We’ve been talking a lot about machine learning lately. People are using it for speech generation and recognition, computer vision, and even classifying radio signals. If you’ve yet to climb the learning curve, you might be interested in a new free class from Google using TensorFlow.
Of course, we’ve covered tutorials for TensorFlow before, but this is structured as a 15 hour class with 25 lessons and 40 exercises. Of course, it is also from the horse’s mouth, so to speak. Google says the class will answer questions like:
- How does machine learning differ from traditional programming?
- What is loss, and how do I measure it?
- How does gradient descent work?
- How do I determine whether my model is effective?
- How do I represent my data so that a program can learn from it?
- How do I build a deep neural network?
Google says you should be adept at intro level algebra and that higher math could be helpful, although not essential. You should also know something about programming with some familiarity in Python. The exercises run in your browser, so you don’t need any exotic set up. There are also a few tools that have suggested tutorials if you aren’t up to speed on them already. For example, the pandas library and bash are included in that list.
If you get really serious, Google have a lot of educational resources that will take you further. If you learn better by example, you might try getting naked. If you want an even longer class, try this one.
9 thoughts on “Machine Learning Crash Course From Google”
“Google says you should be adept at intro level algebra and that higher math could be helpful, although not essential.”
Might need if if you’re aiming for those million dollar jobs.
Google is 7 years behind our firm
I did like the Caltech CS 156 “Learning From Data” AI course by Prof. Abu-Mostafa, and also liked *a lot* the the Berkeley CS188 Artificial Intelligence courses from Profs. Klein and Abbeel.
The former course has more math but both are hands-on programming with lots of great exercises.
The Stanford CS373 “Artificial Intelligence for Robotics” from Prof. Thrun and CS229 “Machine Learning” from Prof. Ng
are ok and fun also, but not even close to the other ones above.
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And how do we measure it?
Have you tried a banana?
or a pointed stick!!
Is that you Mr. Apricot?
how small do these boxes get?
Great post, thanks.
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