Diego A. Mesa
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(3)
linear algebra
(1)
mcmc
(1)
neural networks
(1)
numpy
(2)
optimization
(1)
probability
(1)
A Bottom-Up Introduction to Neural Networks
From 1-D Forward propagation to vectorized Backprop and everything in between
neural networks
linear algebra
numpy
In this notebook we will walk through the
forward
and
backward
computation directions (
fowardprop/backprop
) of neural networks.
Feb 3, 2023
An Introduction to Optimization
From Gradients, to Stochastic Gradient Descent with Momentum and ADAM and everything inbetween!
optimization
numpy
In this notebook we will walk through the several
gradient-based
optimization techniques
from scratch
.
Feb 2, 2023
An Introduction to Markov Chains, MCMC and The Gibbs Sampler
From Probability to Markov Chain Monte Carlo and the Gibbs Sampler!
probability
mcmc
In this notebook, we begin by reviewing some of the basics of
probability
and
Bayes rule
, then make out way into
markov chains
, and
monte-carlo sampling
, combining them to…
Feb 1, 2023
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