Fundamentals of Neural Networks - Backward Propagation Through Time

The lecture covers backpropagation through time in recurrent neural networks (RNNs), building on the concept of gradient descent with a focus on time-stamped loss functions. It explains the RNN architecture, detailing how information flows forward and backward. The lecture discusses prediction and loss functions, using binary cross-entropy as an example. It then moves on to optimization, emphasizing the importance of selecting the right algorithm, and provides a detailed walkthrough of implementing gradient descent, including parameter updates and customization for specific datasets.

What are the parameters that can be adjusted to minimize the loss function?

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How does the choice of optimization algorithm affect the training of a recurrent neural network?

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What is the role of gradients in the gradient descent algorithm for updating weights?

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