Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in RNN: Loss Function
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Information Technology (IT), Architecture
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University
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Hard
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The video tutorial explains the concept of loss functions in recurrent neural networks (RNNs), focusing on stochastic gradient descent and its application in updating parameters. It discusses the overall loss function, how parameters like WX, WY, and WA impact the loss, and introduces backpropagation through time. The tutorial also compares batch mode and stochastic mode, highlighting their differences in computing losses. The next video will cover the chain rule for computing derivatives.
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