Linear Algebra, AI, and Vector Databases

See the slides

Linear Algebra and AI

Following those slides, the recommended educational sequence is:

1. What is a Vector? – https://www.youtube.com/watch?v=klTvEwg3oJ4
2. Retrieval Augmented Generation (RAGs) – a use case for vectors – https://www.youtube.com/watch?v=u47GtXwePms
3. The core keywords from – https://neptune.ai/blog/understanding-vectors-from-a-machine-learning-perspective
   1. Input, Model, Output
   2. Scalar, Vector, Matrix
   3. Vectrorization – Sparce Input Vector
   4. Dense Input Vector
   5. Vector Space
   6. Introduction to Vector Operations
4. Continued introduction to Linear Algebra from: https://billjellesmacoding.netlify.app/blog/20240504_linear_algebra_machine_learning

 

Additional Projects:

• Dot Products in CUDA (GPU)
  • https://www.youtube.com/watch?v=gB1K5S_fWKA
• A longer format introduction to RAGs – https://www.youtube.com/watch?v=0i_7Cn31VrU
• Notice the use of dot products in Genarative AI – https://github.com/MoCoMakers/gan_numpy/blob/a090762f7c4123e26849b9871fe5e26f055775b4/gan.py#L59