Deep Learning, NLP, NMT, AI, ML
Neovim integration for Google Keep, built using gkeepapi Requirements Neovim 0.5 Python 3.6+ A patched font (optional. Used for icons) Table of Contents Installation gkeep supports all the usual plugin managers Packer require(‘packer’).startup(function() use {‘stevearc/gkeep.nvim’, run = ‘:UpdateRemotePlugins’} end) Paq require “paq” { {‘stevearc/gkeep.nvim’, run = vim.fn[‘remote#host#UpdateRemotePlugins’]}; } vim-plug
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Original creator: Games Computer Play config.py allows dynamic variable loadingIt is more versitle than .env files example: log=True # disables override logs if False (default True) showMap=True # overrides the showmap=False option in the main file Powershell:get-content log.txt -wait -tail 30 Bash:tail log.txt -f Copyright Copyright (C) 2021 gamescomputersplay and nopeless All code is licensed under GPL-3.0-or-later. That is, the GNU General Public License, either version 3, or (at your option) any later version. The
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gcp-doctor is a command-line diagnostics tool for GCP customers. It finds and helps to fix common issues in Google Cloud Platform projects. It is used to test projects against a wide range of best-practices and frequent mistakes, based on the troubleshooting experience of the Google Cloud Support team. gcp-doctor is open-source and contributions are welcome! Note that this is not an officially supported Google product, but a community effort. The Google Cloud Support team maintains this code and we do […]
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Print a directory tree structure in your Python code. Download You can simply: pip install directory-structure Or you can also: Clone the repository to your local machine. Enter the directory. Download necessary modules/libraries. git clone https://github.com/gabrielstork/directory-structure.git cd directory-structure pip install -r requirements.txt
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🐞A debug toolbar for FastAPI based on the original django-debug-toolbar. 🐞Swagger UI & GraphQL are supported. Documentation: https://fastapi-debug-toolbar.domake.io Installation pip install fastapi-debug-toolbar Quickstart Add DebugToolbarMiddleware middleware to your FastAPI application: from debug_toolbar.middleware import DebugToolbarMiddleware from fastapi import
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A collection of easy Python small projects to help you improve your programming skills. Table Of Contents
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Copyright (C) 2016-2018 AutoML Group Attention: This package is a reimplementation of the original SMAC tool (see reference below). However, the reimplementation slightly differs from the original SMAC. For comparisons against the original SMAC, we refer to a stable release of SMAC (v2) in Java which can be found here. The documentation can be found here. Status for master branch: Status for the development branch SMAC is a tool for algorithm configuration to optimize the parameters of arbitrary algorithms across […]
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Genetic feature selection module for scikit-learn Genetic algorithms mimic the process of natural selection to search for optimal values of a function. Installation The easiest way to install sklearn-genetic is using pip pip install sklearn-genetic or conda conda install -c conda-forge sklearn-genetic Requirements Python >= 2.7 scikit-learn >= 0.20.3 DEAP >= 1.0.2 Example from __future__ import print_function import numpy as np from sklearn import datasets, linear_model from genetic_selection import GeneticSelectionCV def main(): iris = datasets.load_iris() # Some
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“If an army of monkeys were strumming on typewriters they might write all the books in the British Museum.” monkeys is a framework designed to make genetic programming in Python accessible, quick, flexible, and fun. Get started here. What is genetic programming? Genetic programming algorithms are a class of evolutionary algorithms in which solutions to a problem are represented as executable tree structures – programs. In order to use genetic programming in order to solve a problem, two things must […]
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A supervised domain-agnostic framework that allows for probabilistic modelling, namely the prediction of probability distributions for individual data points. The package offers a variety of features and specifically allows for the implementation of probabilistic prediction strategies in the supervised contexts comparison of frequentist and Bayesian prediction methods strategy optimization through hyperparamter tuning and ensemble methods (e.g. bagging) workflow automation List of developers and contributors Documentation The full documentation is available here. Installation Installation is easy using Python’s package manager $ […]
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