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

The video tutorial explains the implementation of batch gradient descent in neural networks, contrasting it with stochastic gradient descent. It details the necessary code modifications, focusing on loss calculation and updates. The tutorial also discusses the computational resources required for batch processing and highlights the efficiency of vectorized code. Finally, it introduces the concept of mini-batch gradient descent, setting the stage for the next video.

Explain the process of updating the loss in batch gradient descent.

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What modifications are made to compute the average loss in batch gradient descent?

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What is the main difference between stochastic gradient descent and batch gradient descent?

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What is the significance of the error decreasing after each epoch in batch gradient descent?

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Why is vectorization important in the implementation of batch gradient descent?

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What are the computational resource requirements for batch gradient descent?

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Describe the role of mini-batches in the context of batch gradient descent.

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