today I've completed following course at coursera:
"Neural Networks for Machine Learning".
I should admit, that this course was great but for me to pass all of it presented a challenge. But also I shoud notice that neural networks for machine learning was really informative course. I should admit that for me it was very interesting to learn more about perceptrons then I new. Remind myself about restricted boltzmann machine. Very discoverable for me was explanation about recurrent neural networks and how to derive math for recurrent neural networks. And much much more.
Also some parts were missing for me. For me it was hard to grasp about probabilities and Bayesian statistics usage for Deep Belief nets and Deep learning. I hope in future new versions Geof Hinton use little bit another words in order to explain Boltzmann machine, deep belief nets.
Eye opening for me was his explanation of autoencoders and language processing. I never thought about modelling language or modelling hierarchical data.
And of course, I want to leave proves of my learning:
If you follow this link, you'll see my certificate.
Also you can Download pdf version,
And below goes also screenshot of this cerfitificate: