Super harsh guide to machine Learning
First, read fucking Hastie, Tibshirani, and whoever. Chapters 1-4 and 7. If you don’t understand it, keep reading it until you do.
You can read the rest of the book if you want. You probably should, but I’ll assume you know all of it.
Take Andrew Ng’s Coursera. Do all the exercises in Matlab and python and R. Make sure you get the same answers with all of them.
Now forget all of that and read the deep learning book. Put tensorflow or torch on a Linux box and run examples until you get it. Do stuff with CNNs and RNNs and just feed forward NNs.
Once you do all of that, go on arXiv and read the most recent useful papers. The literature changes every few months, so keep up.
There. Now you can probably be hired most places. If you need resume filler, so some Kaggle competitions. If you have debugging questions, use StackOverflow. If you have math questions, read more. If you have life questions, I have no idea.
Deep Feature Flow for Video Recognition - position itself as a framework for video recognition. paper and source code are available. Pictures are nice.
Awesome TensorFlow - collection of links about Tensor flow
“The Best Public Datasets for Machine Learning and Data Science” by Stacy Stanford https://link.medium.com/qDHawlpg4X
collated list of image and video databases that people have found useful for computer vision research and algorithm evaluation. http://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.htm
How To Improve Deep Learning Performance, 2016, Jason Brownlee. Recipies about performance improvements.
Divided in 4 subtopics:
- Improve Performance With Data.
- Improve Performance With Algorithms.
- Improve Performance With Algorithm Tuning.
- Improve Performance With Ensembles.
Towards Data Science – medium based website with interesting articles about data science.