Super harsh guide to machine Learning

Super harsh guide to machine learning (reddit)

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.


Exploring Stochastic Gradient Descent with Restarts (SGDR)

Using Machine Learning on Compute Engine to Make Product Recommendations

CRISP-DM: проверенная методология для Data Scientist-ов (Russian) Original CRISP-DM methodology

Why your neutral network is not working

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.

Demystifying deep reinforcement learning

Feature Engineering, о чём молчат online-курсы (Rus.)