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There’s also the ML as a service (MLaaS) movement that lowers the barrier for common ML capabilities (eg image object detection and audio transcription). Basically, you use APIs. See: https://aws.amazon.com/machine-learning/. Source: over 1 year ago
Do you have questions about Data Science and ML on AWS - https://aws.amazon.com/machine-learning/. Source: about 3 years ago
For neural network theory, I'm very fond of http://neuralnetworksanddeeplearning.com, but you'll need calculus and some linear algebra to understand it. Source: 5 months ago
Alternatively, building them from scratch in numpy is definitely possible and an excellent way to learn the fundamentals. It will take some time though (debugging issues with a hand-rolled LSTM is very, very painful). There's a zillion tutorials/books out there (I think I started with http://neuralnetworksanddeeplearning.com). Source: 5 months ago
- http://neuralnetworksanddeeplearning.com/ The Watch the Caltech telecourse. - Source: Hacker News / 8 months ago
Neural Networks and Deep Learning, a free online book. http://neuralnetworksanddeeplearning.com/. - Source: Hacker News / 9 months ago
Many years ago I was studying deep learning using this resource: * http://neuralnetworksanddeeplearning.com/ I decided to try to implement everything from scratch in Elixir (after initially doing all the math with pen and paper on a trivial example to get the feel of it). Obviously pure elixir was extremely slow, so I started creating NIFs to pass over matrix multiplication to OpenBLAS. Then I was thinking more... - Source: Hacker News / 11 months ago
Machine Learning Playground - Breathtaking visuals for learning ML techniques.
Colornet - Neural Network to colorize grayscale images
Lobe - Visual tool for building custom deep learning models
Quick Draw Game - Can a neural network learn to recognize doodles?
Apple Machine Learning Journal - A blog written by Apple engineers
AETROS - Create, train and monitor deep neural networks