Geoff Hinton's Coursera Course Almost Ended

I am now in the final week of the course (see previous post) and just have the final exam to complete. The course has been very intensive, very interesting and much more difficult than the first machine learning course I took. Personally, the big take aways from this course for the things that I want to do are:

• Softmax activation function for output layers. I intend to replace my current use of the Sigmoid function in the output layer of my standby neural net with this Softmax function. The Softmax is far more suitable for my intended classification purposes.
• Octave code for using momentum to speed up the training of a neural net.
• Restricted Boltzmann machines, the stacking thereof and deep learning, and unsupervised learning. I shall talk more about this in a future post.

With regard to the training of my standby neural net, it is mostly completed. I say mostly because as soon as I learned about the above mentioned items I stopped training it once I had trained it on sufficient data to cover almost 99% of the dominant cycle periods to be found in the data. It seemed pointless to continue training it with increasing training times and diminishing returns, particularly since it is destined to be remodelled and retrained using what I've just learned. For now I will subject it to cross validation testing and if it passes this, I shall deploy it for a short period until such time as it is replaced by the neural net I have in mind following on from the course.