It also mentions some challenges of IA, such as ensuring usability across different devices and languages. The document suggests that effective IA is important for marketing campaigns, brand presence, and the overall user experience on a website.