Forwarded from Machine Learning with Python
A guide to Loop Engineering has been released — a new approach to working with AI agents
The repository loop-engineering has been published, offering a paradigm shift: instead of manually prompting AI agents, the developer designs a cycle that does this automatically. 🔄🤖
The author notes that most people still use Claude Code, Codex, Cursor, and Grok as a regular chat: prompt → wait → copy → correct → prompt again. Loop Engineering proposes to stop being a "nanny" for the agent and instead build a system where agents work, check, correct, and escalate on their own. 🛠️⚙️
The repository includes ready-made cycles for daily triage, PR, CI, dependencies, changelog, and issues. It includes CLI for creating cycles, evaluating tokens, auditing the repository, and safely running agents via GitHub Actions. 📋✅
"Prompt engineering was about how to write better prompts. Loop engineering is about creating a system where agents continue to work without your supervision at every step," the description says. 🚀🧠
The repository is available on GitHub.
Repository: https://github.com/cobusgreyling/loop-engineering
#LoopEngineering #AI #Agents #GitHub #DevOps #Automation
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The repository loop-engineering has been published, offering a paradigm shift: instead of manually prompting AI agents, the developer designs a cycle that does this automatically. 🔄🤖
The author notes that most people still use Claude Code, Codex, Cursor, and Grok as a regular chat: prompt → wait → copy → correct → prompt again. Loop Engineering proposes to stop being a "nanny" for the agent and instead build a system where agents work, check, correct, and escalate on their own. 🛠️⚙️
The repository includes ready-made cycles for daily triage, PR, CI, dependencies, changelog, and issues. It includes CLI for creating cycles, evaluating tokens, auditing the repository, and safely running agents via GitHub Actions. 📋✅
"Prompt engineering was about how to write better prompts. Loop engineering is about creating a system where agents continue to work without your supervision at every step," the description says. 🚀🧠
The repository is available on GitHub.
Repository: https://github.com/cobusgreyling/loop-engineering
#LoopEngineering #AI #Agents #GitHub #DevOps #Automation
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A Chinese developer has released an open-source replacement for NumPy that performs calculations on GPUs. It's called CuPy 🚀. In many cases, it's enough to replace a single line:
The same code can run on CUDA up to 100 times faster ⚡️.
What it can do:
→ Compatible with existing NumPy and SciPy code 🛠️.
→ No need to rewrite the program or learn new syntax 📝.
→ Supports not only CUDA but also AMD ROCm 💻.
The project is completely open-source 📂:
🔗 https://github.com/cupy/cupy
#Python #GPU #NumPy #CuPy #AI #DeepLearning
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import cupy as cp
The same code can run on CUDA up to 100 times faster ⚡️.
What it can do:
→ Compatible with existing NumPy and SciPy code 🛠️.
→ No need to rewrite the program or learn new syntax 📝.
→ Supports not only CUDA but also AMD ROCm 💻.
The project is completely open-source 📂:
🔗 https://github.com/cupy/cupy
#Python #GPU #NumPy #CuPy #AI #DeepLearning
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Don't learn ML by randomly jumping through tutorials. 🚫📚
DS-ML Bootcamp is a public repository for a Data Science and machine learning course for beginners who want a structured path from zero to practical projects. 🚀📊
It helps transition from installation and concepts to practical ML work, organizing lessons, assignments, code examples, datasets, and solutions around the main machine learning workflow. 🛠️🧠
Key features:
- End-to-end workflow - covers data collection, preprocessing, train/test split, model selection, training, evaluation, and deployment 🔄📈
- Lesson-based structure - starts with tools/setup, Data Science, ML, data fundamentals, and regression 📚🧮
- Practical materials - assignments give learners structured tasks, not just reading notes ✍️✅
- Code + datasets - Python examples and raw CSV datasets included for exercises 🐍📂
- Set up for repetition - the README says you can clone the repository and use Jupyter or VS Code while going through lessons 💻🔁
Free public repository on GitHub. 🆓
https://github.com/goobolabs/ds-ml-bootcamp
#MachineLearning #DataScience #Coding #Python #AI #Learning
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DS-ML Bootcamp is a public repository for a Data Science and machine learning course for beginners who want a structured path from zero to practical projects. 🚀📊
It helps transition from installation and concepts to practical ML work, organizing lessons, assignments, code examples, datasets, and solutions around the main machine learning workflow. 🛠️🧠
Key features:
- End-to-end workflow - covers data collection, preprocessing, train/test split, model selection, training, evaluation, and deployment 🔄📈
- Lesson-based structure - starts with tools/setup, Data Science, ML, data fundamentals, and regression 📚🧮
- Practical materials - assignments give learners structured tasks, not just reading notes ✍️✅
- Code + datasets - Python examples and raw CSV datasets included for exercises 🐍📂
- Set up for repetition - the README says you can clone the repository and use Jupyter or VS Code while going through lessons 💻🔁
Free public repository on GitHub. 🆓
https://github.com/goobolabs/ds-ml-bootcamp
#MachineLearning #DataScience #Coding #Python #AI #Learning
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GitHub
GitHub - goobolabs/ds-ml-bootcamp: Data Science and Machine Learning Bootcamp. (Jun - 2026)
Data Science and Machine Learning Bootcamp. (Jun - 2026) - goobolabs/ds-ml-bootcamp
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The math.perm() method
The math.perm() method in Python returns the number of ways to select k elements from n elements, with and without repetition. 🧮
Syntax:
Where:
n: The number of elements from which k elements are selected.
k: The number of elements that are selected.
In the first example, the method returns the number of ways to select 3 elements from 5 elements. The result is 60 ways. 📊
In the second example, the method returns the number of ways to select 5 elements from 10 elements. The result is 252 ways. 🚀
#Python #Math #Coding #Programming #DataScience #Tech
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The math.perm() method in Python returns the number of ways to select k elements from n elements, with and without repetition. 🧮
Syntax:
math.perm(n, k)
Where:
n: The number of elements from which k elements are selected.
k: The number of elements that are selected.
In the first example, the method returns the number of ways to select 3 elements from 5 elements. The result is 60 ways. 📊
In the second example, the method returns the number of ways to select 5 elements from 10 elements. The result is 252 ways. 🚀
#Python #Math #Coding #Programming #DataScience #Tech
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Machine Learning
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Cheat sheet for Scikit-learn: 📚 Scikit-learn is a Python library for machine learning.
📥 Loading Data - downloading and preparing data.
🧼 Preprocessing - standardization, normalization, and feature processing.
🏗️ Create Your Model - creating models for classification, regression, and clustering.
🎯 Model Fitting - training the model on data.
🔮 Prediction - obtaining forecasts.
📊 Evaluate Performance - assessing the quality of the model using various metrics.
🔄 Cross-Validation - checking the model on different samples.
⚙️ Tune Your Model - optimizing parameters using Grid Search and Randomized Search.
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📥 Loading Data - downloading and preparing data.
🧼 Preprocessing - standardization, normalization, and feature processing.
🏗️ Create Your Model - creating models for classification, regression, and clustering.
🎯 Model Fitting - training the model on data.
🔮 Prediction - obtaining forecasts.
📊 Evaluate Performance - assessing the quality of the model using various metrics.
🔄 Cross-Validation - checking the model on different samples.
⚙️ Tune Your Model - optimizing parameters using Grid Search and Randomized Search.
#ScikitLearn #MachineLearning #Python #DataScience #AI #MLOps
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Towards Data Science
How Far Can Classical NLP Go? From Bag-of-Words to Stacking on Spooky Author Identification | Towards Data Science
An end-to-end classical NLP experiment on Kaggle’s Spooky Author Identification task: from Vowpal Wabbit and TF-IDF/NB-SVM baselines to a tuned stacked ensemble, with a compact representation survey of Bag-of-Words, BM25, Word2Vec, and FastText for context.
🚀 Looking for a portfolio-ready NLP project?
I recently published an end-to-end walkthrough on Towards Data Science using Kaggle’s Spooky Author Identification dataset.
You’ll see how far classical NLP can go with:
📝 Bag-of-Words and TF-IDF
🔤 Character n-grams
📊 Model comparison
🧩 Ensemble stacking
It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step:
🔗 https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/
I recently published an end-to-end walkthrough on Towards Data Science using Kaggle’s Spooky Author Identification dataset.
You’ll see how far classical NLP can go with:
📝 Bag-of-Words and TF-IDF
🔤 Character n-grams
📊 Model comparison
🧩 Ensemble stacking
It’s a practical project for anyone preparing for an ML/DS role, with no deep learning required. I walk through the entire workflow step by step:
🔗 https://towardsdatascience.com/how-far-can-classical-nlp-go-from-bag-of-words-to-stacking-on-spooky-author-identification/
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🔖 The Legendary MIT Textbook on Mathematics for Computer Science
Mathematics for Computer Science is one of the best free textbooks for developers, ML engineers, and data scientists.
It contains over 1000 pages covering discrete mathematics, logic, graphs, probability, combinatorics, recurrence relations, and other fundamental topics.
⛓️ Link to the textbook:
https://people.csail.mit.edu/meyer/mcs.pdf
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Mathematics for Computer Science is one of the best free textbooks for developers, ML engineers, and data scientists.
It contains over 1000 pages covering discrete mathematics, logic, graphs, probability, combinatorics, recurrence relations, and other fundamental topics.
⛓️ Link to the textbook:
https://people.csail.mit.edu/meyer/mcs.pdf
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Combining Plots in Matplotlib 📊
In Matplotlib, you can easily combine multiple plots in a single window using the subplot() function. Simply create the necessary plots, specify their layout, add titles, and you'll get a clear visualization for easy data comparison.
#Matplotlib #DataVisualization #Python #DataScience #Coding #Plotting
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In Matplotlib, you can easily combine multiple plots in a single window using the subplot() function. Simply create the necessary plots, specify their layout, add titles, and you'll get a clear visualization for easy data comparison.
#Matplotlib #DataVisualization #Python #DataScience #Coding #Plotting
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Forwarded from Machine Learning with Python
Reinforcement Learning Methods and Tutorials 🧠📚
In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.
Learning Resources: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow 🚀
Here's a collection of simple materials on methods and practical guides, covering both basic reinforcement learning algorithms and modern, recently developed, and updated advanced algorithms. 📖✨
#ReinforcementLearning #MachineLearning #AI #DeepLearning #TechTutorials #DataScience
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In these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years.
Learning Resources: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow 🚀
Here's a collection of simple materials on methods and practical guides, covering both basic reinforcement learning algorithms and modern, recently developed, and updated advanced algorithms. 📖✨
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