Honestly it felt a lot like topping out on that 5.9 climbing route (say what you like) you've been projecting for 6 months.In my present job I need to provide accurate technical advice to researchers concerning the best computational resources for various research problems. Parallel processing architectures are necessary to achieve the exponential performance gains of the past and those gains are necessary to solve bigger and more complex problems. To use parallel architectures one must employ efficient, parallel algorithms many of which are used in machine learning. These factors (and a perverse, philosophical interest in AI) motivated me to take Andrew Ng's much touted online Machine Learning course.
I learned how supervised and unsupervised
learning algorithms work, wrote efficient, vectorized code to implement these algorithms AND brushed up
on linear algebra and partial differential equations!Many other courses are offered for FREE on Coursera. Sure, you have to pay the big bucks (and spend years) to get that coveted Stanford degree but you can learn what you need to know about all sorts of knowledge domains from the top experts in their fields for FREE. Now, what do you need to learn?
P.S.did I mention that it was FREE?
Full disclosure - I did pay 79 bucks for this certificate I can hang on my wall, easily the best $79 I ever spent on college education!
Softmax cost function equation courtesy of Stanford's UFLDL web site
