Multi-layered Perceptron: History and Training Algorithm
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Explains the learning process in multi-layer neural networks, including back-propagation, activation functions, weights update, and error backpropagation.
Explores model-based deep reinforcement learning, focusing on Monte Carlo Tree Search and its applications in game strategies and decision-making processes.
Explores optimal errors in high-dimensional models, comparing algorithms and shedding light on the interplay between model architecture and performance.
