Attacks against machine learning — an overview
This blog post surveys the attacks techniques that target AI (Artificial Intelligence) systems and how to protect against them.
machine-learning
Model evaluation, model selection, and algorithm selection in machine learn
A single-PDF version of Model Evaluation parts 1-4 is available on arXiv: https://arxiv.org/abs/1811.12808
Feature Engineering with Tidyverse
Stay up-to-date on the latest data science and AI news in the worlds of artificial intelligence, machine learning, deep learning, implementation, and more.
Text Classifier Algorithms in Machine Learning – Stats and Bots
Introduction to Market Basket Analysis in Python
Using mlxtend to perform market basket analysis on online retail data set.
Set Theory Ordered Pairs and Cartesian Product with R
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Top speed for top-k queries
Stay up-to-date on the latest data science and AI news in the worlds of artificial intelligence, machine learning, deep learning, implementation, and more.
Monte Carlo theory, methods and examples (2013)
Modern Machine Learning Algorithms: Strengths and Weaknesses
Get to know the ML landscape through this practical, concise overview of modern machine learning algorithms. Plus, we'll discuss the tradeoffs of each.
Berkeley CS189 Machine Learning: Complete Lecture Notes [pdf]
40 Techniques Used by Data Scientists - Data Science Central
These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 40 links below, you will find a selection of…
https://blog.statsbot.co/introduction-to-imitation-learning-32334c3b1e7a
scikit-surprise 1.0.5 : Python Package Index
An easy-to-use library for recommender systems.
Eecs227at
ML beyond Curve Fitting: An Intro to Causal Inference and do-Calculus
Since writing this post back in 2018, I have extended this to a 4-part series on causal inference: * ➡️️ Part 1: Intro to causal inference and do-calculus [https://www.inference.vc/untitled] * Part 2: Illustrating Interventions with a Toy Example…
Machine Learning Explained: Vectorization and matrix operations
Gaussian Processes for Machine Learning: Contents
Sequence Modeling with CTC
A visual guide to Connectionist Temporal Classification, an algorithm used to train deep neural networks in speech recognition, handwriting recognition and other sequence problems.
A Deep Dive into Monte Carlo Tree Search
Why Momentum Really Works
We often think of optimization with momentum as a ball rolling down a hill. This isn't wrong, but there is much more to the story.
Kernel Cookbook
A reference manual for creating covariance functions.
Introducing Similarity Search at Flickr | code.flickr.com
Eigenvectors and Eigenvalues explained visually
LightTag is a text annotation platform for data scientists creating AI trai
LightTag, a newly launched startup from a former NLP researcher at Citi, has built a "text annotation platform" designed to assist data scientists who
A guide to receptive field arithmetic for Convolutional Neural Networks
The receptive field is perhaps one of the most important concepts in Convolutional Neural Networks (CNNs) that deserves more attention from…
Command Line Tricks For Data Scientists
For many data scientists, data manipulation begins and ends with Pandas or the Tidyverse. In theory, there is nothing wrong with this…
How we grew from 0 to 4 million women on our fashion app, with a vertical machine learning approach
My name is Gabi (my bio), and I’m the CEO and co-founder of Chicisimo. We launched three years ago, our goal was to offer automated outfit…
Understanding Feature Engineering (Part 3) — Traditional Methods for Text D
Traditional strategies for taming unstructured, textual data
A Gentle Introduction to Concept Drift in Machine Learning - Machine Learni
Data can change over time. This can result in poor and degrading predictive performance in predictive models that assume a static relationship between input and output variables. This problem of the changing underlying relationships in the data is called concept drift in the…
Start With Gradient Boosting, Results from Comparing 13 Algorithms on 165 D
Which machine learning algorithm should you use? It is a central question in applied machine learning. In a recent paper by Randal Olson and others, they attempt to answer it and give you a guide for algorithms and parameters to try on your problem first, before spot checking a…
Probabilistic Filters By Example: Cuckoo Filter and Bloom Filters
CatBoost vs. Light GBM vs. XGBoost - KDnuggets
Who is going to win this war of predictions and on what cost? Let’s explore.
Multiscale Methods and Machine Learning - KDnuggets
We highlight recent developments in machine learning and Deep Learning related to multiscale methods, which analyze data at a variety of scales to capture a wider range of relevant features. We give a general overview of multiscale methods, examine recent successes, and compare…
Logistic Regression: A Concise Technical Overview
Interested in learning the concepts behind Logistic Regression (LogR)? Looking for a concise introduction to LogR? This article is for you. Includes a Python implementation and links to an R script as well.
Hierarchical Classification – a useful approach when predicting thousands o
Traditionally, most of the multi-class classification problems (i.e. problems where you want to predict where a given sample falls into, from a set of possible results) focus on a small number of possible predictions.
Time Series for Dummies – The 3 Step Process
Time series forecasting is an easy to use, low-cost solution that can provide powerful insights. This post will walk through introduction to three fundamental steps of building a quality model.
How we grew from 0 to 4 million women on our fashion app, with a vertical m
Three years ago we launched Chicisimo, our goal was to offer automated outfit advice. Today, with over 4 million women on the app, we want to share how our data and machine learning approach helped us grow. It’s been chaotic but it is now under control.
Linear Algebra Cheat Sheet for Machine Learning
All of the Linear Algebra Operations that You Need to Use in NumPy for Machine Learning. The Python numerical computation library called NumPy provides many linear algebra functions that may be useful as a machine learning practitioner. In this tutorial, you will discover the…
The Periodic Table of Data Science
This periodic table can serve as a guide to navigate the key players in the data science space. The resources in the table were chosen by looking at surveys taken from data science users, such as the 2016 Data Science Salary Survey by O'Reilly, the 201...
Recommendation System Algorithms: An Overview
This post presents an overview of the main existing recommendation system algorithms, in order for data scientists to choose the best one according a business’s limitations and requirements.
Using Self-Organizing Maps to solve the Traveling Salesman Problem
Using Self-Organizing Maps to solve the Traveling Salesman Problem The Traveling Salesman Problem is a well known challenge in Computer Science: it consists on finding the shortest route possible that traverses all cities in a given map only once. Although its simple…
One-page R: a survival guide to data science with R - DataScienceCentral.com
This article comes from Togaware. A Survival Guide to Data Science with R These draft chapters weave together a collection of tools for the data scientist—tools that are all part of the R Statistical Software Suite. Each chapter is a collection of one (or more) pages that cover…
Numenta Anomaly Benchmark: A Benchmark for Streaming Anomaly Detection
Numenta created the open source Numenta Anomaly Benchmark (NAB) to test and their own anomaly detection algorithms. Learn more about how Numenta and Domino worked together to develop the NAB.
Topic Modeling with LDA Introduction
Stay up-to-date on the latest data science and AI news in the worlds of artificial intelligence, machine learning, deep learning, implementation, and more.
Assessing Data with Item Response Theory
Stay up-to-date on the latest data science and AI news in the worlds of artificial intelligence, machine learning, deep learning, implementation, and more.
How to Handle Imbalanced Classes in Machine Learning
Imbalanced classes put "accuracy" out of business. This is a surprisingly common problem in machine learning, and this guide shows you how to handle it.
arbox/data-science-with-ruby: Practical Data Science with Ruby based tools.
Practical Data Science with Ruby based tools.
https://news.21.co/quantifying-decentralization-e39db233c28e
Understanding Machine Learning Algorithms
Machine learning algorithms aren’t difficult to grasp if you understand the basic concepts. Here, a SAS data scientist describes the foundations for some of today’s popular algorithms.
Top-100-Data-science-interview-questions
Data science, also known as data-driven decision, is an interdisciplinery field about scientific methods, process and systems to extract knowledge from data in various forms, and take descision based on this knowledge. A data scientist should not only be evaluated only on…