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Subjective Questions-Unit-5 by subjecttimepass is a document available to read on EtoBox.

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The document discusses key strategies for refining neural network architectures, including deepening the network, increasing width, batch normalization, dropout, and attention mechanisms, each impacting learning efficiency, generalization, and convergence. It also covers the ε-greedy policy in reinforcement learning, highlighting its role in balancing exploration and exploitation, along with its advantages and disadvantages. Techniques like mini-batch training and soft updates are analyzed for their signifi

Author
subjecttimepass
Language
EN