pedrodall

pedrodall's Stars

• cornellius-gp/linear_operator

  A LinearOperator implementation to wrap the numerical nuts and bolts of GPyTorch

  Language:Python9027

• davibarreira/Vizagrams.jl

  Integrating diagramming and data visualization

  Language:Jupyter Notebook431

• awesome-mlss/awesome-mlss

  🤖 Machine Learning Summer School deadlines

  Language:HTML2.6k296

• g-benton/Volt

  Public Implementation of Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes

  Language:Jupyter Notebook4611

• ShangtongZhang/DeepRL

  Modularized Implementation of Deep RL Algorithms in PyTorch

  Language:Python3.2k679

• ShangtongZhang/reinforcement-learning-an-introduction

  Python Implementation of Reinforcement Learning: An Introduction

  Language:Python13.5k4.8k

• norvig/pytudes

  Python programs, usually short, of considerable difficulty, to perfect particular skills.

  Language:Jupyter Notebook22.6k2.4k

• rougier/matplotlib-tutorial

  Matplotlib tutorial for beginner

  Language:Python2.9k807

• sebjai/robust-risk-aware-rl

  Some implementations from the paper robust risk aware reinforcement learning

  Language:Jupyter Notebook3313

• ablaom/MLJTutorial.jl

  Notebooks for introducing the machine learning toolbox MLJ (Machine Learning in Julia)

  Language:Jupyter Notebook253

• lucasmoschen/modelo-tcc-emap

  Este repositório contém os arquivos necessários para começar o Trabalho de Conclusão de Curso da Escola de Matemática Aplicada (FGV EMAp)

  Language:TeX295

• davibarreira/LunarVimConfig

  My configuration for Lunar

  Language:Lua456

• dynamicslab/langevin-regression

  Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton

  Language:Jupyter Notebook2410

• braydie/HowToBeAProgrammer

  A guide on how to be a Programmer - originally published by Robert L Read

  15.9k1.3k

• fccoelho/Curso_Blockchain

  Indtroductory course to cryptocurrencies and applications of Blockchain technologies.

  Language:HTML20256

• AxelThevenot/Python_Benchmark_Test_Optimization_Function_Single_Objective

  Language:Python7929

• SciML/SciMLBook

  Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)

  Language:HTML1.8k334

• JunoLab/Weave.jl

  Scientific reports/literate programming for Julia

  Language:Julia82795

• numbbo/coco

  Numerical Black-Box Optimization Benchmarking Framework

  Language:C26186

• MilesCranmer/symbolic_deep_learning

  Code for "Discovering Symbolic Models from Deep Learning with Inductive Biases"

  Language:Python722132

• ucuapps/computer-vision-course

  Computer Vision course for CS bachelors in UCU (2019)

  Language:Jupyter Notebook305

• davidbrochart/ipyx

  A Jupyter widget library for reactive variables

  Language:Python5

• betanalpha/stochastic_integrators

  The Fragility of Hamiltonian Monte Carlo to Stochastic Data

  Language:TeX8

• betanalpha/jupyter_case_studies

  Inference case studies in jupyter

  Language:Jupyter Notebook9120

• maxbiostat/BayesianStatisticsCourse

  PhD-level course at [EMAp](https://emap.fgv.br/en)

  Language:TeX216

• zenml-io/zenml

  ZenML 🙏: The bridge between ML and Ops. https://zenml.io.

  Language:Python4k431

• FirebaseExtended/remote-styles

  Conditionally load CSS from Firebase Remote Config

  Language:TypeScript643

• FirebaseExtended/reactfire

  Hooks, Context Providers, and Components that make it easy to interact with Firebase.

  Language:TypeScript3.5k401

• FirebaseExtended/experimental-extensions

  🧪 A laboratory for new extensions created by Firebase

  Language:TypeScript24441

• OpenIntroStat/ims

  📚 Introduction to Modern Statistics - A college-level open-source textbook with a modern approach highlighting multivariable relationships and simulation-based inference. For v1, see https://openintro-ims.netlify.app.

  Language:JavaScript864168