Forwarded from Python Courses & Resources
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Python Tip: Operator Overloading
This is a very important concept in Python.
π https://t.me/Python53
This is a very important concept in Python.
Have you ever wondered how #Python understands what the + operator means? For numbers, it's addition; for strings, it's concatenation; for lists, it's union. This is operator overloading in action.ο»Ώ
Operator overloading means defining special behavior for operators (+, -, *, ==, etc.) in your user-defined classes. You determine how these operators should work with your objects.
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SVFR β a full-fledged framework for restoring faces in videos.
It can:
Essentially, the model takes old or damaged videos and makes them "as if they were shot yesterday". And it's free and open-source.
1. Create an environment
conda create -n svfr python=3.9 -y
conda activate svfr
2. Install PyTorch (for your CUDA)
pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2
3. Install dependencies
pip install -r requirements.txt
4. Download models
conda install git-lfs
git lfs install
git clone https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt models/stable-video-diffusion-img2vid-xt
5. Start processing videos
python infer.py \
--config config/infer.yaml \
--task_ids 0 \
--input_path input.mp4 \
--output_dir results/ \
--crop_face_region
Where task_ids:
*
0 β face enhancement*
1 β colorization*
2 β redrawing damageAn ideal tool if:
#python #soft #github
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Forwarded from Python Courses & Resources
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The matrix cookbook.pdf
676.5 KB
π Notes and Important Formulas β¬
οΈ "Matrices, Linear Algebra, and Probability"
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π #DataScience #Python #Math
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π¨π»βπ» This booklet serves as an essential resource for individuals initiating their studies in data science. It consolidates comprehensive information on matrices, linear algebra, and probability, thereby eliminating the necessity of consulting multiple sources.
βοΈ The document encompasses nearly all pertinent formulas and key concepts. It addresses foundational topics such as determinants and matrix inverses, as well as advanced subjects including eigenvalues, eigenvectors, Singular Value Decomposition (SVD), and probability distributions.
https://t.me/CodeProgrammer
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Do you want to learn how to implement an artificial neural network from scratch in Python using NumPy? ππ
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Ever wondered why activation functions are so critical in neural networks? π€π€
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Do you want to learn how to implement an artificial neural network from scratch in Python using NumPy? ππ
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reader3 πβ¨
When you want to connect an AI like Gemini to help you analyze books or content, copying text from a reader usually becomes a hassle. π©π»
Especially if you want to discuss a book by chapters. Highlighting text manually and copying it disrupts the flow and feels like a waste of time. β³π«
Yesterday, Andrzej Karpati, a well-known AI expert, released a new project to the public: reader3, which solves this problem very neatly. ππ οΈ It's a lightweight EPUB reader that allows you to read a book together with AI. π€π
Its interface is as minimalist as possible: only the necessary reading and navigation functions. ππ§ You can also manage your library through folders. πβ¨
The key feature is that it breaks an EPUB into chapters and displays the content one chapter at a time. ππ
This makes it easy to copy the needed part of the book and pass it to a large model for analysis or discussion. ππ It significantly improves the reading experience when paired with AI. ππ§
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π Language: #Python 61.0%
βοΈ Stars: 1.5k
β‘οΈ Link to GitHub https://github.com/karpathy/reader3
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When you want to connect an AI like Gemini to help you analyze books or content, copying text from a reader usually becomes a hassle. π©π»
Especially if you want to discuss a book by chapters. Highlighting text manually and copying it disrupts the flow and feels like a waste of time. β³π«
Yesterday, Andrzej Karpati, a well-known AI expert, released a new project to the public: reader3, which solves this problem very neatly. ππ οΈ It's a lightweight EPUB reader that allows you to read a book together with AI. π€π
Its interface is as minimalist as possible: only the necessary reading and navigation functions. ππ§ You can also manage your library through folders. πβ¨
The key feature is that it breaks an EPUB into chapters and displays the content one chapter at a time. ππ
This makes it easy to copy the needed part of the book and pass it to a large model for analysis or discussion. ππ It significantly improves the reading experience when paired with AI. ππ§
And it's very easy to get started - just run two commands via uv. β‘π οΈ As a result, it's an excellent tool for those who love reading and want to use AI as a companion for text analysis. ππ€π€
π Language: #Python 61.0%
βοΈ Stars: 1.5k
β‘οΈ Link to GitHub https://github.com/karpathy/reader3
#AI #Python #Reader3 #Tech #BookLovers #Github
https://t.me/CodeProgrammer
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Forwarded from Learn Python Coding
Cheat sheet on the basics of Python: ππ
basic syntax and language rules π
scalar types β basic data types (int, float, bool, str, NoneType) π’
datetime β working with date and time π β°
data structures β Python data structures (list, tuple, dict, set) π
list β mutable lists for storing data collections π
tuple β immutable sequences of values π
dict (hash map) β storing data in a key-value format π
set β unique elements without order π
slicing β obtaining parts of sequences through indices and step βοΈ
module/library β connecting modules and libraries π
help functions β using help() and dir() to explore the Python API π
#Python #Coding #DataScience #Programming #Tech #DevCommunity
basic syntax and language rules π
scalar types β basic data types (int, float, bool, str, NoneType) π’
datetime β working with date and time π β°
data structures β Python data structures (list, tuple, dict, set) π
list β mutable lists for storing data collections π
tuple β immutable sequences of values π
dict (hash map) β storing data in a key-value format π
set β unique elements without order π
slicing β obtaining parts of sequences through indices and step βοΈ
module/library β connecting modules and libraries π
help functions β using help() and dir() to explore the Python API π
#Python #Coding #DataScience #Programming #Tech #DevCommunity
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Forwarded from Machine Learning
π Master Binary Classification with Neural Networks! π§ β¨
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Binary classification is at the heart of many machine learning applications. π―π€
Our super-detailed guide walks you through the entire process step by step. ππ
π‘ Dive in and start building your own neural network today! ππ₯
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#MachineLearning #NeuralNetworks #Python #DataScience #AI #Tech
Ever wondered how to build a neural network from scratch in Python using NumPy? ππ
Binary classification is at the heart of many machine learning applications. π―π€
Our super-detailed guide walks you through the entire process step by step. ππ
π‘ Dive in and start building your own neural network today! ππ₯
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Forwarded from Data Analytics
Pandas vs Polars vs DuckDB: Which Library Should You Choose? π€π
pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows ππ. Polars focus on fast, memory-efficient DataFrame processing β‘πΎ, while DuckDB brings a SQL-first approach for querying local files and embedded analytics ποΈπ.
Each tool fits a different kind of local data workflow π οΈ. In this article, we compare pandas, Polars, and DuckDB across performance, architecture, interoperability, and real-world use cases ππ.
More: https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/ π
#DataScience #Pandas #Polars #DuckDB #Python #Analytics
pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows ππ. Polars focus on fast, memory-efficient DataFrame processing β‘πΎ, while DuckDB brings a SQL-first approach for querying local files and embedded analytics ποΈπ.
Each tool fits a different kind of local data workflow π οΈ. In this article, we compare pandas, Polars, and DuckDB across performance, architecture, interoperability, and real-world use cases ππ.
More: https://www.analyticsvidhya.com/blog/2026/05/pandas-vs-polars-vs-duckdb/ π
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Forwarded from Machine Learning
π A huge open-source course on AI Engineering from scratch
In the repository, we've collected:
β 435 lessons;
β 320+ hours of content;
β Python, TypeScript, and Rust;
β AI agents, MCP servers, prompts, and AI skills.
Moreover, almost every lesson includes practical tasks, so this isn't just theory, but a full-fledged roadmap for AI Engineering. π
βοΈ Link to the repository
https://github.com/rohitg00/ai-engineering-from-scratch
#AI #MachineLearning #Python #Rust #OpenSource #Tech
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In the repository, we've collected:
β 435 lessons;
β 320+ hours of content;
β Python, TypeScript, and Rust;
β AI agents, MCP servers, prompts, and AI skills.
Moreover, almost every lesson includes practical tasks, so this isn't just theory, but a full-fledged roadmap for AI Engineering. π
βοΈ Link to the repository
https://github.com/rohitg00/ai-engineering-from-scratch
#AI #MachineLearning #Python #Rust #OpenSource #Tech
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Stop discovering ML Python libraries one random tutorial at a time π
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 π.
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 π.
Key features:
β’ 920-project index β a large scan-friendly map of open-source ML Python projects πΊοΈ
β’ 34 categories β browse by area like ML frameworks, NLP, image data, AutoML, deployment, interpretability, and more π§©
β’ Quality-score ranking β projects are ordered using an automated score from repo and package-manager signals βοΈ
β’ Rich project metadata β entries show signals like stars, forks, issues, contributors, activity, downloads, and dependencies π
β’ Weekly updates + contributions β the list is updated regularly and can be improved via issues, PRs, or projects.yaml edits π
Itβs open-source (CC BY-SA 4.0 license) π.
https://github.com/lukasmasuch/best-of-ml-python π
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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 π.
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 π.
Key features:
β’ 920-project index β a large scan-friendly map of open-source ML Python projects πΊοΈ
β’ 34 categories β browse by area like ML frameworks, NLP, image data, AutoML, deployment, interpretability, and more π§©
β’ Quality-score ranking β projects are ordered using an automated score from repo and package-manager signals βοΈ
β’ Rich project metadata β entries show signals like stars, forks, issues, contributors, activity, downloads, and dependencies π
β’ Weekly updates + contributions β the list is updated regularly and can be improved via issues, PRs, or projects.yaml edits π
Itβs open-source (CC BY-SA 4.0 license) π.
https://github.com/lukasmasuch/best-of-ml-python π
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