Hey! These are the handwritten notes, I've taken while studying.
a) CS231N Notes - Has the complete class notes of Stanford's CS231N course in Deep Learning
a) Data Structures and Algorithms
i) Part 1 - Has basics, Time and Space complexity, Arrays, Stacks, Queues, Linked Lists, Recursion
ii) Part 2 - Has trees, hashing, sorting, searching
b) Digital Logic
i) coming soon
c) Discrete Math
i) Part 1 - Has set, countability, relations, posets, hasse, graph theory, logic
ii) Part 2 - coming soon\
d) Signals and Systems
i) Part 1 - Has basics, properties, fourier series and transforms, convulutions, parseval's theorem, time frequency dualities
ii) Part 2 - coming soon\
e) Math
i) Probablity(Part 1) - Has axiomatic approach, basics, discrete and continuous random vars, Binomial and Poisson distribution
ii) Laplace Transforms - Has basics, laplace transforms of all functions, basic operations, inverse laplace transform, convolution theorem, applications in ODE
iii) Probablity(Part 2) - has bivariate stuff/uniform dist/geom dist/gamma dist/normal dist
iv) Statistics - has correlation and regression