Deep Learning - Artificial Neural Networks with Tensorflow - Stochastic Gradient Descent

The video tutorial explains gradient descent, focusing on stochastic gradient descent (SGD) in TensorFlow 2.0. It highlights the efficiency of using random samples to approximate the average, similar to measuring the average height of a population. The tutorial discusses the benefits of batch processing in deep learning, using smaller batch sizes to reduce computation time. It provides a pseudo code for implementing batch gradient descent and emphasizes the importance of randomizing data to avoid learning undesirable patterns. An exercise is suggested to compare the convergence speed of different batch sizes.

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

Why is it advantageous to use a smaller sample size when calculating the average height of a population?

How does the concept of using a smaller batch size apply to deep learning and training models?

What are the potential consequences of not randomizing the data on each epoch during training?

In the context of Tensorflow 2.0, what is the significance of the batch size argument in the fit function?