Lab Recap 1
Course 9 : CP decomposition
Course on CP decomposition
Notebooks : BasicCP.ipynb, MultiplicationsTensors.ipynb
The seminal paper about CP decomposition and Neural Networks compression : Lebedev.pdf
An implementation of NN compression
Course 8 : Basics on tensors
Part I : introduction to tensors
Part II: operations on tensors and multiply tensors
Notebooks : Tensors1.ipynb, Tensors2.ipynb
Course 7
Small introduction to Transformers : Lecture7.pdf
Transformers : What can they do : Lecture7a.ipynb
Dive into Transformers architecture : Lecture7b.ipynb
Course 6
Lab : Lab6_empty.ipynb
Course 5 : other extensions
Lecture 5 :Lecture5.pdf
Notebooks : Lecture5a.ipynb, Lecture5b.ipynb, Lecture5c.ipynb
Course 4 : momentum
Lecture 4 : Lecture4.pdf
Notebooks : Lecture4a.ipynb, Lecture4b.ipynb, Lecture4c.ipynb
Variants of Momentum Algorithms
Lab 4 : Lab4.ipynb (use the version mentionned in the website)
Course 3 : Stochastic Optimisation
Lecture 3 : Lecture3.pdf
Notebooks : GD.ipynb, SGD.ipynb, MiniBatchSGD.ipynb
Course 2 : Optimisation
Lecture 2 : Lecture2.pdf
Companion notebooks : Lecture2a.ipynb, Lecture2b.ipynb, Lecture2c.ipynb
Course 1 : Introduction to matrices
Lecture 1 : Matrices.pdf, Eigenvalues.pdf
Notebooks : Vectors.ipynb, Matrices.ipynb, Lecture2a.ipynb, Lecture2b.ipynb
How could we find eigenvalues and eigenvectors
Lab 1a : Lab1a.pdf. Notebook of Lab 1a : Lab1a.ipynb
Lab 1b : Lab1b.pdf. Notebook of Lab 1b : Lab1b.ipynb
Lab 2 : Lab2.pdf