Deep Learning CNN Convolutional Neural Networks with Python - Why Derivatives

The video tutorial explains a simple convolutional neural network (CNN) with a focus on its components, including the use of a sigmoid function for classification. It delves into the calculation of Y hat, a key output of the network, and discusses the role of derivatives in optimization. The tutorial introduces the concept of gradient descent, an iterative algorithm used to find the minimum of a function, and highlights the importance of derivatives in this process. The video sets the stage for a deeper exploration of the chain rule in the next session.

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