VISSL · A library for state-of-the-art self-supervised learning from images

A library for state-of-the-art self-supervised learning from images

Graph Theory Basics

What you need to know as graph theory adoption continues to take off

Conda: essential concepts and tricks

for beginners as well as advanced users

XGBoost: Extreme Gradient Boosting — How to Improve on Regular Gradient Boosting?

A detailed look at differences between the two algorithms and when you should choose one over the other

4 Easy Steps for Implementing CatBoost

an end-to-end tutorial on how to apply an emerging Data Science algorithm

Two outlier detection techniques you should know in 2021

Elliptic Envelope and IQR-based detection

stitchfix/mab: Library for multi-armed bandit selection strategies, including efficient deterministic implementations of Thompson sampling…

Library for multi-armed bandit selection strategies, including efficient deterministic implementations of Thompson sampling and epsilon-greedy. - stitchfix/mab

Gaussian Process Regression From First Principles

Gaussian Process Regression is a remarkably powerful class of machine learning algorithms. Here, we introduce them from first principles.

Introduction to hierarchical clustering (Part 3 — Spatial clustering)

Introducing a spatial dimension into hierarchical clustering

A Comprehensive Mathematical Approach to Understand AdaBoost

Learn how AdaBoost works from a Math perspective, in a comprehensive and straight-to-the-point manner.

How you can quickly build ML web apps with Streamlit.

The quickest way to embed your models into web apps.

Thompson Sampling using Conjugate Priors

Multi-Armed Bandits: Part 5b

stanfordmlgroup/ngboost: Natural Gradient Boosting for Probabilistic Prediction

Natural Gradient Boosting for Probabilistic Prediction - stanfordmlgroup/ngboost

How to use PyCaret — the library for lazy data scientists

Train, visualize, evaluate, interpret, and deploy models with minimal code.

The Algorithms That Make Instacart Roll

Instacart crunches petabytes daily to predict what will be on grocery shelves and even how long it will take to find parking

State-of-the-Art Image Generation Models

I have aggregated some of the SotA image generative models released recently, with short summaries, visualizations and comments. The overall development is summarized, and the future trends are spe…

Gradient-Free-Optimizers A collection of modern optimization methods in Pyt

Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces. - SimonBlanke/Gradient-Free-Optimizers

PyCaret — pycaret 2.2.0 documentation

Home - PyCaret

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A Complete Guide To Survival Analysis In Python, part 3 - KDnuggets

Concluding this three-part series covering a step-by-step review of statistical survival analysis, we look at a detailed example implementing the Kaplan-Meier fitter based on different groups, a Log-Rank test, and Cox Regression, all with examples and shared code.

www-eio.upc.edu/~pau/cms/rdata/datasets.html

An overview of synthetic data types and generation methods

Synthetic data can be used to test new products and services, validate models, or test performances because it mimics the statistical property of production data. Today you'll find different types of structured and unstructured synthetic data.

Affinity Analysis (Market Basket Analysis)

Have you ever wondered how often do you buy certain items together? Why do you buy some items together? How likely do you purchase an item…

Categorical cross-entropy and SoftMax regression

Ever wondered how to implement a simple baseline model for multi-class problems ? Here is one example (code included).

Explain Machine Learning Models: Partial Dependence

From black box to no more box

Error Backpropagation Learning Algorithm

The error backpropagation learning algorithm is a supervised learning technique for neural networks that calculates the gradient of descent for weighting different variables.

Why you should always use feature embeddings with structured datasets

A simple technique for boosting accuracy on ANY model you use

Data Science & AI Glossary | DeepAI

The data science and artificial intelligence terms you need while reading the latest research

Generative Graph Models with NetworkX

A comprehensive guide on standard generative graph approaches with implementation in NetworkX

Hacker News

Computer Science, Machine Learning, Programming, Art, Mathematics, Philosophy, and Short Fiction

Hacker News

Data labeling is often the biggest bottleneck in machine learning. Active learning lets you train machine learning models with much less labeled data. The best AI-driven companies, like Tesla, use active learning.

The Ultimate Scikit-Learn Machine Learning Cheatsheet - KDnuggets

With the power and popularity of the scikit-learn for machine learning in Python, this library is a foundation to any practitioner's toolset. Preview its core methods with this review of predictive modelling, clustering, dimensionality reduction, feature importance, and data…

Image Processing with Python — Blob Detection using Scikit-Image

How to identify and segregate specific blobs in your image

General Methods | Papers With Code

Browse 1109 deep learning methods for General.

Decades-Old Graph Problem Yields to Amateur Mathematician

By making the first progress on the “chromatic number of the plane” problem in over 60 years, an anti-aging pundit has achieved mathematical immortality.

SVM Classifier and RBF Kernel — How to Make Better Models in Python

A complete explanation of the inner workings of Support Vector Machines (SVM) and Radial Basis Function (RBF) kernel

Using strip charts to visualize dozens of time series at once

Strip charts are extremely useful to make heads or tails from dozens (and up to several hundred) of time series over very long periods of…

Link Prediction and Information Theory: A Tutorial

Using Mutual Information to measure the likelihood of candidate links in a graph.

Forget coding, you can now solve your AI problems with Excel

Microsoft Excel is a powerful tool for learning the basics of data science and machine learning.

Algorithms for Decision Making | Hacker News

Model Compression: A Look into Reducing Model Size

Why is Model Compression important? A significant problem in the arms race to produce more accurate models is complexity, which leads to…

New Features of Scikit-Learn. An Overview of the Most Important… | by Ankit

An Overview of the Most Important Features in Version 0.24

Comparing Binary, Gray, and One-Hot Encoding

This article shows a comparison of the implementations that result from using binary, Gray, and one-hot encodings to implement state machines in an FPGA. These encodings are often evaluated and applied by the synthesis and implementation tools, so it’s important to know why the…

Meet whale! ? The stupidly simple data discovery tool. | by Robert Yi | Dat

And why your data science team needs it.

How to Automate Tasks on GitHub With Machine Learning for Fun and Profit |

A tutorial on how to build a GitHub App that predicts and applies issue labels using Tensorflow and public datasets.

10 Stochastic Gradient Descent Optimisation Algorithms Cheat Sheet | by R

Stochastic gradient descent optimisation algorithms you should know for deep learning

Benchmark functions | BenchmarkFcns

This website is for sale! benchmarkfcns.xyz is your first and best source for all of the information you’re looking for. From general topics to more of what you would expect to find here, benchmarkfcns.xyz has it all. We hope you find what you are searching for!

8 New Tools I Learned as a Data Scientist in 2020 | by Ben Weber | Dec, 202

Making the move from Docker to Live Deployments

Practical Graph Theory in Ruby

This is the next installment in the "Practical Computer Science" series, where you will learn how to apply classic computer science concepts to solve real problems using Ruby. Today we are going to talk about Graph

BFGS in a Nutshell: An Introduction to Quasi-Newton Methods

Demystifying the inner workings of BFGS optimization