Deep Learning CNN Convolutional Neural Networks with Python - Batch MiniBatch Stochastic Gradient Descent

The video tutorial discusses the significance of the learning rate in neural networks, highlighting its role in reaching the global minimum efficiently. It explores the challenges in selecting the optimal learning rate and compares different gradient descent methods, including batch, stochastic, and mini-batch gradient descent. The tutorial emphasizes the advantages of mini-batch gradient descent in terms of computational efficiency and smoother convergence.

Why is it challenging to find the optimal learning rate for a new dataset?

What are some heuristics suggested for setting the learning rate?

What is mini-batch gradient descent and how does it combine the advantages of both batch and stochastic methods?