Projects
- ml-botting-core — Making ML more accessible to botting apps. Solving Complex UI Challenges w/ ML.
- ml-bricks — Common utils for machine learning, computer vision
- ml_buff — no summary
- ml-caboodle — All the stuff that doesn't have another home
- ml-callbacks — Simple ml callbacks to track model performance and state
- ml-cavalry — Custom tools for Machine Learning and Optimization
- ML-Classification-model-selector-Basavaraj100 — It select best classfication model
- ml-clerk — Module to record/document your model changes
- ml-cli — A group of cli commands that helps with machine learning and docker commands
- ml-cli-azureml-pipeline — ML-Cli run in an azureML Pipeline
- ml-cloud-tools — ML Tools for the Cloud
- ml-collections — ML Collections is a library of Python collections designed for ML usecases.
- ml-commons — no summary
- ml-commons-pekalam — Tensorflow utilities and custom implementations used in my projects.
- ml-comp — An engine for running component based ML pipelines
- ml-compiler-opt — Tooling for ML in LLVM
- ml-confs — A utility to handle configurations for machine learning pipelines
- ml-contextual-ads-common — PyPi package created by Schibsted's Product & Application Security team.
- ml-core — Core Package for MissingLink.ai
- ml-crafter — Performs end to end ML model development
- ml-crypto — pyCrypto wrapper, used by various MissingLink.ai libraries
- ml-csdlo6021 — ml_csdl06021
- ml-dash — A Beautiful Visualization Dashboard For Machine Learning
- ml-data-api — susallwave data api python sdk
- ml-data-ci — A platform for tracking data-centric AI pipelines in dynamic streaming data
- ml-data-creation — ml_data_creation
- ml-data-gen — ml_data_gen Package for generating testing data
- ml-dataset — datasets for easy machine learning use
- ml-dataset-tools — Tools for data set handling. Primarily for deep learning and computer vision tasks.
- ml-datasets — Machine Learning dataset loaders
- ml-deploy — Package to deploy ml model
- ml-dev-tools — Useful functions for Machine Learning
- ml-diamond — DIAMOND: A Flexible Distributed Machine Learning Library for Novel Learning Algorithms and Models
- ml-digits-recognition — A simple digits recognition neural network
- ml-dl-models — Module to access machine learning and deep learning module
- ml-dojo — A small example package
- ml-dp-utils — Pacote utilidades Deep Learning
- ml-dronebase-data-utils — A collection of commonly functions used by DroneBase ML Engineers
- ml-dtypes — no summary
- ML-Education-Tools — ML Education Tools for Teaching
- ml-eeg — A ml & eeg helper library
- ml-eis — data processing and machine learning model for EIS
- ml-engine — Machine learning engine
- ml-env — no summary
- ml-ephys — ephys tools for MountainLab
- ml-etl — machine learning data pipeline
- ml-evaluation — PyPi package created by Schibsted's Product & Application Security team.
- ml-evaluation-framework — no summary
- ml-example — Example of ml project
- ml-experiment-manager — Manage data during machine learning projects
- ml-experiments — Monitor you model training anywhere.
- ml-express — A Python library for day to day data analysis and machine learning.
- ml-fashion-cnn — Code for fashion MNIST using CNN
- ml-fast-train — Package to train basic models
- ml-feature-store — no summary
- ml-flow-client — test
- ML-Formatter — An easy to use package for parsing media and transforming it for your Machine Learning projects.
- ml-framework — Machine Learning Framework
- ml-functions — no summary
- ml-gan — Code for GAN on Fashion MNIST dataset
- ml-gates — Code for AND, NOT, OR, NOR, NAND, XOR
- ml-git — ML-Git: version control for ML artefacts
- ml-goodput-measurement — Package to retrieve Goodput of jobs running on Cloud TPU.
- ml-hadoop-experiment — TensorFlow and Pytorch helpers to run experiments on Hadoop
- ml-helper — Helpers to speed up and structure machine learning projects
- ml-helpers — Functions to help build machine learning tools
- ml-holmes — A machine learning benchmark on tabular data
- ml-hyperparameters — A basic library to help find the best hyperparameters in sklearn
- ml-ibge-cities — A Django select cities web-based ibge.
- ml-idm — A tool that provides a direct interface to a model you want to interact with. Get predictions, build graphs, analyse models with external tools.
- ML-IIITL — A helping package for ML written by Sankalp
- ml-impute — A package for synthetic data generation for imputation using single and multiple imputation methods.
- ml-in-prod-juliarty-ml-project-1 — That is a homework project
- ml-indie-tools — A collection of tools for low-resource indie machine learning development
- ml-infer — Inference toolkit for machine learning models
- ml-infrastructure — Software infrastructure to for machine learning
- ml-init — Install the main ML libraries
- ml-insights — Package to calibrate and understand ML Models
- ml-investment — Machine learning tools for investment
- ml-iris-backprop — Code for backpropogation on Iris Dataset
- ml-jobs — no summary
- ml-legit — Package for Data Management of MissingLink.ai
- ml-leoxiang66 — A package of RL algorithms
- ml-lib — Machine learning library built on top of TensorFlow.
- ml-lime-xai — Code for XAI using Lime (Breast Cancer Dataset)
- ml-list — List of all Libraries
- ml-liv — no summary
- ml-logger — no summary
- ml-logsdon — A small example package
- ml-logwriter — A ML logger package
- ml-lstm — Code for LSTM on IMDb Dataset
- ml-manager — A manager which manages all your ML experiments
- ML-master — Ceci est un test de création d'un package python
- ML-medic-kit — The Machine Learning Medic Kit is designed to enhance the capabilities of health data scientists tackling binary classification problems
- ml-meta — no summary
- ml-metadata — A library for maintaining metadata for artifacts.
- ML-Methods — Use for Machine Learning
- ml-mixins — A colleciton of useful mixins for machine learning development code.
- ml-mnist-cnn — Code for handwritten MNIST using CNN
- ml-model — Utility for making hyperparameter tuning easier