Machine Learning with Python

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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* 1 — colorization * 2 — redrawing damage An ideal tool if: 🟢you're restoring archival videos; 🟢you're creating historical content; 🟢you're working with neural networks and video effects; 🟢you want a wow result without paid services.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.me/CodeProgrammer

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𝐕𝐢𝐬𝐮𝐚𝐥 𝐛𝐥𝐨𝐠 on Vision Transformers is live. https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web Learn how ViT works from the ground up, and fine-tune one on a real classification dataset.

CNNs process images through small sliding filters. Each filter only sees a tiny local region, and the model has to stack many layers before distant parts of an image can even talk to each other. Vision Transformers threw that whole approach out. ViT chops an image into patches, treats each patch like a token, and runs self-attention across the full sequence. Every patch can attend to every other patch from the very first layer. No stacking required. That global view from layer one is what made ViT surpass CNNs on large-scale benchmarks. 𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐛𝐥𝐨𝐠 𝐜𝐨𝐯𝐞𝐫𝐬: - Introduction to Vision Transformers and comparison with CNNs - Adapting transformers to images: patch embeddings and flattening - Positional encodings in Vision Transformers - Encoder-only structure for classification - Benefits and drawbacks of ViT - Real-world applications of Vision Transformers - Hands-on: fine-tuning ViT for image classification The Image below shows Self-attention connects every pixel to every other pixel at once. Convolution only sees a small local window. That's why ViT captures things CNNs miss, like the optical illusion painting where distant patches form a hidden face. The architecture is simple. Split image into patches, flatten them into embeddings (like words in a sentence), run them through a Transformer encoder, and the class token collects info from all patches for the final prediction. Patch in, class out. Inside attention: each patch (query) compares itself to all other patches (keys), softmax gives attention weights, and the weighted sum of values produces a new representation aware of the full image, visualizes what the CLS token actually attends to through attention heatmaps. The second half of the blog is hands-on code. I fine-tuned ViT-Base from google (86M params) on the Oxford-IIIT Pet dataset, 37 breeds, ~7,400 images. 𝐁𝐥𝐨𝐠 𝐋𝐢𝐧𝐤 https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web

𝐒𝐨𝐦𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 ViT paper dissection https://youtube.com/watch?v=U_sdodhcBC4 Build ViT from Scratch https://youtube.com/watch?v=ZRo74xnN2SI Original Paper https://arxiv.org/abs/2010.11929 https://t.me/CodeProgrammer

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The Python + Generative AI series by Azure AI Foundry has ended, but all materials are open Now you can calmly rewatch the recordings, download the slides, and try the code from each session — from LLM and RAG to AI agents and MCP. All resources are here: aka.ms/pythonai/resources 👉  @codeprogrammer

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🤖 Python libraries for AI agents — what to study If you want to develop AI agents in Python, it's important to understand the order of studying libraries. Start with LangChain, CrewAI or SmolAgents — they allow you to quickly assemble simple agents, connect tools, and test ideas. The next level is LangGraph, LlamaIndex and Semantic Kernel. These tools are already used for production systems: RAG, orchestration, and complex workflows. The most complex level is AutoGen, DSPy and A2A. They are needed for autonomous multi-agent systems and optimizing LLM pipelines. LangChain — simple agents, tools, and memory  github.com/langchain-ai/langchain CrewAI — multi-agent systems with roles  github.com/joaomdmoura/crewAI SmolAgents — lightweight agents for quick experiments  github.com/huggingface/smolagents LangGraph — orchestration and stateful workflow  github.com/langchain-ai/langgraph LlamaIndex — RAG and knowledge-agents  github.com/run-llama/llama_index Semantic Kernel — AI workflow and plugins  github.com/microsoft/semantic-kernel AutoGen — autonomous multi-agent systems  github.com/microsoft/autogen DSPy — optimizing LLM pipelines  github.com/stanfordnlp/dspy A2A — protocol for interaction between agents  github.com/a2aproject/A2A https://t.me/CodeProgrammer 🌟

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