This is a list of links to articles, blogposts, videos, tutorials that I have found very useful on my ML journey. It serves as a useful reference point and is ever-growing.

Setup

Tensorflow or Pytorch?

Google Colab

I love articles, tutorials, videos by the following:

Adrian Rosebrock Pyimagesearch.
Andrej Karpathy blog.
Chris Olah blog.
Andrew Ng Coursera.
Francois Chollet Book.
Jeremey Howard Fast.ai.
Rachel Thomas Fast.ai.
Chris Albon Great flashcards - Buy them!.
Aakash Nain Jovian.
Hannah Fry [Makes number fun!]http://www.hannahfry.co.uk().
Jason Brownlee Tutorials.

Powerful Quotes

  • F Chollet:
    “You don’t need to know everything. You don’t really need a formal background in this or that – though it helps, you don’t even need a PhD. You do, however, need to be constantly learning, be curious, read books. Don’t be “too busy” to learn, or otherwise proud of your ignorance.”
    “Honestly, the question is not, and has never been, “ can ML replace radiologists/etc” (which won’t happen in the foreseeable future). The question is, how can radiology/etc utilise ML to improve outcomes, decrease the cost of car, and broaden accessibility.”

  • Geoff Hinton:
    “Read enough sp you start developing intuitions and then trust your intuitions and go for it!.”

  • Andrew Ng:
    “Deep Learning is a superpower. With it you can make a computer see, synthesize novel art, translate languages, render a medical diagnosis, or build pieces of a car that can drive itself. If that isn’t a superpower, I don’t know what is.”

Ignore stuff below.

You can include alert boxes

…and…

You can include info boxes

Python code and output:

# Prints '2'
print(1+1)

2

Tables

| Tensorflow | Pytorch | |-|-| | Keras | Fastai | —