javihaus Goto Github PK

advanced-time-series-analysis

Advance Time Series Analysis using Probabilistic Programming, Auto Regressive Neural Networks and XGBoost Regression.

auto-ml-app-with-dash

Auto ML application app with dash. Classification and Regression algorithms.

bandits-algorithms-in-real-world

We all have seen lots of articles where Reinforcement Learning (LR) agents have been used to cross frozen lakes, climb mountains, choosing the best routes for a cab in a city, etc. Games present a significant challenge to real-life agents since they require us to make several decisions. Normally, we must optimize these decisions in order to improve our overall score (or not to fall down a hole in the frozen lake, otherwise we can get troubles). Games also show how the math behind agents works in a beautiful way. However, RL isn't about playing games; it's about developing agents who can make decisions on their own.

category_theory_machine_learning

List of papers studying machine learning through the lens of category theory

cern-quantum-computing-course

Quantum computing is one the most promising new trends in information processing. In this course, we will introduce from scratch the basic concepts of the quantum circuit model (qubits, gates and measures) and use them to study some of the most important quantum algorithms and protocols, including those that can be implemented with a few qubits (BB84, quantum teleportation, superdense coding...) as well as those that require multi-qubit systems (Deutsch-Jozsa, Grover, Shor..). We will also cover some of the most recent applications of quantum computing in the fields of optimization and simulation (with special emphasis on the use of quantum annealing, the quantum approximate optimization algorithm and the variational quantum eigensolver) and quantum machine learning (for instance, through the use of quantum support vector machines and quantum variational classifiers). We will also give examples of how these techniques can be used in chemistry simulations and high energy physics problems. The focus of the course will be on the practical aspects of quantum computing and on the implementation of algorithms in quantum simulators and actual quantum computers (as the ones available on the IBM Quantum Experience and D-Wave Leap). No previous knowledge of quantum physics is required and, from the mathematical point of view, only a good command of basic linear algebra is assumed. Some familiarity with the python programming language would be helpful, but is not required either.

chaos-in-time-series

Finding Chaos iand getting insights in Time Series.

conditionalneuralprocess

Conditional Neural Process

covid19-spain

Bayesian analysis of COVID19 evolution in Spain

dash-app-gimnasio

Deploy a Dash app in Heroku with GitHub

digital-twin-in-python

In this repo we will show how to build a simple but useful Digital Twin using python. Our asset will be a Li-ion battery. This Digital Twin will allow us to model and predict batteries behavior and can be included in any virtual asset management process.

edward

A library for probabilistic modeling, inference, and criticism. Deep generative models, variational inference. Runs on TensorFlow.

explainable-deep-neural-networks

Explainable Deep Neural Networks

gosynthetic

:triangular_ruler: Jekyll theme for building a personal site, blog, project documentation, or portfolio.

kaggle-covid-19-drug-discovery

mlops

MLOps examples

motivated-topology

A topology textbook with a hubristic title

quantum-machinel-leaning

Classification Algorithm using Circuit-Centric Quantum Classifiers with IBM qiskit library.

quantumcomputingseries

The purpose of this project is to explain quantum information theory, using both theory and some mild application, to a beginner who has little to no experience with the field. Very little pre-requisites are required and I am very confident that any ambitious highschooler can complete this text. The initial form of this series will be in a set of Jupyter notebooks that will eventually be turned into an e-book/video series.

rlfinalproject

tabular-quantum-gan

A Tabular Quantum GAN (TQGAN) for synthetic data generation

tgan

Generative adversarial training for generating synthetic tabular data.