Data Science and Machine Learning (Theory and Projects) A to Z - RNN Implementation: Automatic Differentiation

Data Science and Machine Learning (Theory and Projects) A to Z - RNN Implementation: Automatic Differentiation

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Information Technology (IT), Architecture

University

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The video discusses the challenges of gradient computation in neural networks, particularly in recurrent neural networks (RNNs). It introduces automatic differentiation as a solution, highlighting its simplicity compared to analytical methods. The video also introduces PyTorch, a deep learning package from Facebook, and mentions other packages like TensorFlow and MxNet. The focus is on simplifying gradient computations using these tools, with a promise to demonstrate automatic differentiation in PyTorch in the next video.

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OPEN ENDED QUESTION

3 mins • 1 pt

What new insight or understanding did you gain from this video?

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