Welcome to the repository where you can find various resources and structured learning paths for artificial intelligence, deep learning, data science, and machine learning.
- Introduction to TensorFlow for Deep Learning with Udacity
- In-depth tutorials and exercises to gain practical skills
- Comprehensive material for interview preparation
- Guides and quizzes to test your knowledge
- Implementations and explanations of fundamental data structures and algorithms
- Challenge problems to improve your coding skills
- Tailored content for aspiring data scientists
- Interview questions from basic to advanced levels
- Specialization in managing and analyzing data on AWS
- Hands-on labs and projects
- Deep dive into deep learning concepts
- Projects and case studies to solidify understanding
- Courses by DeepLearning.AI
- Latest advancements and practical assignments
- Exploring transformer models with Hugging Face
- Tutorials for implementing state-of-the-art models
- Official Google AI resources
- Comprehensive Q&A section for learners
- Interactive Jupyter notebooks
- Collaborations using Google Colab
- Integrating language models with blockchain technology
- New paradigms in decentralized AI
- Projects based in Python
This repository is curated by Vibudh Singh, a passionate advocate for open education and a believer in sharing knowledge freely. With a background in [Your Background], [Your Name or Alias] has compiled these resources to help others on their journey to mastering AI and data science.
For any queries or discussions, feel free to reach out on LinkedIn or via email.
Contributions are welcome! If you'd like to add more resources or improve existing ones, please send a pull request or open an issue.
This project is licensed under the [MIT liscense] - see the LICENSE file for details.
- Thanks to all the content creators and educators whose resources are listed here.
- Special mention to the AI and data science community for their continual support and inspiration.
- Open Powershell
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cd D:\tabbyml\temp.tabby\models\TabbyML (not needed)
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docker run -it --gpus all -p 8080:8080 -v D:\tabbyml\temp\.tabby:/data tabbyml/tabby serve --model TabbyML/StarCoder-7B --device cuda
For 8099 port:
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docker run -it --gpus all -p 8099:8099 -v D:\tabbyml\temp\.tabby:/data tabbyml/tabby serve --model TabbyML/StarCoder-7B --device cuda
(Optional - for other local devices) Ngrok link:
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ngrok http --domain=beetle-whole-luckily.ngrok-free.app 8080
References yt TUTORIAL Tabby Website