Autonomous AI research on Apple Silicon
Port of the project Karpathy’s autoresearch for Apple Silicon based on MLX, which implements autonomous research cycles with control via program.md 🍏
What’s interesting:
• native support for Apple Silicon without PyTorch/CUDA
• fixed training budget (~5 minutes)
• logging of results in results.tsv
• simple structure for autonomous experiments
• optimization of models for more efficient operation
https://github.com/trevin-creator/autoresearch-mlx 🔬
#AppleSilicon #AIResearch #MLX #AutonomousAI #MachineLearning #OpenSource
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Port of the project Karpathy’s autoresearch for Apple Silicon based on MLX, which implements autonomous research cycles with control via program.md 🍏
What’s interesting:
• native support for Apple Silicon without PyTorch/CUDA
• fixed training budget (~5 minutes)
• logging of results in results.tsv
• simple structure for autonomous experiments
• optimization of models for more efficient operation
https://github.com/trevin-creator/autoresearch-mlx 🔬
#AppleSilicon #AIResearch #MLX #AutonomousAI #MachineLearning #OpenSource
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❤7
Transformer implementations for vision, audio, and AI agents 🤖👁️🎵
Repo: https://github.com/Nicolepcx/transformers-the-definitive-guide
#AI #MachineLearning #Vision #Audio #Agents #Tech
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Repo: https://github.com/Nicolepcx/transformers-the-definitive-guide
#AI #MachineLearning #Vision #Audio #Agents #Tech
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✅ 13 full courses live right now
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Stop discovering ML Python libraries one random tutorial at a time 🛑
Best-of Machine Learning with Python is a curated GitHub index of open-source machine learning Python libraries for builders who need a faster way to compare the ecosystem 📚.
It helps you shortlist tools by grouping projects into categories and ranking them with a project-quality score based on metrics collected from GitHub and package managers 📊.
Key features:
• 920-project index – a large scan-friendly map of open-source ML Python projects 🗺️
• 34 categories – browse by area like ML frameworks, NLP, image data, AutoML, deployment, interpretability, and more 🧩
• Quality-score ranking – projects are ordered using an automated score from repo and package-manager signals ⚙️
• Rich project metadata – entries show signals like stars, forks, issues, contributors, activity, downloads, and dependencies 📈
• Weekly updates + contributions – the list is updated regularly and can be improved via issues, PRs, or projects.yaml edits 🔄
It’s open-source (CC BY-SA 4.0 license) 📜.
https://github.com/lukasmasuch/best-of-ml-python 🔗
#MachineLearning #Python #ML #OpenSource #DataScience #TechStack
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Best-of Machine Learning with Python is a curated GitHub index of open-source machine learning Python libraries for builders who need a faster way to compare the ecosystem 📚.
It helps you shortlist tools by grouping projects into categories and ranking them with a project-quality score based on metrics collected from GitHub and package managers 📊.
Key features:
• 920-project index – a large scan-friendly map of open-source ML Python projects 🗺️
• 34 categories – browse by area like ML frameworks, NLP, image data, AutoML, deployment, interpretability, and more 🧩
• Quality-score ranking – projects are ordered using an automated score from repo and package-manager signals ⚙️
• Rich project metadata – entries show signals like stars, forks, issues, contributors, activity, downloads, and dependencies 📈
• Weekly updates + contributions – the list is updated regularly and can be improved via issues, PRs, or projects.yaml edits 🔄
It’s open-source (CC BY-SA 4.0 license) 📜.
https://github.com/lukasmasuch/best-of-ml-python 🔗
#MachineLearning #Python #ML #OpenSource #DataScience #TechStack
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Forwarded from Machine Learning
Data leakage is one of the main reasons why ML demos look impressive... and then fail in production. 📉
The model didn't become smarter.
It just happened to see the correct answers in advance.
In 4 minutes, you'll understand where data leaks hide. 🔍
Let's break it down below: 👇
1. Data Leakage 🕳️
Data leakage occurs when information that won't be available at the time of actual prediction is used during the model training process.
Because of this, metrics on the validation stage can look much better than the actual quality of the model on new, previously unseen data.
2. Model Evaluation ⚖️
The test set isn't just "additional data".
It's a simulation of the future.
Only train the model on the information that would have been available to you at the time of prediction.
Evaluate it on examples that the model couldn't have influenced during training.
3. Direct Leakage 🚨
This is the most obvious type of leakage.
Examples:
- a field with information from the future;
- an ID that encodes the target variable;
- a variable that appears only after an event has occurred;
- duplicate records in both the training and test sets.
If a feature doesn't exist at the time of inference (prediction), then it's likely a source of data leakage.
4. Indirect Leakage 🕵️
This is the type of leakage that most often traps teams.
You perform normalization, imputation, feature selection, outlier removal, or dimensionality reduction before splitting the data into a training and test set.
The model didn't directly see the data from the test set.
But your preprocessing pipeline already saw it.
5. Train/Test Split ✂️
Wrong:
Right:
The same idea applies to imputers, encoders, feature selection, PCA, and any preprocessing step that is trained on the data.
6. Cross-Validation 🔄
Each fold is a mini-experiment with a training and test set.
Therefore, preprocessing should be performed within each fold.
If you prepared the entire dataset once and then ran cross-validation, each fold would already have had access to its held-out data.
7. Pipelines 🛠️
A pipeline isn't just a way to make the code cleaner.
It's also a defense against data leakage.
Combine preprocessing, feature selection, and the model into a single pipeline, and then pass this pipeline to cross-validation or hyperparameter search (grid search).
8. AI Engineering Version 🤖
Data leaks also occur in RAG systems and when evaluating LLMs.
Leakage occurs when you tune chunks, prompts, re-rankers, thresholds, or examples on the same evaluation dataset that you later present as "held-out".
As a result, your benchmark turns into training data.
9. Leakage Checklist ✅
Before trusting the obtained metric, ask yourself:
- Could this feature exist at the time of prediction?
- Was any transformation (transform) step trained (fit) on the test data?
- Did cross-validation include the entire pipeline?
- Were we tuning parameters on the final evaluation dataset?
If the answer is "yes", then the metric likely doesn't reflect the actual quality of the model.
#MachineLearning #DataScience #MLOps #DataLeakage #ArtificialIntelligence #TechTips
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The model didn't become smarter.
It just happened to see the correct answers in advance.
In 4 minutes, you'll understand where data leaks hide. 🔍
Let's break it down below: 👇
1. Data Leakage 🕳️
Data leakage occurs when information that won't be available at the time of actual prediction is used during the model training process.
Because of this, metrics on the validation stage can look much better than the actual quality of the model on new, previously unseen data.
2. Model Evaluation ⚖️
The test set isn't just "additional data".
It's a simulation of the future.
Only train the model on the information that would have been available to you at the time of prediction.
Evaluate it on examples that the model couldn't have influenced during training.
3. Direct Leakage 🚨
This is the most obvious type of leakage.
Examples:
- a field with information from the future;
- an ID that encodes the target variable;
- a variable that appears only after an event has occurred;
- duplicate records in both the training and test sets.
If a feature doesn't exist at the time of inference (prediction), then it's likely a source of data leakage.
4. Indirect Leakage 🕵️
This is the type of leakage that most often traps teams.
You perform normalization, imputation, feature selection, outlier removal, or dimensionality reduction before splitting the data into a training and test set.
The model didn't directly see the data from the test set.
But your preprocessing pipeline already saw it.
5. Train/Test Split ✂️
Wrong:
fit the scaler on all data → split the data → evaluate
Right:
split the data → fit the scaler only on the training set → apply it to both the training and test sets
The same idea applies to imputers, encoders, feature selection, PCA, and any preprocessing step that is trained on the data.
6. Cross-Validation 🔄
Each fold is a mini-experiment with a training and test set.
Therefore, preprocessing should be performed within each fold.
If you prepared the entire dataset once and then ran cross-validation, each fold would already have had access to its held-out data.
7. Pipelines 🛠️
A pipeline isn't just a way to make the code cleaner.
It's also a defense against data leakage.
Combine preprocessing, feature selection, and the model into a single pipeline, and then pass this pipeline to cross-validation or hyperparameter search (grid search).
8. AI Engineering Version 🤖
Data leaks also occur in RAG systems and when evaluating LLMs.
Leakage occurs when you tune chunks, prompts, re-rankers, thresholds, or examples on the same evaluation dataset that you later present as "held-out".
As a result, your benchmark turns into training data.
9. Leakage Checklist ✅
Before trusting the obtained metric, ask yourself:
- Could this feature exist at the time of prediction?
- Was any transformation (transform) step trained (fit) on the test data?
- Did cross-validation include the entire pipeline?
- Were we tuning parameters on the final evaluation dataset?
If the answer is "yes", then the metric likely doesn't reflect the actual quality of the model.
#MachineLearning #DataScience #MLOps #DataLeakage #ArtificialIntelligence #TechTips
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Telegram
AI PYTHON 🌟
You’ve been invited to add the folder “AI PYTHON 🌟”, which includes 14 chats.
❤6
Forwarded from Github Top Repositories
🌟 DataTalksClub/data-engineering-zoomcamp caught my eye on GitHub Trending today.
🔗 https://github.com/DataTalksClub/data-engineering-zoomcamp
📝 Data Engineering Zoomcamp is a free 9-week course on building production-ready data pipelines. The next cohort starts in January 2026. Join the course here 👇🏼
──────────────────────────────
The Data Engineering Zoomcamp is a free 9-week course that covers the fundamentals of data engineering. It's designed to help you build an end-to-end data pipeline from scratch, with hands-on experience using industry-standard tools and best practices.
Key features of the course include structured modules, hands-on workshops, and a final project to reinforce your learning. You'll learn about
The course is suitable for anyone with basic coding experience and familiarity with
The course has a strong community and support system, with a dedicated #course-data-engineering channel on Slack for discussions and troubleshooting.
The course is taught by experienced instructors, including Alexey Grigorev and Michael Shoemaker, and is sponsored by companies like Kestra and Bruin.
Overall, the Data Engineering Zoomcamp is a great resource for anyone looking to learn data engineering fundamentals and build a career in the field.
So, what are you waiting for? Join the course and start building your skills today - it's a
──────────────────────────────
🧠 Channel: https://t.me/GithubRe
🔗 https://github.com/DataTalksClub/data-engineering-zoomcamp
📝 Data Engineering Zoomcamp is a free 9-week course on building production-ready data pipelines. The next cohort starts in January 2026. Join the course here 👇🏼
──────────────────────────────
The Data Engineering Zoomcamp is a free 9-week course that covers the fundamentals of data engineering. It's designed to help you build an end-to-end data pipeline from scratch, with hands-on experience using industry-standard tools and best practices.
Key features of the course include structured modules, hands-on workshops, and a final project to reinforce your learning. You'll learn about
containerization, infrastructure as code, workflow orchestration, data warehousing, and analytics engineering. The course is suitable for anyone with basic coding experience and familiarity with
SQL. No prior data engineering experience is necessary. You can enroll in the course by registering for the next cohort or following the self-paced learning path.The course has a strong community and support system, with a dedicated #course-data-engineering channel on Slack for discussions and troubleshooting.
The course is taught by experienced instructors, including Alexey Grigorev and Michael Shoemaker, and is sponsored by companies like Kestra and Bruin.
Overall, the Data Engineering Zoomcamp is a great resource for anyone looking to learn data engineering fundamentals and build a career in the field.
So, what are you waiting for? Join the course and start building your skills today - it's a
free 9-week course that can change your career!──────────────────────────────
🧠 Channel: https://t.me/GithubRe
❤5
Interactive Explainer 🧠✨
The Anatomy of an LLM 🔍
A visual walk through the machinery inside a large language model: from raw text, to tokens, to vectors, to attention, to the next token. ⚙️🧬
🔗 Link: https://www.royvanrijn.com/anatomy-of-an-llm/
#LLM #AI #Tech #NeuralNetworks #MachineLearning #DeepLearning
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The Anatomy of an LLM 🔍
A visual walk through the machinery inside a large language model: from raw text, to tokens, to vectors, to attention, to the next token. ⚙️🧬
🔗 Link: https://www.royvanrijn.com/anatomy-of-an-llm/
#LLM #AI #Tech #NeuralNetworks #MachineLearning #DeepLearning
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Roy van Rijn
The Anatomy of an LLM | Interactive Visual Guide to How Language Models Work
An interactive visual explainer for developers showing how LLMs work, from tokenization and embeddings to attention, transformers, training, KV cache, and quantization.
❤8
Forwarded from Machine Learning
FREE MIT books on AI and Machine Learning: 📚🤖
1. Foundations of Machine Learning cs.nyu.edu/~mohri/mlbook/
2. Understanding Deep Learning udlbook.github.io/udlbook/
3. Introduction to Machine Learning Systems ❯ Vol 1: mlsysbook.ai/vol1/assets/do ❯ Vol 2: mlsysbook.ai/vol2/assets/do
4. Algorithms for ML algorithmsbook.com
5. Deep Learning deeplearningbook.org
6. Reinforcement Learning andrew.cmu.edu/course/10-703/
7. Distributional Reinforcement Learning direct.mit.edu/books/oa-monog
8. Multi Agent Reinforcement Learning marl-book.com
9. Agents in the Long Game of AI direct.mit.edu/books/oa-monog
10. Fairness and Machine Learning fairmlbook.org
11. Probabilistic Machine Learning
❯ Part 1 : probml.github.io/pml-book/book1
❯ Part 2 : probml.github.io/pml-book/book2
#MIT #AI #MachineLearning #DeepLearning #ReinforcementLearning #FreeBooks
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1. Foundations of Machine Learning cs.nyu.edu/~mohri/mlbook/
2. Understanding Deep Learning udlbook.github.io/udlbook/
3. Introduction to Machine Learning Systems ❯ Vol 1: mlsysbook.ai/vol1/assets/do ❯ Vol 2: mlsysbook.ai/vol2/assets/do
4. Algorithms for ML algorithmsbook.com
5. Deep Learning deeplearningbook.org
6. Reinforcement Learning andrew.cmu.edu/course/10-703/
7. Distributional Reinforcement Learning direct.mit.edu/books/oa-monog
8. Multi Agent Reinforcement Learning marl-book.com
9. Agents in the Long Game of AI direct.mit.edu/books/oa-monog
10. Fairness and Machine Learning fairmlbook.org
11. Probabilistic Machine Learning
❯ Part 1 : probml.github.io/pml-book/book1
❯ Part 2 : probml.github.io/pml-book/book2
#MIT #AI #MachineLearning #DeepLearning #ReinforcementLearning #FreeBooks
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📚 13 live now · 40+ in 2–3 weeks · full library in ~2 months
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The discount disappears with the last seat. Don't sleep on it.
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Machine Learning with Python
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Don't miss this opportunity!
Once you register, you will receive future courses for free.
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