awesome-computer-vision

A curated list of awesome computer vision resources

awesome-deep-vision

A curated list of deep learning resources for computer vision

awesome-federated-learning

Federated Learning Library: https://fedml.ai

awesome-github-profile-readme

😎 A curated list of awesome Github Profile READMEs 📝

awesome-interview-questions

:octocat: A curated awesome list of lists of interview questions. Feel free to contribute! :mortar_board:

awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

awesome-solidity

⟠ A curated list of awesome Solidity resources, libraries, tools and more

cheat-sheets

Data Science Cheatsheets

codestackr

coding-interview-university

A complete computer science study plan to become a software engineer.

computer-vision-projects

Lets make our hand dirty with CV.

covid-19-and-pneumonia-detection-from-x-ray

data-science

A Beginners Guide to Data Science. A Respository to get you job ready as a Data Science fresher

data-science-end-to-end

A Respository to get you job ready as a Data Scientist

data-structure-and-algorithms

Covering all the interviews Problem solving the leet code problems.

data-visualization-and-analysis

deep-learning-from-scratch

deeplearninganimepapers

A list of papers and other resources on deep learning with anime style images.

ensembling-blending-and-stacking

Ive shown each and everyway how to blend and stack with bunch of algortihms then its all up to you. how you use them.

faceshifter

Unofficial PyTorch Implementation for FaceShifter (https://arxiv.org/abs/1912.13457)

feature-engineering

All Techniques of Feature Engineering.

hacktoberfest2020

Make your Hacktoberfest 2020 contribution here! Win stickers and a T-shirt on completing 4 pull requests. (Specially for beginners)! :D

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

house-price-prediction

Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home. Practice Skills Creative feature engineering Advanced regression techniques like random forest and gradient boosting

hyperparameter-tuning-with-the-hparams-dashboard

When building machine learning models, you need to choose various hyperparameters, such as the dropout rate in a layer or the learning rate. These decisions impact model metrics, such as accuracy. Therefore, an important step in the machine learning workflow is to identify the best hyperparameters for your problem, which often involves experimentation. This process is known as "Hyperparameter Optimization" or "Hyperparameter Tuning". The HParams dashboard in TensorBoard provides several tools to help with this process of identifying the best experiment or most promising sets of hyperparameters.

interview-prepartion-data-science

julia-for-machine-learning

Julia provides powerful tools for deep learning (Flux.jl and Knet.jl), machine learning and AI. Julia’s mathematical syntax makes it an ideal way to express algorithms just as they are written in papers, build trainable models with automatic differentiation, GPU acceleration and support for terabytes of data with JuliaDB. Julia's rich machine learning and statistics ecosystem includes capabilities for generalized linear models, decision trees, and clustering. You can also find packages for Bayesian Networks and Markov Chain Monte Carlo.

keras-tuner

Using Keras Tuner for knowing Best Hyper Parameters

machine-learning-from-scratch

malenoma--classification