Machine Learning lab
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Welcome to Machine Learning Lab! Explore machine learning and data science with discussions, tutorials, and resources. Discover insights in ML approaches, Projects and practical applications. Admin: @kian_bd
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پستهای کانال
| 2 | Microsoft Data Formulator
https://www.youtube.com/watch?v=ySWRDqrzKrk
@Machine_learning_lab_K | 215 |
| 3 | Methods and Strategies for Building and Refining Reasoning Models
https://sebastianraschka.com/blog/2025/understanding-reasoning-llms.html
@Machine_learning_lab_K | 192 |
| 4 | AI Agents Course
https://huggingface.co/learn/agents-course/en/unit0/introduction
@Machine_learning_lab_K | 190 |
| 5 | Data Engineering Zoomcamp: A Free 9-Week Course on Data Engineering Fundamentals
https://github.com/DataTalksClub/data-engineering-zoomcamp
@Machine_learning_lab_K | 180 |
| 6 | What Makes a Reward Model a Good Teacher? An Optimization Perspective
https://arxiv.org/abs/2503.15477
@Machine_learning_lab_K | 181 |
| 7 | 🧠 Bain MRI Dataset 5,000,000+ studies + reports
#KaggleDatasets #DataScience #MachineLearning #DataAnalysis #DataVisualization #OpenData #DataCleaning #TextClassification #NLP #SentimentAnalysis #BigData #APIAutomation #DataLicensing #SocialMediaData #PythonIntegration #DataModeling #kaggle #ComputerVision #python #LLM #DeepLearning #Pytorch #HuggingFace #Dataset
https://t.me/datasets1 | 292 |
| 8 |  🧠 Bain MRI Dataset 5,000,000+ studies + reports
MRI scans of human brains with medical reports
This dataset consists of high-quality #MRI scans of human brains, accompanied by medical reports. It is designed for tasks such as detection, classification, and segmentation of brain abnormalities. The dataset includes a variety of brain scans with different frames and studies, along with clinical information for each patient.
The dataset includes:
structured by series each serie more than 50 frame
Total: 5,000,000+ high-quality DICOM (DCM) frames
Medical reports provide the following details:
Type of study
MRI machine specifications
Patient demographics: Age, sex, race | 286 |
| 9 | Oumi is a fully open-source platform that streamlines the entire lifecycle of foundation models - from data preparation and training to evaluation and deployment. Whether you’re developing on a laptop, launching large scale experiments on a cluster, or deploying models in production, Oumi provides the tools and workflows you need.
Link: https://oumi.ai/docs/en/latest/index.html
@Machine_learning_lab_K | 274 |
| 10 | Are you interested in VLMs and LLMs. Use “oumi” as an ent-to-end solution for learning these hot trends from scratch:
https://oumi.ai/docs/en/latest/index.html
@Machine_learning_lab_K | 262 |
| 11 | 📢 Interested in Shiny applications?
If you’re curious about how Shiny works in real projects, I’ve built a few interactive applications using R and Python. You can check them out on my GitHub!
🔗 GitHub Repositories
Feel free to explore, give feedback, or even contribute! 🚀
#Shiny #DataScience #Python #R #WebApps #MachineLearning
@Machine_learning_lab_K | 274 |
| 12 | **Interested in Shiny applications?**If you’re curious about how Shiny works in real projects, I’ve built a few interactive applications using R and Python. You can check them out on my GitHub!
🔗 GitHub Repositories
Feel free to explore, give feedback, or even contribute! 🚀
#Shiny #DataScience #Python #R #WebApps #MachineLearning | 1 |
| 13 | 🔹 What is Shiny?
Shiny is an open-source framework that allows you to build interactive web applications using R or Python, without requiring extensive web development skills. It enables data scientists and analysts to create dynamic dashboards and data-driven tools effortlessly.
🔹 Why Use Shiny?
✅ Easy to Use – Develop web apps with minimal coding in HTML, CSS, or JavaScript.
✅ Interactive & Reactive – Apps update automatically in response to user inputs.
✅ Supports Data Science – Seamlessly integrates with R/Python packages for visualization, machine learning, and analytics.
✅ Customizable – Extend functionality using JavaScript, CSS, or APIs.
✅ Deploy Anywhere – Host on Shiny Server, RStudio Connect, Docker, or cloud platforms.
🔹 Key Applications
🔹 Data visualization dashboards
🔹 Machine learning model interfaces
🔹 Business intelligence tools
🔹 Healthcare & finance analytics
🔹 Automated reporting systems
🚀 Whether you're in data science, finance, healthcare, or research, Shiny helps bring your insights to life with interactive web applications!
Link: https://shiny.posit.co/
@Machine_learning_lab_k | 220 |
| 14 |  DeepSeek just dropped ANOTHER open-source AI model, Janus-Pro-7B.
It's multimodal (can generate images) and beats OpenAI's DALL-E 3 and Stable Diffusion across GenEval and DPG-Bench benchmarks.
Available: https://huggingface.co/deepseek-ai/Janus-Pro-7B
@Machine_learning_lab_k | 205 |
| 15 | Omni-RGPT: Unifying Image and Video Region-level Understanding
via Token Marks
** Omni-RGPT is a multimodal large language model designed for region-level understanding of both images and videos. It uses a novel approach called Token Mark to represent regions, which solves issues of scalability and temporal drift commonly seen in videos.
Link: https://arxiv.org/pdf/2501.08326
@Machine_learning_lab_k | 253 |
| 16 | Hugging Face released a free course on agents
AI Agents are autonomous systems that can understand user requests, break them down into steps, and execute actions to accomplish tasks. They combine language models with tools and external functions to interact with their environment. This module covers how to build effective agents using the smolagents library, which provides a lightweight framework for creating capable AI agents.
Link: https://github.com/huggingface/smol-course/tree/main/8_agents
@Machine_learning_lab_K | 227 |
| 17 | Vertex AI RAG Engine: A developers tool
Link: https://developers.googleblog.com/en/vertex-ai-rag-engine-a-developers-tool/
@Machine_learning_lab_K | 182 |
| 18 | https://arxiv.org/abs/2501.09223
@Machine_learning_lab_K | 195 |
| 19 | Phi-4 is a 14B parameter, state-of-the-art open model from Microsoft.
Link: https://ollama.com/library/phi4
@Machine_learning_lab_K | 213 |
| 20 | A simple way to explain 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗠𝗲𝗺𝗼𝗿𝘆.
https://x.com/i/status/1878813189766299763
@Machine_learning_lab_K | 1 |
