My favorite way to work with multiple filters in pandas.Series — not a chain of .loc, but a single mask. 🐼

The chain looks neat, but breaks on real data and easily gives unexpected results:

s = pd.Series([10, 15, 20, 25, 30])
s.loc[s > 20].loc[s % 2 == 1]

The problem is that the second .loc again looks at the original s, not the already filtered result. The logic gets messy. 🤯

It's more reliable to gather everything into one expression:

s = pd.Series([10, 15, 20, 25, 30])

mask = (s > 20) & (s % 2 == 1)
result = s.loc[mask]

One mask, one point of truth. ✅

It's easier to debug. Fewer surprises when the code grows. 🚀

#Pandas #Python #DataScience #CodingTips #DataEngineering #Debugging

500 AI/ML/Computer Vision/NLP projects with code 🚀

This is a large collection of 500 ready-made projects in the field of machine learning, deep learning, computer vision, and NLP 🧠

All examples come with code, so you can not just read them, but immediately analyze and run them ⚙️

➡️ Link to GitHub:
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Python Programming for Beginners: Learn Python from Scratch

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Transformers become more understandable when you can "poke" the model directly. 🧠✨

Transformer Explainer is an interactive visualization tool for studying how text-generating transformer-based models, such as GPT, work. 🔍

It helps connect the architecture with real behavior by running a live GPT-2 directly in the browser, allowing you to enter your own text and showing how the internal components work together to predict the next tokens. 🔄📝

Key features: 🌟

- Live GPT-2 in the browser - experiment without setting up a separate model server 💻
- Your own text - try your own prompts and see how the model processes them ✍️
- Internal components - observe the operations working inside the transformer 🔧
- Focus on predicting the next token - link each visual step to the model's predictions 🎯
- Local development - clone the repository, install dependencies, and run via npm for in-depth study ⚙️

It's open-source (MIT license). 📜

https://github.com/poloclub/transformer-explainer

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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. 📖✨

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Top YouTube Channels to Master Tech Skills 🚀

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🔖 A useful training tool for Data Scientists 📊

🫡 Real-world tasks from IT companies;
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⛓ Link to the training tool
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A Powerful Alternative to Pandas 🚀

This is an optimized replacement for Pandas that can significantly speed up data processing without requiring major changes to your code. ⚙️

To get started, simply replace a single import:

import fireducks.pandas as pd

Performance Benchmarks demonstrate speed improvements in various use cases. 📈

More: https://colab.research.google.com/drive/1UIokuJ4cytoiVSabRDqcziDXOan8bVua?usp=sharing

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A collection of resources on MLOps for those who want to understand how machine learning systems are brought to production. 🚀🤖

https://github.com/visenger/awesome-mlops

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🔖 Over 300 real-world case studies of ML systems from top companies. 🤖

We found a repository that collects genuine ML engineering experience – not theory from textbooks, but real stories of implementing models in production. 📚

Inside, you'll find case studies from Uber, Netflix, Google, and other companies: how they built the architecture, what problems arose, where the systems failed, and what solutions helped them recover. 🏗️

⛓ Link to GitHub
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"Introduction to Machine Learning" is another free textbook on machine learning, approximately 600 pages long, which emphasizes a deep mathematical understanding of the subject. 📚🧮

The book begins with the mathematical foundations necessary for further study: linear algebra, mathematical analysis, probability theory, matrix analysis, and optimization methods. It then covers the main supervised learning algorithms: linear and logistic regression, the k-nearest neighbors method, decision trees, random forests, boosting, and neural networks. 🤖📈

A significant portion of the book is dedicated to probabilistic and generative models. It discusses Monte Carlo methods, graphical models, Bayesian networks, variational methods, normalizing flows, variational autoencoders (VAEs), and generative adversarial networks (GANs). 🎲🧠

The final chapters discuss clustering, principal component analysis (PCA), learning on manifolds, and theoretical estimates of a model's ability to generalize. 🔍📊

In my opinion, this is an excellent resource for those who want to gain a broad understanding of machine learning and understand the mathematics underlying the key methods, rather than treating them as "black boxes." 💡✨

https://arxiv.org/pdf/2409.02668

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