Questions tagged [stochastic-gradient-descent]

For questions related to stochastic gradient descent (SGD), which is stochastic gradient descent that uses stochastic (or noisy) gradients.

Filter by
Sorted by
Tagged with
6
votes
2answers
964 views

What exactly is averaged when doing batch gradient descent?

I have a question about how the averaging works when doing mini-batch gradient descent. I think I now understood the general gradient descent algorithm, but only for online learning. When doing mini-...
5
votes
1answer
2k views

Is back-propagation applied for each data point or for a batch of data points?

I am new to deep learning and trying to understand the concept of back-propagation. I have a doubt about when the back-propagation is applied. Assume that I have a training data set of 1000 images ...
15
votes
3answers
36k views

How do I choose the optimal batch size?

Batch size is a term used in machine learning and refers to the number of training examples utilised in one iteration. The batch size can be one of three options: batch mode: where the batch size is ...
9
votes
2answers
10k views

How do I handle negative rewards in policy gradients with the cross-entropy loss function?

I am using policy gradients in my reinforcement learning algorithm, and occasionally my environment provides a severe penalty (i.e. negative reward) when a wrong move is made. I'm using a neural ...
3
votes
1answer
560 views

Why is the learning rate generally beneath 1?

In all examples I've ever seen, the learning rate of an optimisation method is always less than $1$. However, I've never found an explanation as to why this is. In addition to that, there are some ...
3
votes
2answers
78 views

What is the difference between batch and mini-batch gradient decent?

I am learning deep learning from Andrew Ng's tutorial Mini-batch Gradient Descent. Can anyone explain the similarities and dissimilarities between batch GD and mini-batch GD?
1
vote
1answer
77 views

Why is the sample size of stochastic gradient descent a power of 2?

I watched the video lecture of cs224: Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 2 – Word Vectors and Word Senses. They take the sample size of the window to be $2^5 = 32$ or $2^6 ...