Berkan Sesen

Berkan Sesen

Apr 13

Gaussian Process Regression: The Bayesian Approach to Curve Fitting

#bayesian #supervisedlearning #probabilistic #inference

13 min read

Berkan Sesen

Apr 10

Hyperparameter Optimization: Grid vs Random vs Bayesian

#optimisation #supervisedlearning #bayesian

16 min read

Berkan Sesen

Apr 8

Policy Gradients: REINFORCE from Scratch with NumPy

#reinforcementlearning #deeplearning #optimisation

16 min read

Berkan Sesen

Apr 6

Deep Q-Networks: Experience Replay and Target Networks

#reinforcementlearning #deeplearning #optimisation

18 min read

Berkan Sesen

Apr 4

Q-Learning from Scratch: Navigating the Frozen Lake

#reinforcementlearning #optimisation

11 min read

Berkan Sesen

Apr 3

Genetic Algorithms: From Line Fitting to the Travelling Salesman

#optimisation #supervisedlearning

11 min read

Berkan Sesen

Apr 2

Backpropagation Demystified: Neural Nets from First Principles

#deeplearning #supervisedlearning #optimisation

13 min read

Berkan Sesen

Apr 1

MCMC Island Hopping: An Intuitive Guide to the Metropolis-Hastings Algorithm

#bayesian #sampling #probabilistic

9 min read

Berkan Sesen

Mar 31

Hidden Markov Models: When Clusters Have Memory

#unsupervisedlearning #timeseries #generative #probabilistic

13 min read

Berkan Sesen

Mar 30

From K-Means to GMM: Hard vs Soft Clustering

#unsupervisedlearning #optimisation

13 min read

Berkan Sesen

Mar 29

From MLE to Bayesian Inference: Why Your Estimate Needs a Prior

#bayesian #inference #statistics #probabilistic

15 min read

Berkan Sesen

Mar 28

Gaussian Mixture Models: The EM Algorithm in Practice

#unsupervisedlearning #generative #probabilistic #optimisation

12 min read

Berkan Sesen

Mar 27

The EM Algorithm: An Intuitive Guide with the Coin Toss Example

#unsupervisedlearning #inference #optimisation #probabilistic

10 min read

Berkan Sesen

Mar 26

Maximum Likelihood Estimation from Scratch: From Coin Flips to Gaussians

#statistics #inference #optimisation #probabilistic

13 min read

Berkan Sesen

Mar 25

Five Easy Pieces of Linear Regression

#supervisedlearning #statistics #optimisation

12 min read

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Gaussian Process Regression: The Bayesian Approach to Curve Fitting

Hyperparameter Optimization: Grid vs Random vs Bayesian

Policy Gradients: REINFORCE from Scratch with NumPy

Deep Q-Networks: Experience Replay and Target Networks

Q-Learning from Scratch: Navigating the Frozen Lake

Genetic Algorithms: From Line Fitting to the Travelling Salesman

Backpropagation Demystified: Neural Nets from First Principles

MCMC Island Hopping: An Intuitive Guide to the Metropolis-Hastings Algorithm

Hidden Markov Models: When Clusters Have Memory

From K-Means to GMM: Hard vs Soft Clustering

From MLE to Bayesian Inference: Why Your Estimate Needs a Prior

Gaussian Mixture Models: The EM Algorithm in Practice

The EM Algorithm: An Intuitive Guide with the Coin Toss Example

Maximum Likelihood Estimation from Scratch: From Coin Flips to Gaussians

Five Easy Pieces of Linear Regression