machine-learning
How UMAP Works — umap 0.3 documentation
What is Hierarchical Clustering?
The article contains a brief introduction to various concepts related to Hierarchical clustering algorithm.
An In-Depth Guide to Contrastive Learning: Techniques Models and Applications
Discover the fundamentals of contrastive learning, including key techniques like SimCLR, MoCo, and CLIP. Learn how contrastive learning improves unsupervised learning and its practical applications.
How exactly Stitch Fix’s “Tinder for clothes” learns your style
Each customer has an individualized style map, laying out her feelings about peasant blouses, A-line dresses, or pencil skirts.
Understanding AdaBoost – or how to turn Weakness into Strength
Many of you might have heard of the concept “Wisdom of the Crowd”: when many people independently guess some quantity, e.g. the number of marbles in a jar glass, the average of their guesses is often pretty accurate – even though many of the guesses are totally off. The same…
Knowledge extraction from unstructured texts | Tech Blog
There is an unreasonable amount of information that can be extracted from what people publicly say on the internet. Learn how to do it.
benedekrozemberczki/awesome-gradient-boosting-papers: A curated list of gradient boosting research papers with implementations.
A curated list of gradient boosting research papers with implementations. - GitHub - benedekrozemberczki/awesome-gradient-boosting-papers: A curated list of gradient boosting research papers with ...
Modeling the Unseen
How Instacart uses Machine Learning to spot lost demand in its fulfillment chain
Arima Model – Complete Guide to Time Series Forecasting in Python
Using ARIMA model, you can forecast a time series using the series past values. In this post, we build an optimal ARIMA model from scratch and extend it to Seasonal ARIMA (SARIMA) and SARIMAX models. You will also see how to build autoarima models in python
Synthetic data generation — a must-have skill for new data scientists
A brief rundown of packages and ideas to generate synthetic data for self-driven data science projects and deep diving into machine…
Kernel density estimation explainer
Matthew Conlen provides a short explainer of how kernel density estimation works. Nifty.
Comparison of the Text Distance Metrics | ActiveWizards: data science and engineering lab
This article discusses and compares different approaches of how to compare two text strings.
https://dhruvonmath.com/2019/04/04/kernels
How to Use t-SNE Effectively
Although extremely useful for visualizing high-dimensional data, t-SNE plots can sometimes be mysterious or misleading.
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Principal component analysis: pictures, code and proofs
The code used to generate the plots for this post can be found here.
Python Data Science Handbook | Python Data Science Handbook
The Hitchhiker’s Guide to Feature Extraction
Some Tricks and Code for Kaggle and Everyday work. This post is about useful feature engineering methods and tricks that I have learned and end up using often.
What is (Gaussian) curvature?
Algorithms by Jeff Erickson
Distill — Latest articles about machine learning
Articles about Machine Learning
Jacobian matrix and determinant - Wikipedia
In vector calculus, the Jacobian matrix (/dʒəˈkoʊbiən/,[1][2][3] /dʒɪ-, jɪ-/) of a vector-valued function of several variables is the matrix of all its first-order partial derivatives. When this matrix is square, that is, when the function takes the same…
One-Shot Learning: Learning More with Less Data
The 5 Sampling Algorithms every Data Scientist need to know
This post is about some of the most common sampling techniques one can use while working with data.
Five Command Line Tools for Data Science
You can do more data science than you think from the terminal.
TF-IDF: The best content optimization tool SEOs aren’t using - Search Engin
Term frequency–inverse document frequency uncovers the specific words that top-ranking pages use to give target keywords context.
yzhao062/pyod: A Python Toolbox for Scalable Outlier Detection (Anomaly Det
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques - yzhao062/pyod
A Gentle Introduction to Noise Contrastive Estimation
Find out how to use randomness to learn your data by using Noise Contrastive Estimation with this guide that works through the particulars of its implementation.
Introduction to Genetic Algorithms
The Hitchhiker’s Guide to Feature Extraction
168 votes, 13 comments. 2.2M subscribers in the datascience community. A space for data science professionals to engage in discussions and debates on…
Designing Data-Intensive Applications (DDIA) — an O’Reilly book by Martin Kleppmann (The Wild Boar Book)
The mathematics and Intuitions of Principal Component Analysis (PCA) Using
As data scientists or Machine learning experts, we are faced with tonnes of columns of data to extract insight from, among these features…
Applied Category Theory | Mathematics | MIT OpenCourseWare
Category theory is a relatively new branch of mathematics that has transformed much of pure math research. The technical advance is that category theory provides a framework in which to organize formal systems and by which to translate between them, allowing one to transfer…
Policy Gradient Algorithms
A Visual Exploration of Gaussian Processes
How to turn a collection of small building blocks into a versatile tool for solving regression problems.
The Illustrated Word2vec
Discussions: Hacker News (347 points, 37 comments), Reddit r/MachineLearning (151 points, 19 comments) Translations: Chinese (Simplified), French, Korean, Portuguese, Russian “There is in all things a pattern that is part of our universe. It has symmetry, elegance, and grace…
ROC Curve Explained in One Picture - DataScienceCentral.com
With a ROC curve, you’re trying to find a good model that optimizes the trade off between the False Positive Rate (FPR) and True Positive Rate (TPR). What counts here is how much area is under the curve (Area under the Curve = AuC). The ideal curve in the left image fills in…
How to Use ROC Curves and Precision-Recall Curves for Classification in Python - MachineLearningMastery.com
It can be more flexible to predict probabilities of an observation belonging to each class in a classification problem rather than predicting classes directly. This flexibility comes from the way that probabilities may be interpreted using different thresholds that allow the…
The why and how of nonnegative matrix factorization
A Visual Guide to Evolution Strategies | 大トロ
http://precisionagricultu.re/how-machine-learning-is-gradually-changing-mod
The Swiss Army Knife of Hashmaps
A while back, there was a discussion comparing the performance of using the hashbrown crate (based on Google’s SwissTable implementation1) in the Rust compiler. In the last RustFest, Amanieu was experimenting on integrating his crate into stdlib, which turned out to have some…
Four Techniques for Outlier Detection
There are many techniques to detect and optionally remove outliers from a dataset. In this blog post, we show an implementation in KNIME Analytics Platform of four of the most frequently used - traditional and novel - techniques for outlier detection.
How to visualize decision tree
Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when…
A Machine Learning Approach to Shipping Box Design. (arXiv:1809.10210v1 [st
Having the right assortment of shipping boxes in the fulfillment warehouse to pack and ship customer's online orders is an indispensable and integral part of nowadays eCommerce business, as it...
Cookbook — Bayesian Modelling with PyMC3
Recently I’ve started using PyMC3 for Bayesian modelling, and it’s an amazing piece of software! The API only exposes as much of heavy machinery of MCMC as you need — by which I mean, just the pm.sample() method (a.k.a., as Thomas Wiecki puts it, the Magic Inference Button™)…
A Feature Selection Tool for Machine Learning in Python
Using the FeatureSelector for efficient machine learning workflows
Vertical Spotlight: Machine Learning for Manufacturing
Receiver Operating Characteristic Curves Demystified (in Python)
In this blog, I will reveal, step by step, how to plot an ROC curve using Python. After that, I will explain the characteristics of a basic ROC curve.