This section will be dedicated to guides to learning Data Science, Machine Learning, Deep Learning, and the likes. The material comes from links to various sources, one of the primary ones being Virgilio, for those who are not familiar with it.
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- Existing guides
- New guides
- Contribute to Virgili0
- 👉 Feynman Learning Technique 👈
- 👉 How to Learn #AI 👈
- [👉 Python to Neural Networks : A Guide for Beginners 👈] (https://www.linkedin.com/feed/update/urn:li:activity:6690911303441219584/)
- Math Fundamentals
- Statistics Fundamentals
- Python Fundamentals
- Jupyter Notebook
- The Data Science Process
- What do I need for ML?
- Do you really need ML?
- ML use cases
- Introduction to ML
- Machine Learning Theory
- Introduction to Artificial Neural Networks
- ML Study paths
- Machine Learning Study Path [deadlink]
- Computer Vision
- Basic Python [deadlink] | Alternative: Python Basics
- Matrix Algebra [deadlink] | Alternative: Matrix Algebra
- Python for Data Science [deadlink]
- Deep Learning Theory
- Deep Learning in Cloud
- Object Tracking based on Deep Learning
- Object Instance Segmentation using TensorFlow Framework and Cloud GPU Technology
- See guide map
- See Purgatorio
Learn how you can also contribute to Virgili0 and add your own guides and professional experience to the existing resources.
Contributions are very welcome, please share back with the wider community (and get credited for it)!
Please have a look at the CONTRIBUTING guidelines, also have a read about our licensing policy.
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