Is stochastic gradient descent an incremental machine learning method? How does stochastic gradient descent relate to incremental machine learning/ online learning?
Also, I am new to incremental ML and need to build my knowledge about it .. Any good beginners references you can recommend will be appreciated :-)
It can be used as such; however stochastic gradient descent is, simply, a framework.
One reference, that may be useful, as a starting point, is this: https://hal.archives-ouvertes.fr/jpa-00211064/document
This is an excellent question and the answer is no. Machine learning algorithms based on gradient-descent are not incremental in general: they would forget what they've previously learned if you use them in an incremental setting. For example, if you first feed/train your model with the image of a car and later with the image of a cat, the model forgets the car (the former). This is called catastrophic forgetting. To tackle this, one has to feed/train the model in an IID manner, which means that the data of each mini-batch must be representative of all classes and images.
Please refer to following papers for further information:
Article Fast Federated Learning by Balancing Communication Trade-Offs
Preprint Fast Federated Learning by Balancing Communication Trade-Offs
Dear Alia Alkameli ,
Stochastic gradient descent is a very popular and common algorithm used in various Machine Learning algorithms, most importantly forms the basis of Neural Networks. In this article, I have tried my best to explain it in detail, yet in simple terms. I highly recommend going through linear regression before proceeding with this article.
Gradient, in plain terms means slope or slant of a surface. So gradient descent literally means descending a slope to reach the lowest point on that surface.
https://towardsdatascience.com/stochastic-gradient-descent-clearly-explained-53d239905d31?gi=61c9645e9ef9
Regards,
Shafagat
Stam Nicolis Thanks for your response. I will take a look at the paper you attached.
Milad Khademi Nori Thanks for your answer and your explanation.
And also thanks for introducing a new term "federated learning" :-D
I'll take a look at the papers and come back to you with more questions.
Kindly read this.
https://www.quora.com/What-is-the-difference-between-incremental-gradient-and-stochastic-gradient-descent
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