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3 articles Archived #deep-learning
Earlier articles with this tag. Some details may be outdated.
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Activation And Loss Functions In Deep Learning
A practical guide to activation and loss functions, their derivatives, numerical pitfalls, and sensible output-layer pairings.
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Understanding Gradient Descent
A practical guide to gradient descent, mini-batches, momentum, RMSprop, and Adam, with the trade-offs that matter during training.
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Neural Networks
A clear introduction to dense neural networks, forward propagation, backpropagation, regularization, and a working NumPy training example.