Implementation notebooks and scripts of Deep Reinforcement learning Algorithms in PyTorch and TensorFlow.
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Updated
Jan 16, 2020 - Jupyter Notebook
Implementation notebooks and scripts of Deep Reinforcement learning Algorithms in PyTorch and TensorFlow.
Reinforcement Learning Notebooks
Quantitative Finance, Financial Machine Learning and visualizations Notebooks
a collection of numerical experiments documented in jupyter notebooks.
Codes, notebooks and data for the computation of the self-energy and vertex renormalization spinfoam amplitudes. The algorithm is based on Monte Carlo simulations.
Some notebooks for learning about bayesian models
Selected topics in Computational Physics.
a collection of python notebooks using RL agents to play Atari games in OpenAI gym environments
Financial Mathmatics Concepts with theory & visualizations
A collection of Statistics and ML notebooks useful for Graduate Students
Jupyter notebooks implementing Reinforcement Learning algorithms in Numpy and Tensorflow
Some interesting applications of Stochastic Processes using Jupyter Notebooks for descriptive and instructive illustrations.
This notebook shows how to use variance constrained semi grand canonical (VC-SGC) Molecular Dynamics/Monte Carlo (MD/MC) calculations in pyiron
An approximation of π calculated via Monte Carlo method and proposed in Jupyter Notebook. A solution for Computer Simulation (40634-1 Sharif UT, Spring 2023) homework, the 1st series.
Notebooks for my youtube Reinforcement Learning leactures.
Github repo for the submission of the codes and notebooks for the LSN course at UNIMI
Notebook for implementing Monte Carlo techniques (Metropolis-Hastings and Augmented Gibbs) to solve a Bayesian Probit regression.
Codes and notebooks for the application of Markov Chain Monte Carlo in spinfoams. Computation of boundary observables, correlation functions and entanglement entropy.
Code and notebooks for the computation of the 16-cell spinfoam amplitude, including boundary observables and quantum correlations. The computation is based on MCMC .
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