christianversloot/machine-learning-articles

Analysis updated 2026-07-03

★ 3,680Audience · generalComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((machine-learning-articles))
    What it is
      Article archive 2019-2022
      MachineCurve backup
      Reading resource no code
    Topics covered
      Convolutional networks
      GANs generative models
      BERT and language models
      Clustering DBSCAN
    Who its for
      ML learners
      Practitioners looking up concepts
      Beginners in deep learning

mindmap root((machine-learning-articles)) What it is Article archive 2019-2022 MachineCurve backup Reading resource no code Topics covered Convolutional networks GANs generative models BERT and language models Clustering DBSCAN Who its for ML learners Practitioners looking up concepts Beginners in deep learning

Click or tap to explore — scroll the page freely

What do people build with it?

USE CASE 1

Read practical explanations of machine learning topics like convolutional networks, GANs, and BERT at an accessible level.

USE CASE 2

Use as a reference archive when looking up how a specific ML algorithm or technique works, without needing to run any code.

USE CASE 3

Study a broad ML curriculum spanning 2019-2022 covering both classic and modern approaches.

How does it compare?

	christianversloot/machine-learning-articles	cleardusk/3ddfa	ctubio/krypto-trading-bot
Stars	3,680	3,680	3,680
Language	—	Python	C++
Setup difficulty	easy	hard	hard
Complexity	1/5	4/5	4/5
Audience	general	researcher	developer

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

No license explicitly stated in the explanation.

In plain English

This repository is an archive of machine learning articles written by Christian Versloot between May 2019 and February 2022, originally published on his website MachineCurve.com. When the site was taken down, he moved the articles here so they would remain publicly accessible. The topics listed in the repository metadata cover a broad range of machine learning subjects, including convolutional neural networks, deep learning, generative models known as GANs, clustering algorithms like DBSCAN, and language models such as BERT and ALBERT. These are all practical and educational topics that practitioners and learners in the field commonly look up. The README itself is very short and does not describe the format of the articles, how they are organized, or how many there are. Based on the description, this appears to be a reading resource rather than runnable code.

Copy-paste prompts

Prompt 1

I'm reading the MachineCurve BERT article from this archive. Explain how BERT handles masked language modeling in plain English, then show me how to fine-tune it for text classification in Python.

Prompt 2

The archive has articles on GANs. Explain how a GAN's generator and discriminator train against each other, then give me a minimal working GAN in PyTorch.

Prompt 3

I found a MachineCurve article on DBSCAN clustering. How does DBSCAN differ from k-means and when should I choose it for my dataset?

Prompt 4

Help me implement a convolutional neural network from scratch in PyTorch using the concepts described in the MachineCurve CNN articles.

Prompt 5

I'm a beginner reading the machine-learning-articles archive. What order should I read the topics in to build up from basics to deep learning?

Frequently asked questions

What is machine-learning-articles?

An archive of practical machine learning articles originally published on MachineCurve.com between 2019 and 2022, covering CNNs, GANs, BERT, clustering algorithms, and more, preserved here after the site was taken down.

What license does machine-learning-articles use?

No license explicitly stated in the explanation.

How hard is machine-learning-articles to set up?

Setup difficulty is rated easy, with roughly 5min to a first successful run.

Who is machine-learning-articles for?

Mainly general.

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