Reinforcement Learning and Deep RL Python Theory and Projects - DNN Implementation Minibatch Gradient Descent

The video tutorial explains the implementation of mini batch gradient descent, a method combining stochastic and batch gradient descent. It covers setting up the function, parameters, and batch processing logic. Debugging and error fixing are demonstrated, followed by a discussion on parameter tuning and neural network implementation. The tutorial concludes with a look at using frameworks like Torch for more efficient coding.

What is mini batch gradient descent and how does it differ from batch and stochastic gradient descent?

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Explain the significance of the batch size parameter in mini batch gradient descent.

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How do you determine the number of batches in mini batch gradient descent?

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What happens if the total number of examples is not completely divisible by the batch size?

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Describe the process of updating parameters after each batch in mini batch gradient descent.

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What are the advantages and disadvantages of having many parameters in a neural network?

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Why is it beneficial to use frameworks for implementing neural networks?

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