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  3. eriklindernoren
  4. ML-From-Scratch
PythonVisible projectStale activityNo linked package signalPartial snapshot

eriklindernoren/ML-From-Scratch

Rank not captured·Top 100·All-time stars·◐Cached ranking snapshot·Updated Aug 15, 2026

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.

Compare closest alternativesOpen GitHub

First read

Useful directional context, not a final verdict yet

eriklindernoren/ML-From-Scratch is better read as a directional signal than a clean recommendation. Keep the snapshot conservative and validate source activity, package reality, and close alternatives before committing to it.

Visible

32.5K public stars in the current GitStar snapshot.

Recognizable in the ecosystem

Active

Last commit Oct 15, 2023.

Stale activity

Adopted

Treat stars as discovery context until a linked package appears.

No linked package mapping

Confidence

One or more key signals are partial, so GitStar keeps the interpretation conservative.

Partial snapshot

Snapshot facts

  • 32.5K stars
  • 5.4K forks
  • Last commit Oct 15, 2023
  • Package usage not mapped yet

Compare lens

academic/awesome-datascience and tangyudi/Ai-Learn are the closest comparison targets GitStar found. A side-by-side comparison usually tells you more than a single raw rank.

Signal trail

Trajectory

Read the recent motion first. This block is for deciding whether the repository still looks alive, compounding, or flattening before you trust stars alone.

Daily momentum
Recent momentum is not captured
Weekly momentum
Recent momentum is not captured
Monthly momentum
Recent momentum is not captured
Last commit
Oct 15, 2023

Package reality

No linked npm or PyPI package is mapped for this repository yet, so the page leans more heavily on GitHub-visible popularity and should be read more conservatively.

GitStar expects a package signal here, but no npm or PyPI package is linked to this repository yet.

Hype vs Reality
Insufficient Package Data
Package mapping
No linked package yet
Cross-links
Standalone repo read

Validation note

GitStar can summarize public signals for eriklindernoren/ML-From-Scratch, but the GitHub repository is still the primary place to confirm release cadence, issue activity, and maintainer intent.

data-miningdata-sciencedeep-learningdeep-reinforcement-learninggenetic-algorithmmachine-learningmachine-learning-from-scratch
🛡️ Editorial Context

GitStar surfaces public popularity and package signals. These rankings are not endorsements, security reviews, or investment advice.

Why this rank

This repository stands out because it combines stable visibility.

Stable visibility
📈 Momentum & Adoption Signals
Approximate star trajectory

Reconstructed from current stars and cached daily/weekly/monthly deltas.

Now: 32.5K
30d ago7d ago1d agoNow
Daily momentum
No signal
Weekly momentum
No signal
Monthly momentum
No signal
Signal mix (log-scaled for readability)
Current stars32.5K
🧭 Hype vs Reality
Insufficient Package Data

GitStar can see repository momentum, but it does not have a reliable linked package signal yet.

Treat stars and recent movement as discovery context only until npm or PyPI usage is available.

Status
Insufficient Package Data
Package footprint
No linked npm or PyPI usage signal
Method note
This label appears when GitStar cannot find strong enough package telemetry to compare attention against adoption. Compare it against other Python repos before treating stars as a moat.
📐 Read the Hype vs Reality heuristic
Related alternative

academic/awesome-datascience

Shares the learning category footprint with eriklindernoren/ML-From-Scratch, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

tangyudi/Ai-Learn

Shares the learning category footprint with eriklindernoren/ML-From-Scratch, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

keras-team/keras

Shares the learning category footprint with eriklindernoren/ML-From-Scratch, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

streamlit/streamlit

Shares the learning category footprint with eriklindernoren/ML-From-Scratch, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Research links

Cross-links

Compare against related repos

GitStar picked awesome-datascience + Ai-Learn as the closest next comparison from the related repository set.

Open compare presetCompare with awesome-datascienceCompare with Ai-Learn
Repo utility

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Wider nearby ecosystem

Compare with awesome-datascienceCompare with Ai-Learn
Unknown
29.8K stars
academic/awesome-datascience

:memo: An awesome Data Science repository to learn and apply for real world problems.

Unknown
13.3K stars
tangyudi/Ai-Learn

人工智能学习路线图，整理近200个实战案例与项目，免费提供配套教材，零基础入门，就业实战！包括：Python，数学，机器学习，数据分析，深度学习，计算机视觉，自然语言处理，PyTorch tensorflow machine-learning,deep-learning data-analysis data-mining mathematics data-science artificial-intelligence python tensorflow tensorflow2 caffe keras pytorch algorithm numpy pandas matplotlib seaborn nlp cv等热门领域

Python
64.2K stars
keras-team/keras

Deep Learning for humans

Python
45.5K stars
streamlit/streamlit

Streamlit — A faster way to build and share data apps.

Python
43.5K stars
ray-project/ray

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Python
43.4K stars
gradio-app/gradio

Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!

Next step after the validation read

Move into a compare preset, organization view, or the heuristic notes once this first fold tells you whether the repo looks visible, active, adopted, and credible enough to keep researching.
Compare the closest alternativesRead the heuristic

Learn and methodology

Keep trust-building context reachable, but behind the first data read instead of ahead of it.
GuideMethodologyArticlesWeekly Digest

About This Page

This page provides a quick overview of eriklindernoren/ML-From-Scratch based on GitStar's cached data. The signal chart reconstructs approximate checkpoints from current stars plus cached daily, weekly, and monthly star deltas, so it is best read as directional context rather than as a precise historical audit log.

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