Can you say me the diffirence between SGD and back propogation?

Backpropagation is an efficient method of **computing gradients** in directed graphs of computations, such as neural networks. This is **not** a learning method, but rather a nice computational trick which is **often used in learning methods**. This is actually a simple implementation of **chain rule** of derivatives, which simply gives you ability to compute all required partial derivatives in linear time in terms of the graph size (while naive gradient computations would scale exponentialy with depth).

SGD is one of many optimization methods, namely **first order optimizer**, meaning, that it is based on analysis of the **gradient** of the objective. Consequently, in terms of neural networks it is often applied together with backprop to make an efficient updates. You could also apply SGD to gradients obtained in a different way (from sampling, numerical approximators etc.). Symmetrically you can use other optimization techniques with backprop as well, everything that can use gradient/jacobian.

This common missconception comes from the fact, that for simplicity people sometimes say "trained with backprop", what actually means (if they do not specify optimizer) "trained with SGD using backprop as a gradient computing technique". Also, in old textbooks you can find things like "delta rule" and other a bit confusing terms, which describe exactly the same thing (as neural network community was for a long time a bit independent from general optimization community).

Thus you have two layers of abstraction:

- gradient computation - where backprop comes to play
- optimization level - where techniques like SGD, Adam, Rprop, BFGS etc. come into play, which (if they are first order or higher) use gradient computed above