Essential Math for Data Science: Visual Introduction to Singular Value Deco
This article will cover singular value decomposition (SVD), which is a major topic of linear algebra, data science, and machine learning.
machine-learning
Say Hello To Recommendation Systems
A preview into one of the most prominent data science applications
DAGs and Control Variables
How to select control variables for causal inference using Directed Acyclic Graphs
A Guide To Using The Difference-In-Differences Regression Model
We’ll show how to use the DID model to estimate the effect of hurricanes on house prices
Sobol Indices to Measure Feature Importance
Understanding the model’s output plays a major role in business-driven projects, and Sobol can help
Reproducible ML: Maybe you shouldn’t be using Sklearn’s train_test_split
Reproducibility is critical for robust data science — after all, it is a science.
Machines are haunted by the curse of dimensionality
The curse of dimensionality comes into play when we deal with a lot of data having many dimensions or features.
Survival Analysis in R (in under 10-minutes)
Making a survival analysis can be a challenge even for experienced R users, but the good news is I’ll help you make beautiful, publication-quality survival plots in under 10-minutes. Here’s what WE are going to do: Make your first survival model an...
How to Evaluate Survival Analysis Models
Introduction to the most popular performance evaluation metrics for survival analysis along with practical Python examples
Flip Flop: Why Zillow’s Algorithmic Home Buying Venture Imploded
XGBoost Alternative Base Learners
Introducing dart, gblinear, and XGBoost Random Forests
Similarity-Based Image Search for Visual Art
Evaluating similarity of visual art from both human perceptual & quantitative judgments
Useful Python decorators for Data Scientists
I show toy implementations of Python decorator patterns that may be useful for Data Scientists.
One Line of Code to Accelerate Your Sklearn Algorithms on Big Data
The introduction of the intel sklearn extension. Make your Random Forest even faster than XGBoost.
CatBoost vs. LightGBM vs. XGBoost
Which is the best algorithm?
The Big Six Matrix Factorizations – Nick Higham
Six matrix factorizations dominate in numerical linear algebra and matrix analysis: for most purposes one of them is sufficient for the task at hand. We summarize them here. For each factorization …
93 Datasets That Load With A Single Line of Code
How you can pull one of a few dozen example political, sporting, education, and other frames on-the-fly.
Survival Analysis: A Brief Introduction
An initial look into the method best suited for examining time-to-event data
Focal Loss : A better alternative for Cross-Entropy
Focal loss is said to perform better than Cross-Entropy loss in many cases. But why Cross-Entropy loss fails, and how Focal loss addresses…
Data Mining: Market Basket Analysis with Apriori Algorithm
Uncovering the secret behind why breads are always conveniently placed beside butter in groceries
How does Shazam work? Music Recognition Algorithms, Fingerprinting, and Processing | Toptal®
The Shazam music recognition application made it finally possible to put a name to that song on the radio. But how does this magical miracle actually work? In this article, Toptal Freelance Software Engineer Jovan Jovanovic sheds light on the principles of audio signal…
Louvain’s Algorithm for Community Detection in Python
Apply Louvain’s Algorithm in Python for Community Detection
Improving Shopping Recommendations for Customers Through eBay’s Relevance C
Under the new machine learning model, buyers are recommended items that are more aligned to their shopping interests on eBay.
Introduction to SHAP Values and their Application in Machine Learning
Learn how the SHAP library works under the hood
Multi-Armed Bandit Algorithms: Thompson Sampling
Intuition, Bayes, and an example
The Top 10 Algorithms Every Programmer Should Know In Graph Data Structure!
Why to learn these graph algorithms? Graph algorithms are a set of instructions that...
Introduction — Machine Learning from Scratch
Evaluating the potential return of a model with Lift, Gain, and Decile Anal
Use these three tools to understand the usefulness of your machine learning models
CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All i...
Natural Language Processing with Transformers Book
Welcome · Advanced R.
Welcome | Data Science at the Command Line, 2e
This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get…
AI Virtual Assistant Technology Guide 2022
They can help you get an appointment or order a pizza, find the best ticket deals and bring your...
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5–10x Faster Hyperparameter Tuning with HalvingGridSearch
How to optimize the hyperparameters of a machine learning model and how to speed up the process
Experiment Tracking with MLflow in 10 Minutes
Managing Machine Learning Lifecycle made easy — explained with Python examples
A Guide To ML Experiment Tracking — With Weights & Biases
Easily learn to track all of your ML experiments with metrics and logs with an example project walkthrough!
Read this before using ROC-AUC as a metric
No metric is perfect.
Text Summarization with NLP: TextRank vs Seq2Seq vs BART
Natural Language Processing with Python, Gensim, Tensorflow, Transformers
SHAP: Explain Any Machine Learning Model in Python
A Comprehensive Guide to SHAP and Shapley Values
Real-world website visitor forecast with Facebook Prophet: A Complete Tutor
As a data analyst at Microsoft, I must investigate and understand time-series data every day. Besides looking at some key performance…
Asset2Vec: Turning 3D Objects into Vectors and Back
How we used NeRF to embed our entire 3D object catalogue to a shared latent space, and what it means for the future of graphics
Interpretable Machine Learning using SHAP — theory and applications
Introduction
Introducing TorchRec, a library for modern production recommendation system
We are excited to announce TorchRec, a PyTorch domain library for Recommendation Systems. This new library provides common sparsity and parallelism primitives, enabling researchers to build state-of-the-art personalization models and deploy them in production.
What is Relational Machine Learning?
A dive into fundamentals of learning representations beyond feature vectors
Machine Learning Algorithms Cheat Sheet — Accel.AI
Machine learning is a subfield of artificial intelligence (AI) and computer science that focuses on using data and algorithms to mimic the way people learn, progressively improving its accuracy. This way, Machine Learning is one of the most interesting methods in Computer…
Topic Modeling in Python | Toptal
Topic modeling can bring NLP to the next level. Here’s how.
What Color Is This? | Stitch Fix Technology – Multithreaded
We need to know what colors our merch is. But because downstream users include many different people and algorithms, we need to describe colors as a hierarch...
Machine Learning Gets a Quantum Speedup | Quanta Magazine
Two teams have shown how quantum approaches can solve problems faster than classical computers, bringing physics and computer science closer together.
Data Scientists, The 5 Graph Algorithms that you should know
Because Graph Analytics is the future