Top 38 Python Libraries for Data Science, Data Visualization & Machine Lear
This article compiles the 38 top Python libraries for data science, data visualization & machine learning, as best determined by KDnuggets staff.
machine-learning
A Practical Introduction to 9 Regression Algorithms
Hands-on tutorial to effectively use different Regression Algorithms
Carl-McBride-Ellis/Compendium-of-free-ML-reading-resources
Compendium of free ML reading resources.
[2106.10860v1] Multiplying Matrices Without Multiplying
Multiplying matrices is among the most fundamental and compute-intensive operations in machine learning. Consequently, there has been significant work on efficiently approximating matrix...
Gaussian Belief Propagation
Detecting knee- / elbow points in a graph
Using “Kneedle” algorithmus detecting knees with Python package “kneed”
Complete guide to understanding Node2Vec algorithm
An in-depth guide to understanding node2vec algorithm and its hyper-parameters
The history of Amazon’s forecasting algorithm
The story of a decade-plus long journey toward a unified forecasting model.
An Introduction to Statistical Learning
Tokenization Algorithms Explained
A one-stop-shop for all your tokenization needs
Hora | Hora Search Everywhere
5 Ultimate Python Libraries for Image Processing
OpenCV is not the only one
An introduction to A* pathfinding (tutorial)
This is part 3 of a series on bot programming originally published on the Coder One blog. Part 1:...
How to Add Uncertainty Estimation to your Models with Conformal Prediction
Why conformal prediction for uncertainty estimation can improve your predictions
8 Dimensionality Reduction Techniques every Data Scientists should know
Essential guide to various dimensionality reduction techniques in Python
scikit-learn-intelex · PyPI
Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application.
Papers with Code - Paper with Code Newsletter
Papers With Code highlights trending Machine Learning research and the code to implement it.
Apriori Algorithm for Association Rule Learning — How To Find Clear Links B
Explanation and examples of frequent itemset mining and association rule learning over relational databases in Python
Types of Correlation Coefficients
Different Kinds of Correlation Coefficients in a Deeper Look
Hands-on Survival Analysis with Python
What companies can learn from employee turnover data
Building a VAE Playground with Streamlit
With Streamlit creating a deploying a web app can be very easy!
Semantic Search: Measuring Meaning From Jaccard to Bert
Similarity search is one of the fastest-growing domains in AI and machine learning. At its core, it is the process of matching relevant pieces of information together.
Read Excel files with Python. 1000x Faster.
In this article, I’ll show you five ways to load data in Python. Achieving a speedup of 3 orders of magnitude.
The Methods Corpus | Papers With Code
2284 methods • 143838 papers with code.
Same or Different? The Question Flummoxes Neural Networks. | Quanta Magazine
For all their triumphs, AI systems can’t seem to generalize the concepts of “same” and “different.” Without that, researchers worry, the quest to create truly intelligent machines may be hopeless.
Deep Scatterplots
GPBoost: Combining Tree-Boosting with Gaussian Process and Mixed Effects Mo
Combining tree-boosting with Gaussian process and mixed effects models - fabsig/GPBoost
New Machine Learning Gems for Ruby
In August, I set out to improve the machine learning ecosystem for Ruby and wasn’t sure where it would go. Over the next 5 months, I ended up...
Learn R through examples
This is a draft of a book for learning data analysis with the R language. This book emphasizes hands activities. Comments and suggestions are welcome.
Complete Guide to Data Augmentation for Computer Vision
Data Augmentation is one of the most important topics in Deep Computer Vision. When you train your neural network, you should do data augmentation like… ALWAYS. Otherwise, you are not using your…
Sentiment Analysis — Comparing 3 Common Approaches: Naive Bayes, LSTM, and
Sentiment Analysis, or Opinion Mining, is a subfield of NLP (Natural Language Processing) that aims to extract attitudes, appraisals, opinions, and emotions from text. Inspired by the rapid migration…
Interpreting Scattertext: a seductive tool for plotting text
Scroll down to see how to interpret a plot created by a great tool for comparing two classes and their corpora.
Metric-Based (Ratings-based) Conjoint Analysis
In marketing analytics, conjoint analysis is a technique used to gain specific insights about consumers’ preferences. Often derived from consumer surveys, conjoint analysis can tell us, for instance…
Introduction to Object Detection Model Evaluation
Evaluating object detection models is not straightforward because each image can have many objects and each object can belong to different classes. This means that we need to measure if the model…
10 Must Read ML Blog Posts
A collection of high-impact machine learning blog posts.
https://ruder.io/optimizing-gradient-descent/
NeurIPS 2021 Announcement: The Billion-Scale Approximate Nearest Neighbor Search Challenge
We are excited to announce that this year’s NeurIPS 2021 Conference will host a first-of-its-kind competition in large scale approximate…
Machine learning and recommender systems using your own Spotify data
Creating Spotify recommendations with data science
A/B/C Tests: How to Analyze Results From Multi-Group Experiments
Experimentation is widely used at tech startups to make decisions on whether to roll out new product features, UI design changes, marketing campaigns and more, usually with the goal of improving…
Causal ML for Data Science: Deep Learning with Instrumental Variables
Combining data science and econometrics for an introduction to the DeepIV framework, including a full Python code tutorial.
An Introduction to PyTorch Lightning
Word on the street is that PyTorch lightning is a much better version of normal PyTorch. But what could it possibly have that it brought such consensus in our world? Well, it helps researchers scale…
Combinatorial Optimization: The Knapsack Problem
In this story, we are going to discuss an application of dynamic programming techniques to an optimization algorithm. Through the process of developing an optimal solution, we get to study a variety…
Algorithm-Assisted Inventory Curation
Building the raw materials for personalization at scale
How image search works at Dropbox - Dropbox
12 Jupyter Notebook Extensions That Will Make Your Life Easier
Essential extensions that will boost your productivity in Jupyter Notebook.
Theoretical Understandings of Product Embedding for E-commerce Machine Lear
Projects
Find out about all of the projects of Meta Open Source.
Artificial Intelligence Develops an Ear for Birdsong - Scientific American
Machine-learning algorithms can quickly process thousands of hours of natural soundscapes
Game theory as an engine for large-scale data analysis | DeepMind
Modern AI systems approach tasks like recognising objects in images and predicting the 3D structure of proteins as a diligent student would prepare for an exam. By training on many example...
Singular value decomposition - Wikipedia
In linear algebra, the singular value decomposition (SVD) is a factorization of a real or complex matrix into a rotation, followed by a rescaling followed by another rotation. It generalizes the eigendecomposition of a square normal matrix with an orthonormal eigenbasis to any…