Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.

Admin: @HusseinSheikho || @Hussein_Sheikho
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⚡️ Colorizing old black-and-white videos and "bringing faces to life" for FREE

SVFR — a full-fledged framework for restoring faces in videos.

It can:
💬 BFR — improve blurry faces.
💬 Colorization — colorize black-and-white videos.
💬 Inpainting — redraw damaged areas.
💬 and combine all of this in one pass.

Essentially, the model takes old or damaged videos and makes them "as if they were shot yesterday". And it's free and open-source.

⚙️ Installation locally:

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 — face enhancement
* 1 — colorization
* 2 — redrawing damage

An ideal tool if:
🟢you're restoring archival videos;
🟢you're creating historical content;
🟢you're working with neural networks and video effects;
🟢you want a wow result without paid services.

▶️ Demo on Hugging Face

♎️ GitHub/Instructions

#python #soft #github

https://t.iss.one/CodeProgrammer
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The matrix cookbook.pdf
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📚 Notes and Important Formulas ⬅️ "Matrices, Linear Algebra, and Probability"

👨🏻‍💻 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.

✏️ 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.

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