Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Implementation Gradie

The video tutorial covers the architecture of a neural network with three layers, detailing the number of neurons in each layer. It explains the implementation of the sigmoid activation function and demonstrates how to update parameters using gradient descent. The tutorial concludes with a brief introduction to stochastic gradient descent, which will be covered in the next video.

What are the numbers of neurons in each of the three layers of the neural network discussed?

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Explain the sigmoid activation function as described in the text.

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What is the purpose of the update parameters function in the context of gradient descent?

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Describe the process of updating weights in the neural network as mentioned in the text.

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What is the next step mentioned after writing the activation function and updating parameters?

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