Part 7: Fast Pattern Searching with STUMPY
Finding Similar Subsequences for Known Patterns
machine-learning
Anomaly Detection using Benford’s Law
Locating fraudulent transactions with simple theory.
Advanced Ensemble Learning Techniques
Ensemble is an art and science
Machine learning for anomaly detection: Elliptic Envelope
Bite-sized data science
How to peek inside a black box model — Understand Partial Dependence Plots
In this post, we will be learning a tool to reveal the working mechanism of a black box model. But before we start, let talk about…
The Hundred-Page Machine Learning Book by Andriy Burkov
This is companion wiki of The Hundred-Page Machine Learning Book by Andriy Burkov. The book that aims at teaching machine learning in a concise yet systematic manner.
Why Does No One Use Advanced Hyperparameter Tuning? | by Liam Li | Oct, 202
Takeaways from our experience building state-of-the-art hyperparameter tuning in Determined AI’s integrated deep learning training…
Tutorial: Uncertainty estimation with CatBoost
Understanding why your model is uncertain and how to estimate the level of uncertainty
Python 3.9 New Features & How to Use Them
Python 3.9 New Feature Guide
Histogram Matching
How to generate a histogram for an image, how to equalize the histogram, and finally how to modify your image histogram to be similar to…
Hidden Markov Model (HMM) — simple explanation in high level
HMM is very powerful statistical modeling tool used in speech recognition, handwriting recognition and etc. I wanted to use them, but when…
An intuitive guide to PCA
Ideas behind Principal Component Analysis
Silhouette Method — Better than Elbow Method to find Optimal Clusters
Deep dive analysis of Silhouette Method to find optimal clusters in k-Means clustering
Handling Outliers in Clusters using Silhouette Analysis
Identify and remove outliers in each clusters from K-Means clustering
Machine Learning Enabled High-Sigma Verification Of Memory Designs
Variation-aware memory verification with brute force Monte Carlo accuracy in much less time.
Seven Must-Know Statistical Distributions and Their Simulations for Data Sc
Assumptions, relationships, simulations, and so on
DBSCAN — a density-based unsupervised algorithm for fraud detection
Bite-sized data science on fraud detection
AI 101: Intro to Evolutionary Algorithms
Polynomial Regression: The Only Introduction You’ll Need
A deep-dive into the theory and application behind this Machine Learning algorithm in Python, by a student
A Novel Approach to Feature Importance — Shapley Additive Explanations
The state-of-the-art in feature importance
New features in scikit-learn
Overview of the latest developments in version 0.23
Entropy, Cross-Entropy, and KL-Divergence Explained!
Let us try to understand the most widely used loss function — Cross-Entropy.
Part 5: Fast Approximate Matrix Profiles with STUMPY
Roughly Accurate Matrix Profiles Computed in a Fraction of the Time
The Jewel of the Matrix: A Deep Dive Into Eigenvalues & Eigenvectors
An intuitive look at the abstract concept
The Singular Value Decomposition without Algebra
Understand the Ultimate Linear Algebra concept with Geometry
Solving a Chicken and Egg Problem: Expectation-Maximization (EM)
The Intuition Behind the Popular Expectation-Maximization Algorithm with Example Code
Deep dive into ROC-AUC
deep dive into ROC-AUC
How Sklearn’s “TF-IDF” is different from the standard “TF-IDF”?
Let ‘s go see the differences and analyze step by step the approach which is taken to compute the Sklearn’s TF-IDF
Variational Gaussian Process — What To Do When Things Are Not Gaussian
Learn to use non-Gaussian distributions in Gaussian Process models, and variational inference with Gaussian quadrature to compute…
A Visual Explanation of Gradient Descent Methods (Momentum, AdaGrad, RMSPro
Why can AdaGrad escape saddle point? Why is Adam usually better? In a race down different terrains, which will win?
Dimensionality Reduction in Hyperspectral Images using Python
Dimensionality Reduction Techniques for Hyperspectral Images.
Multiclass Classification with Support Vector Machines (SVM), Kernel Trick
Understanding the mathematic behind SVM + Implementation in Python via scikit-learn
Ten Eisen features that changed the way I do deep learning
How a simple `pip install eisen` will save days of work and solve (almost) all of your problems.
5 Fabulous Python Packages For Data-Science Nobody Knows About
Do you know about these packages?
Simple Guide to Choropleth Maps
Choropleth Maps using Plotly to track COVID 19 cases.
Visualizing Geospatial Data in Python
Open source tools and techniques for visualizing data on custom maps
External Redirection | LinkedIn
Comment Ranking Algorithms: Hacker News vs. YouTube vs. Reddit
A Machine Learning Algorithm Every Data Scientist Needs: Bagged Trees
Eigenfaces — Face Classification in Python
Not enough data for Deep Learning? Try Eigenfaces.
Federated Learning using PyTorch and PySyft | LearnOpenCV
A gentle introduction to federated learning using PyTorch and PySyft with the help of a real life example.
A Product Manager’s Guide to Machine Learning: Cloud Machine Learning
Why obtaining the Amazon Web Services Machine Learning — Specialty (“AWS ML”) certification is one of the best starting points to gaining…
Short technical information about Word2Vec, GloVe and Fasttext
Introduction
Contrasting contrastive loss functions
A comprehensive guide to four contrastive loss functions for contrastive learning
An Introduction to Optical Character Recognition for Beginners
Your first step towards reading text from unstructured data
Graph Theory | BFS Shortest Path Problem on a Grid
Hi all, welcome back to another post of my brand new series on Graph Theory named Graph Theory: Go Hero. I undoubtedly recommend the…
Using K-Means to detect changes in a retail store | Towards Data Science
Unsupervised techniques to identify changes in the behavior
Factor Analysis — A Complete Tutorial
Covering Eigenvalues, Factor Creation and Cronbach’s Alpha
Five Cool Python Libraries for Data Science - KDnuggets
Check out these 5 cool Python libraries that the author has come across during an NLP project, and which have made their life easier.
Insurance Risk Pricing — Tweedie Approach - Towards Data Science
An illustrative guide to estimate the pure premium using Tweedie models in GLMs and Machine Learning