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

The video tutorial explains the implementation of batch gradient descent for training neural networks. It starts with a brief introduction and then delves into the code modifications needed to switch from stochastic to batch gradient descent. The tutorial highlights the differences between the two methods, particularly in how variables are updated. It also discusses the computational resources required for batch gradient descent and the benefits of vectorized code. The video concludes with a preview of the next topic, mini-batch gradient descent.

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

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How does vectorization improve the efficiency of batch gradient descent?

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What is the role of mini-batches in the context of gradient descent?

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