Machine Learning Roadmap 2026
#MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #DataAnalysis #LLM #python
https://news.1rj.ru/str/CodeProgrammer
#MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #DataAnalysis #LLM #python
https://news.1rj.ru/str/CodeProgrammer
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Built by devs who got tired of overpriced cloud providers. Also, there's a monkey 🐒
The deal: Fast NVMe storage, European servers, full control. No surprise bills, no corporate jargon, no waiting days for support.
The specs: ⚡ NVMe SSD that actually makes a difference 🌍 Netherlands • Poland • Germany 💰 Starting at €4.80/month (yeah, really) 🔧 Do whatever you want — it's your server 📊 Outperforms AWS, DigitalOcean & Vultr in benchmarks
Perfect for: Training neural networks without selling your kidney. Running Jupyter 24/7. Testing that crazy idea at 3 AM. Deploying models that actually need to scale. Scraping data without rate limits ruining your day.
🎁 -35% OFF FIRST MONTH Coupon: MONKEY35
https://fotbo.com/
Built by devs who got tired of overpriced cloud providers. Also, there's a monkey 🐒
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YOLO Training Template
Manual data labeling has become significantly more convenient. Now the process looks like in the usual labeling systems - you just outline the object with a frame and a bounding box is immediately created.
The platform allows:
• to upload your own dataset
• to label manually or auto-label via DINOv3
• to enrich the data if desired
• to train a #YOLO model on your own data
• to run inference immediately
• to export to ONNX or NCNN, which ensures compatibility with edge hardware and smartphones
All of this is available for free and can already be tested on #GitHub.
Repo:
https://github.com/computer-vision-with-marco/yolo-training-template
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Manual data labeling has become significantly more convenient. Now the process looks like in the usual labeling systems - you just outline the object with a frame and a bounding box is immediately created.
The platform allows:
• to upload your own dataset
• to label manually or auto-label via DINOv3
• to enrich the data if desired
• to train a #YOLO model on your own data
• to run inference immediately
• to export to ONNX or NCNN, which ensures compatibility with edge hardware and smartphones
All of this is available for free and can already be tested on #GitHub.
Repo:
https://github.com/computer-vision-with-marco/yolo-training-template
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Forwarded from Machine Learning with Python
🤖 Machine Learning Tutorials Repository
1. Python
2. Computer Vision: Techniques, algorithms
3. NLP
4. Matplotlib
5. NumPy
6. Pandas
7. MLOps
8. LLMs
9. PyTorch/TensorFlow
🔗 GitHub: https://github.com/patchy631/machine-learning/tree/main
⭐️ https://news.1rj.ru/str/DataScienceT
1. Python
2. Computer Vision: Techniques, algorithms
3. NLP
4. Matplotlib
5. NumPy
6. Pandas
7. MLOps
8. LLMs
9. PyTorch/TensorFlow
git clone https://github.com/patchy631/machine-learning🔗 GitHub: https://github.com/patchy631/machine-learning/tree/main
⭐️ https://news.1rj.ru/str/DataScienceT
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Collection of books on machine learning and artificial intelligence in PDF format
Repo: https://github.com/Ramakm/AI-ML-Book-References
#MACHINELEARNING #PYTHON #DATASCIENCE #DATAANALYSIS #DeepLearning
👉 @codeprogrammer
Repo: https://github.com/Ramakm/AI-ML-Book-References
#MACHINELEARNING #PYTHON #DATASCIENCE #DATAANALYSIS #DeepLearning
👉 @codeprogrammer
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