How to get a job in Google DeepMind or How to excel in Machine Learning from scratch! - Deepstash
How to get a job in Google DeepMind or How to excel in Machine Learning from scratch!

How to get a job in Google DeepMind or How to excel in Machine Learning from scratch!

Curated from: The AI Epiphany

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ML and main obstacles to learning it [solved]

ML and main obstacles to learning it [solved]

  • ML is a super useful field.
  • Too many resources causes decision fatigue but its really easy to learn it, no matter your background.
  • Most courses are reiterating on the same things.

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STEP1: Learn to code (in Python)

STEP1: Learn to code (in Python)

All the python knowledge you need resources!

✅ video: https://www.youtube.com/watch?v=rfscV...

✅ book: https://automatetheboringstuff.com/

Learn everything else about Python on the fly.

use google, stackoveflow, geeksforgeeks.

TIME : 1 MONTH INCLUDING PRACTICE (EASY)

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STEP2: Get a high-level intro to ML (Coursera)

STEP2: Get a high-level intro to ML (Coursera)

Learn ML (high-level intro):

INPUT MODE -

( ingest knowledge that you do not know)

✅ course: https://www.coursera.org/learn/machin...

✅ course: https://www.coursera.org/specializati...

OUTPUT MODE -

+ at least 1 open-source project (on github or some website as public artifact)

+ 1 blog ( output something practical by structuring your knowledge)

TIME : 2 MONTH INCLUDING PRACTICE (INTENSIVE)

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STEP3: Getting deeper (fast.ai)

STEP3: Getting deeper (fast.ai)

We are getting deeper into the mid level of knowledge pyramind for ML learning.

Learn ML (middle-level intro)

✅ course: https://course.fast.ai/

✅ course: https://course19.fast.ai/part2

Again code, code, code. Open-source some projects on GitHub. Focus on one specific DL framework.(PyTorch)

TIME : 6 MONTH INCLUDING PRACTICE (MODERATE)

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STEP4: Read and implement research papers

STEP4: Read and implement research papers

  • You may need to read papers before or go back to basics after reading a paper, this is not a linear process.
  • This is tough and its gonna suck but learning how to read paper is important for ML engineers and researchers a lot.
  • Start reading research papers and implement 1 paper from scratch. You will learn so much.
  • use https://arxiv.org/

TIME: life long, gets easier with time.

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STEP5: Get strong mathematical foundations

STEP5: Get strong mathematical foundations

You need somebody in your team who knows math for real.

Learn ML (pro)

✅ MML book: https://mml-book.github.io/book/mml-b...

Supplements:

✅ 3B1B LA: https://www.youtube.com/watch?v=fNk_z...

✅ 3B1B Calculus: https://www.youtube.com/watch?v=WUvTy...

✅ chapter 5: https://jakevdp.github.io/PythonDataS... Learn deep learning (pro)

✅ book: https://github.com/janishar/mit-deep-... (you'll find a good pdf in this GitHub repo)

TIME: at least 1 year, more possibly.

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BONUS TIPS

BONUS TIPS

TIP1: Learn how to learn

TIP2: Learn only the tools you need

TIP3: Get into the output mode (code, blog)

TIP4: Focus (the field is too broad)

TIP5: Follow AI people on Twitter

TIP6: Watch Lex's AI podcast

BONUS TIPS:

✅ course: https://www.coursera.org/learn/learni...

✅ podcast: https://www.youtube.com/user/lexfridman

✅ Twitter the people I follow: https://twitter.com/gordic_aleksa

✅ My GitHub: https://github.com/gordicaleksa

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8 reads

IDEAS CURATED BY

anivana

Improve the process

CURATOR'S NOTE

I find machine learning to be the most satisfying education for everybody who has passed through the ordeals of learning it.

Anirban Chatterjee 's ideas are part of this journey:

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