RecSys Series Part 5: Neural Matrix Factorization for Collaborative Filteri

Bringing Neural Architecture into Recommendations

1. Getting started — csvkit 1.0.5 documentation

5 Machine Learning Techniques for Sales Forecasting

Comparing Linear Regression, Random Forest Regression, XGBoost, LSTMs, and ARIMA Time Series Forecasting

Deep Dive into Polynomial Regression and Overfitting - DataScienceCentral.com

In this article, we show that the issue with polynomial regression is not over-fitting, but numerical precision. Even if done right, numerical precision still remains an insurmountable challenge. We focus here on step-wise polynomial regression, which is supposed to be more…

Optimization Techniques — Simulated Annealing

A popular method for optimizing model parameters

So why the heck are they called Support Vector Machines?

In this piece, I attempt to explain the mathematical reasoning behind this ‘complex’ name.

Layered Label Propagation Algorithm

An algorithm for community finding

Visualizing Three-Dimensional Data — Heatmaps, Contours, and 3D Plots with

Plotting heatmaps, contour plots, and 3D plots with Python

tf.data: Creating data input pipelines

Are you not able to load your NumPy data into memory? Does your model have to wait for data to be loaded after each epoch? Is your Keras…

A Complete Beginners Guide to Matrix Multiplication for Data Science with P

Learn matrix multiplication for machine learning by following along with Python examples

Partial Correlation Vs. Conditional Mutual Information

Finding relationships between different variables/ features in a dataset during a data analysis task is one of the key and fundemental…

Build PyTorch Models Easily Using torchlayers

torchlayers aims to do what Keras did for TensorFlow, providing a higher-level model-building API and some handy defaults and add-ons useful for crafting PyTorch neural networks.

Pandas tips I wish I knew before

How does pivot work? What is the main pandas building block? And more …

Co-variance: An intuitive explanation!

A comprehensive but simple guide which focus more on the idea behind the formula rather than the math itself — start building the block…

Matthews Correlation Coefficient: when to use it and when to avoid it

It’s not a silver bullet metric to classification problems

t-SNE clearly explained

An intuitive explanation of t-SNE algorithm and why it’s so useful in practice.

Visualizing Gaussian Elimination

The determinant is related to the volume of a parallelepiped spanned by the vectors in a matrix lets see how.

Bayesian Inference Algorithms: MCMC and VI

Intuition and diagnostics

Comprehensive Guide on Item Based Recommendation Systems

This guide will show in detail how item based recommendation system works and how to implement it in real work environment.

Matrix Factorization as a Recommender System

An Explanation and Implementation of Matrix Factorization

Machine Learning Benchmarking: You’re Doing It Wrong

I’m not going to bury the lede: Most machine learning benchmarks are bad. And not just kinda-sorta nit-picky bad, but catastrophically and fundamentally flawed. TL;DR: Please, for the love of sta…

Lesser-known pandas tricks (2019)

5 lesser-known pandas tricks that help you be more productive

How to Use DBSCAN Effectively

A complete guide on using the most cited clustering algorithm effectively

[P] PyCM 2.6 released : Multi-class confusion matrix library in Python

https://github.com/sepandhaghighi/pycm https://www.pycm.ir custom_rounder function added #279 complement function added sparse_matrix attribute added…

Learn how to read data into a Pandas DataFrame in 5 minutes

Extract data from different sources

Boosting Showdown: Scikit-Learn vs XGBoost vs LightGBM vs CatBoost in Senti

Which boosting algorithm will reign supreme in this head-to-head competition?

Decision Trees for Classification: ID3 Algorithm Explained

This article explains the ID3 Algorithm, in details with calculations, which is one of the many Algorithms used to build Decision Trees.

Optimization — Descent Algorithms

In this post, we will see several basic optimisation algorithms that you can use in various data science problems.

Less Known but Very Useful Pandas Functions

Expedite your data analysis process

Hyperparameter Tuning with Python: Complete Step-by-Step Guide

Why and How to use with examples of Keras/XGBoost

How to Share your Jupyter Notebook in 3 Lines of Code with Ngrok

Imagine having your Friends Working with your Local Jupyter Notebook in a Remote Machine

A Friendly Introduction to Text Clustering

All you need to know about k-means, brown clustering, tf-idf, topic models and LDA.

Local Links Run The World

Networks regulate everything from ant colonies and middle schools to epidemics and the internet. Here’s how they work.

Retail Analytics: A Novel and Intuitive way of finding Substitutes and Comp

Retail Analytics: Data Science for Retail

Test Your Skills: 26 Data Science Interview Questions & Answers

Can you answer them all?

Hyper-Parameter Optimization: A Review of Algorithms and Applications

Since deep neural networks were developed, they have made huge contributions to everyday lives. Machine learning provides more rational advice than humans are capable of in almost every aspect of...

How exactly does PCA work?

Simplest guide to PCA, ever.

A One-Stop Shop for Principal Component Analysis - Towards Data Science

At the beginning of the textbook I used for my graduate stat theory class, the authors (George Casella and Roger Berger) explained in the…

Semi-Supervised Classification of Unlabeled Data (PU Learning)

How to classify unlabeled data when all you have is just a sample of positive data

Implementing XGBoost from scratch

A step by step guide for implementing one of the most trending machine learning algorithm using numpy

The Most Useful ML Tools 2020

5 sets of tools every lazy full-stack data scientist should use

How to use Residual Plots for regression model validation?

Using residual plots to validate your regression models

An Introduction to Support Vector Regression (SVR)

Using Support Vector Machines (SVMs) for Regression

Beyond A/B Testing: Primer on Causal Inference

Making the most out of your experiments and observational data

Building an Incremental Recommender System: Part II

Going above and beyond state-of-the-art with confidence!

[P] pytorch-optimizer -- collections of ready to use optimization algorithm

249 votes, 21 comments. pytorch-optimizer -- collections of ready to use optimization algorithms for PyTorch, includes: AccSGD, AdaBound, AdaMod…

Self Supervised Depth Estimation: Breaking down the ideas

Learning depth without manual annotation

The Curious Case of Kalman Filters

QR Matrix Factorization

Least Squares and Computation (with R and C++)

Convex Hull: An Innovative Approach to Gift-Wrap your Data

How to Leverage Data Visualization with Wrapping Algorithm