ImpactAI

Machine Learning course built on a combination of resources (Stanford's cs221n, Iain Goodfellow's Deep Learning Book, Princeton COS495, Gilbert Strang's MIT18.06, NASA FDL, Fast.AI, NVIDIA resources & others)

How do I get it?

Get a copy of the ImpactAI course by either downloading the zip file or:
git clone https://github.com/h21k/ImpactAI.git

Course Structure

(Note this is currently incomplete - updates follow)

I. Fundamentals - Linear Algebra based on (MIT 18.06) by Gilbert Strang

Gilbert Strang's lectures of which this section is based on can be seen here: www..com
This segment gives you the fundamentals required for the ML sections. This will involve linear algebra, matrix multiplications, vector spaces etc...

A1 = Linear Algebra<br>
A2 = Linear Algebra<br>
A3 = Factorization into A = LU<br>
--
B1 = Linear Algebra<br>
B2 = Linear Algebra<br>
B3 = Factorization into A = LU<br>
--
C1 = Linear Algebra<br>
C2 = Linear Algebra<br>
C3 = Factorization into A = LU<br>

Lecture notes available here: 

Excercises available here: 

II. Convolutional Neural Networks fundamentals - Stanford cs221n by Fei-Fei Li & Justin Johnson

B1 = Optimisation functions

IV.

V.

VI.

VII.

VIII.

IX.

X.

XI.

B1 = Optimisation functions

Workshops, Teaching & Talks

Feel free to contact me in case you want me to give a talk etc.

References

TBA