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ageron
handson-ml3
Jupyter Notebook2022–2023
ageron / handson-ml3

handson-ml3

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

2Distinct people
2Mentions
0Explicit recommenders
14.5KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month2 dated mentions

Observed mentions: 2022-11-18 14:58:41 UTC to 2023-09-06 16:27:33 UTC.

012022-11-01 00:00:00 UTC to 2022-12-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsNov 20222022-12-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2023-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-02-01 00:00:00 UTC to 2023-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-03-01 00:00:00 UTC to 2023-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-04-01 00:00:00 UTC to 2023-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsApr 20232023-05-01 00:00:00 UTC to 2023-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-06-01 00:00:00 UTC to 2023-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-07-01 00:00:00 UTC to 2023-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-08-01 00:00:00 UTC to 2023-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-09-01 00:00:00 UTC to 2023-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsSep 2023

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Nov 20222022-11-01 00:00:00 UTC2022-12-01 00:00:00 UTC101
Dec 20222022-12-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
Jan 20232023-01-01 00:00:00 UTC2023-02-01 00:00:00 UTC000
Feb 20232023-02-01 00:00:00 UTC2023-03-01 00:00:00 UTC000
Mar 20232023-03-01 00:00:00 UTC2023-04-01 00:00:00 UTC000
Apr 20232023-04-01 00:00:00 UTC2023-05-01 00:00:00 UTC000
May 20232023-05-01 00:00:00 UTC2023-06-01 00:00:00 UTC000
Jun 20232023-06-01 00:00:00 UTC2023-07-01 00:00:00 UTC000
Jul 20232023-07-01 00:00:00 UTC2023-08-01 00:00:00 UTC000
Aug 20232023-08-01 00:00:00 UTC2023-09-01 00:00:00 UTC000
Sep 20232023-09-01 00:00:00 UTC2023-10-01 00:00:00 UTC101

What Hacker News says

2 sources
My suggestion: Go to kaggle.com. Sign-up / Sign-in as necessary. Click the "code" tab in the left hand navigation bar. On the ensuing page, select the "beginner" filter, and then change the sort to "by most votes". https://www.kaggle.com/code?sortBy=voteCount&tagIds=13102 The top few results there will probably be good for your friend. This Titanic one looks pretty good, but note that it uses R, which might be off-putting to some people. https://www.kaggle.com/code/mrisdal/exploring-survival-on-th... But browse around, there are several that look pretty comprehensive and well documented at a first glance. --- Also, if your friend wants a book to go along with their learning, look at "Hands On Machine Learning" by Geron. It's a great book anyway, AND the Github repo includes a whole series of Jupyter notebooks to go along with the book, in
Read the rest
cluding a handy "Open in Colab" link that will launch the notebooks directly in Colab. See: https://github.com/ageron/handson-ml3 https://www.oreilly.com/library/view/hands-on-machine-learni...
In Good “Hello World” Intro to Machine Learning for Curious App Developer?
It’s extremely difficult. Through Meetups, Discord, … A group of us made it through Hands on ML last year. https://github.com/ageron/handson-ml3 But that group didn’t want to do heavy theory but someone from another Meetup did so I invited them to our group. Then someone who didn’t want to do the theory changed their mind. Try meeting people online with a well-defined common goal. For example, not simply “I want to learn machine learning” but I want to study a particular book or MOOC. Getting people to agree can be difficult. No one else in my group wants to do Kaggle, for instance. Lots of cool project ideas that don’t go anywhere. Discord seems like it should be the perfect place to meet people but most servers aren’t very active. Here’s an OpenCourseWare server I found a few days ago that’s disappointingly not very active: https://disc
Read the rest
ord.gg/CSKeQegY Ideally math, physics, writing, history, or whatever could be served by dedicated servers.
In Ask HN: What habits did you achieve this year?

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