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H2O

Advanced astronaut Difficulty 5/5 Resource list Stop 31 / 34 ages teen+

Open source distributed machine learning platform written in Java with APIs in R, Python, and Scala.

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Your guide

AI-written overview

What is it?

H2O is in our Computer Science group, a list of resources about the ideas behind computing, like how algorithms and data work. We only know the title, short description and level from our list.

Is it for me?

It is for curious people who want to know why computers work, not only how to use them. Many items are Advanced or Intermediate, so it suits about age teen+ and up.

Grown-ups: some material is university level. A hard item is not a problem; just choose an easier one.

How to start

  1. Read the description and see if the idea sounds interesting.
  2. Open the original site with a grown-up and read the first section.
  3. Draw or write the main idea in your own words.

How much time?

Plan about 80 minutes at a time over about 9 sessions. (estimate)

80 min per session 9 sessions estimate

A plan you can follow

  1. Day 1 30 min Read the first section.
  2. Week 1 45 min Take notes and try one example.
  3. Week 2 60 min Explain the idea to someone.
  4. Week 3 60 min Look for the idea in a program you know.

Times are estimates. Go at your own speed.

Go to the learning site

You are about to leave our site to visit H2O on another website. We don't control that site. Ask a grown-up before you go. Check the original for details. This site may not be in your language. Your browser can translate the page: look for the translate button near the address bar, or ask a grown-up to help.

You are leaving our site

Grown-ups: this link goes to a site we do not control and have not reviewed beyond its listing. Please look at it first.

Go to the learning site (opens the original site in a new tab)

https://github.com/h2oai/awesome-h2o#readme

On another site. From the list: sindresorhus/awesome · License: CC0-1.0 · Computer Science

How to learn from a GitHub project or list

Your path through Python

Stop 29 Build a Deep Learning Library Build project Stop 30 Build Deep Learning From Scratch (reimplement PyTorch internals across 34 stages) Build project Stop 31 H2O Resource list Stop 32 Optical Character Recognition (OCR) Build project Stop 33 RAG for Document Search Build project Stop 34 SlowTorch: Implementation of PyTorch from the ground up in 100% pure Python Build project

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