Basics Deep Learning

• This session will cover the basics of deep learning, including its history, important concepts, and applications.
• We will review model architectures and parameters, including the loss function and optimizer.
• We will also cover training regimes and the basics of gradient descent and backpropagation.

Literature: LeCun, Y., Bengio, Y., & Hinton, G. (2015)

Notebooks

• Basics Deep Learning

Slides

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