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Forget about the code part. It's the least difficult part.
http://blog.digitalmind.io/post/artificial-intelligence-reso...
You should have the equivalent of an undergraduate degree in mathematical statistics (calculus, linear algebra, et al). It should take about 4 years of full time study to achieve that. Forget about the code part. It's the least difficult part.
With modern tools and frameworks you can start learning and applying what you know on practice almost immediately.
Check out Keras and the book "Deep Learning with Python"[1]. They have enabled me to train my first ANN in 2 days, and get to the point of building a MNIST recognizer in a month(and I was reading it pretty slowly).
Sure, if you're coding it from scratch and must understand every signle detail, you do need like 10 years and 3 PhD's. But that's not a wise way to learn.
I recommend to take the simplest tools, and apply them to practical projects immediately. That will give you the general overview of how things work, and then you will learn the details as needed.
[1] https://machinelearningmastery.com/deep-learning-with-python...
Slight tangent, so bare with me. Every other week, posts such as this come up, asking how to learn X, so I was wondering if there is any Github repo or some website that keeps track of all the resources posted here?
"Your package manager for knowledge".
(mostly focused around ML)
I too felt like ML is something new to try, but the lack of real world use cases on a small scale ( not google, Microsoft, ... ) Has kept me from trying/doing.
I only saw the farm with image recognition for vegetables as an example for now.
Anyone has other examples?
Mathematical Monk - https://www.youtube.com/user/mathematicalmonk#p/c/0/ydlkjtov... (includes a probability primer)
Awesome Courses - https://github.com/prakhar1989/awesome-courses - its a very extensive list of university courses including subjects apart from Machine Learning as well
Programming Collective Intelligence - http://www.amazon.com/programming-collective-intelligence-bu... - heard very good reviews about this
Many other resources available apart from the above. You can access more such resources at http://www.tutorack.com/search?subject=machine%20learning
I think its a good idea to go through one or more beginner level courses like that offered by Andrew Ng on Coursera and then do an actual project.
[Disclaimer - I work at tutorack.com mentioned in the comment]
You should have the equivalent of an undergraduate degree in mathematical statistics (calculus, linear algebra, et al). It should take about 4 years of full time study to achieve that. Forget about the code part. It's the least difficult part.
I think that this is a horribly impractical advice, and I keep seeing it everywhere. With modern tools and frameworks you can start learning and applying what you know on practice almost immediately. Check out Keras and the book "Deep Learning with Python"[1]. They have enabled me to train my first ANN in 2 days, and get to the point of building a MNIST recognizer in a month(and I was reading it pretty slowly). Sure,…
Slight tangent, so bare with me. Every other week, posts such as this come up, asking how to learn X, so I was wondering if there is any Github repo or some website that keeps track of all the resources posted here?
There's a very interesting project: https://metacademy.org "Your package manager for knowledge". (mostly focused around ML)