Tag: machinlearning

Tutorial: Demystifying Deep Learning for Data Scientists

Tutorial: Demystifying Deep Learning for Data Scientists

In this great tutorial for PyCon 2020, Eric Ma proposes a very simple framework for machine learning, consisting of only three elements:

  1. Model
  2. Loss function
  3. Optimizer

By adjusting the three elements in this simple framework, you can build any type of machine learning program.

In the tutorial, Eric shows you how to implement this same framework in Python (using jax) and implement linear regression, logistic regression, and artificial neural networks all in the same way (using gradient descent).

I can’t even begin to explain it as well as Eric does himself, so I highly recommend you watch and code along with the Youtube tutorial (~1 hour):

If you want to code along, here’s the github repository: github.com/ericmjl/dl-workshop

Have you ever wondered what goes on behind the scenes of a deep learning framework? Or what is going on behind that pre-trained model that you took from Kaggle? Then this tutorial is for you! In this tutorial, we will demystify the internals of deep learning frameworks – in the process equipping us with foundational knowledge that lets us understand what is going on when we train and fit a deep learning model. By learning the foundations without a deep learning framework as a pedagogical crutch, you will walk away with foundational knowledge that will give you the confidence to implement any model you want in any framework you choose.

https://www.youtube.com/watch?v=gGu3pPC_fBM
A free, self-taught education in Computer Science!

A free, self-taught education in Computer Science!

The Open Source Society University offers a complete education in computer science using online materials.

They offer a proper introduction to the fundamental concepts for all computing disciplines. Evyerthing form algorithms, logic, and machine learning, up to databases, full stack web development, and graphics is covered. Moreover, you will acquire skills in a variety of languages, including Python, Java, C, C++, Scala, JavaScript, and many more.

According to their GitHub page, the curriculum is suited for people with the discipline, will, and good habits to obtain this education largely on their own, but who’d still like support from a worldwide community of fellow learners.

Curriculum

  • Intro CS: for students to try out CS and see if it’s right for them
  • Core CS: corresponds roughly to the first three years of a computer science curriculum, taking classes that all majors would be required to take
  • Advanced CS: corresponds roughly to the final year of a computer science curriculum, taking electives according to the student’s interests
  • Final Project: a project for students to validate, consolidate, and display their knowledge, to be evaluated by their peers worldwide
  • Pro CS: graduate-level specializations students can elect to take after completing the above curriculum if they want to maximize their chances of getting a good job

It is possible to finish Core CS within about 2 years if you plan carefully and devote roughly 18-22 hours/week to your studies. Courses in Core CS should be taken linearly if possible, but since a perfectly linear progression is rarely possible, each class’s prerequisites are specified so that you can design a logical but non-linear progression based on the class schedules and your own life plans.

Links to the contents

Links to the curriculum (v8.0.0)