mtinman13/rtx_project

Exploration of RayTracing using pre-existing graphical library

rtx_project

Exploration of RayTracing using pre-existing graphical library mlx

Key Takeaways and Understandings:

• Quaternion rotation
• Optics
• Distance computation in Euclidean space

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Key Takeaways for Machine Learning 🤖🧠🦾💻

Feature Engineering and Feature Selection:

• Distance calculations for creating new features.
• Normalization and scaling of data.

🤖 Machine Learning Algorithms:

• K-means Clustering: Uses Euclidean distances for grouping.
• K-nearest Neighbors (KNN): Relies on distance measurements.
• Support Vector Machines (SVM): Uses geometry and distances for decision boundaries.

💻 Dimensionality and Dimensionality Reduction:

• Principal Component Analysis (PCA): Utilizes geometric relationships.
• t-SNE and UMAP: Uses distances for data visualization in lower dimensions.

🦾 Real-world Applications:

• Computer Vision: Object detection, 3D reconstruction.
• Natural Language Processing (NLP): Similarity measurements in embedding spaces.

💻 Optimization and Performance:

• K-means++ initialization: Improved cluster initialization for faster convergence.

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🧠 Practical Steps:

• Implement Algorithms: Practice implementing distance-based algorithms like KNN and K-means.
• Data Visualization: Use graphical tools to visualize data in different dimensions.
• Competitions: Participate in Kaggle competitions to apply these concepts on real datasets.