Optimisation

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Berkan Sesen

May 4

Value Iteration vs Q-Learning: Dynamic Programming Meets RL

#reinforcementlearning #optimisation #dynamicprogramming

12 min read

Berkan Sesen

Apr 23

Solving CartPole Without Gradients: Simulated Annealing

#reinforcementlearning #optimisation

13 min read

Berkan Sesen

Apr 21

The Cross-Entropy Method: Solving RL Without Gradients

#reinforcementlearning #optimisation

12 min read

Berkan Sesen

Apr 15

AI Experts Are Dead. Long Live the AI Experts.

#llm #deeplearning #optimisation

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

Mar 30

From K-Means to GMM: Hard vs Soft Clustering

#unsupervisedlearning #optimisation

13 min read

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DEV Community

# optimisation

Value Iteration vs Q-Learning: Dynamic Programming Meets RL

Solving CartPole Without Gradients: Simulated Annealing

The Cross-Entropy Method: Solving RL Without Gradients

AI Experts Are Dead. Long Live the AI Experts.

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

From K-Means to GMM: Hard vs Soft Clustering