Lagrange multipliers with visualizations and code | by Rohit Pandey | Towar
In this story, we’re going to take an aerial tour of optimization with Lagrange multipliers. When do we need them? Whenever we have an…
machine-learning
Beyond One-Hot. 17 Ways of Transforming Categorical Features Into Numeric F
All the encodings that are worth knowing — from OrdinalEncoder to CatBoostEncoder — explained and coded from scratch in Python
Computer Vision | Papers With Code
**Zero-shot learning (ZSL)** is a model's ability to detect classes never seen during training. The condition is that the classes are not known during supervised learning. Earlier work in zero-shot learning use attributes in a two-step approach to infer unknown classes. In the…
Computer Vision | Papers With Code
**Few-Shot Learning** is an example of meta-learning, where a learner is trained on several related tasks, during the meta-training phase, so that it can generalize well to unseen (but related) tasks with just few examples, during the meta-testing phase. An effective approach to…
The Sensitivity Analysis: A Powerful Yet Underused Tool for Data Scientists
Quantifying the effects of varying different inputs, applied on a gemstone dataset with over 50K round-cut diamonds
How to Deploy your Custom ML Model with Streamlit and Heroku
A Step-by-Step Guide to Host your Models!
Project Lighthouse — Part 1: P-sensitive k-anonymity
Part one of a series on how we will measure discrepancies in Airbnb guest acceptance rates using anonymized perceived demographic data.
Sensitivity, Specificity and Meaningful Classifiers
The sometimes confusing concepts involved in interpreting coronavirus testing
SVM Kernels: What Do They Actually Do?
An intuitive visual explanation
Log-Normal Distribution
A Log-Normal Distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed.
Stop One-Hot Encoding Your Categorical Variables.
There are many better alternatives
Matching of Bipartite Graphs using NetworkX
A simple introduction to matching in bipartite graphs with Python code examples
AI system for high precision recognition of hand gestures
Scientists have developed an Artificial Intelligence (AI) system that recognises hand gestures by combining skin-like electronics with computer vision.
Clustering Using Convex Hulls
How to use convex hulls in data clustering
Getting Started with Random Matrices: A Step-by-Step Guide
In the Deep Learning (DL) age, more and more people have encountered and used (knowingly or not) random matrices. Most of the time this…
10 Graph Algorithms Visually Explained
A quick introduction to 10 basic graph algorithms with examples and visualisations
www.cheatsheets.aqeel-anwar.com
Cheat Sheets for Machine Learning and Data Science
Peregrine: A Pattern-Aware Graph Mining System
Peregrine: A Pattern-Aware Graph Mining System.
5 Categorical Encoding Tricks you need to know today as a data scientist
A Complete Pythonic Encoding Tutorial
Speech Recognition with Python. Learn which of the 9 most prominent… | by S
Learn which of the 9 most prominent automatic speech recognition engines is best for your needs, and how to use it in Python programs.
4 Rarely-Used Yet Very Useful Pandas Tricks
Explained with examples
A radical new technique lets AI learn with practically no data
“Less than one”-shot learning can teach a model to identify more objects than the number of examples it is trained on.
A Comparison of Bandit Algorithms
Multi-Armed Bandits: Part 6
How to Choose a Feature Selection Method For Machine Learning - MachineLearningMastery.com
Feature selection is the process of reducing the number of input variables when developing a predictive model. It is desirable to reduce the number of input variables to both reduce the computational cost of modeling and, in some cases, to improve the performance of the model…
MLWhiz: Helping You Learn Data Science!
In this post, I am going to be talking about some of the most important graph algorithms you should know and how to implement them using Python.
A review of consensus protocols
Algorithms for Collision Detection
An online book about collision detection using Processing.
Cataloging Tools for Data Teams
An introduction to Data Cataloging and major tools that data teams can use for data discovery
Complete Guide to Adam Optimization
The Adam optimization algorithm from definition to implementation
63 Machine Learning Algorithms — Introduction | by Priyanshu Jain | The Sta
In this article I covered 63 Algorithms of Machine Learning in easy to understand manner for business professionals.
All the ~Eigen-stuff they never thought you should know
To Infinity and…Linear Algebra?!
Improving complementary-product recommendations
New modeling approach increases accuracy of recommendations by an average of 7%.
Make kNN 300 times faster than Scikit-learn’s in 20 lines!
Using Facebook faiss library for REALLY fast kNN
5 SMOTE Techniques for Oversampling your Imbalance Data
Know your SMOTE ways to oversampled your data
What is Perspective Warping ? | OpenCV and Python
A step-by-step guide to apply perspective transformation on images
Helping robots avoid collisions
The startup Realtime Robotics, co-founded by former MIT postdoc George Konidaris, is helping robots solve the motion planning problem by giving them collision avoidance capabilities.
Leveraging Value from Postal Codes, NAICS Codes, Area Codes and Other Funky
A value is worthless unless it tells you something.
Floating-Point Formats and Deep Learning
Floating-point formats are not the most glamorous or (frankly) the important consideration when working with deep learning models: if your model isn’t working well, then your floating-point format certainly isn’t going to save you! However, past a certain point of model…
Z-score for anomaly detection
Small-bites data science
Writing a Production-Level Machine Learning Framework: Lessons Learned
Some of our insights from developing a PyTorch framework for training and running deep learning models …
https://blog.exxactcorp.com/autograd-the-best-machine-learning-library-youre-not-using/
Causal Inference Book | Miguel Hernan | Harvard T.H. Chan School of Public
Jamie Robins and I have written a book that provides a cohesive presentation of concepts of, and methods for, causal inference. Much of this material is currently scattered across journals in sever…
Vectorizing code matters
I come from the world of MATLAB and numerical computing, where for loops are shorn and vectors are king. During my PhD at UVM, Professor…
A Gentle Introduction to Information Entropy - MachineLearningMastery.com
Information theory is a subfield of mathematics concerned with transmitting data across a noisy channel. A cornerstone of information theory is the idea of quantifying how much information there is in a message. More generally, this can be used to quantify the information in an…
How to Calculate the KL Divergence for Machine Learning - MachineLearningMastery.com
It is often desirable to quantify the difference between probability distributions for a given random variable. This occurs frequently in machine learning, when we may be interested in calculating the difference between an actual and observed probability distribution. This can…
Kullback–Leibler divergence
In mathematical statistics, the Kullback–Leibler (KL) divergence (also called relative entropy and I-divergence[1]), denoted D KL ( P ∥ Q ) {\displaystyle D_{\text{KL}}(P\parallel Q)} , is a type of statistical distance: a measure of how one reference probability…
Latent Dirichlet Allocation: Intuition, math, implementation and visualisat
A tour of one of the most popular topic modelling techniques and a guide to implementing and visualising it using pyLDAvis
XGBoost, LightGBM, and Other Kaggle Competition Favorites
An Intuitive Explanation and Exploration
10 Hyperparameter optimization frameworks.
Tune your Machine Learning models with open-source optimization libraries
Geospatial Indexing with Quadkeys
Squaring the Earth