Multi-armed bandits are a simple but very powerful framework for algorithms that make decisions over time under uncertainty. “Introduction to Multi-Armed Bandits” by Alex Slivkins provides an accessible, textbook-like treatment of the subject.
DataRobot MLOps is helping to increase AI value by automating the deployment, optimization, and governance of machine learning applications.
Many imbalanced classification tasks require a skillful model that predicts a crisp class label, where both classes are equally important. An example of an imbalanced classification problem where a class label is required and both classes are equally important is the detection…
Imbalanced classification is primarily challenging as a predictive modeling task because of the severely skewed class distribution. This is the cause for poor performance with traditional machine learning models and evaluation metrics that assume a balanced class distribution…
By popular demand, I’ve updated this article with the latest tutorials from the past 12 months. Check it out here
Learn about the model that is used in most reinforcement learning problems.
Delivering accurate insights is the core function of any data scientist. Navigating the development road toward this goal can sometimes be tricky, especially when cross-collaboration is required, and these lessons learned from building a search application will help you…
This article will introduce you to Markov Chain Monte Carlo (MCMC) methods, namely Metropolis-Hastings and Bayesian inference, and demonstrate how you can harness them for your next project.
MDP in action: the next step toward solving real-life problems with RL and AI
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This is the fourth post in an article series about MIT's Linear Algebra course. In this post I will review lecture four on factorizing a matrix A into a product of a lower-triangular matrix L and an upper-triangular matrix U, or in other words A=LU. The lecture also shows how to…
Machine learning is one of the hottest topics in computer science today. And not without a reason: it has helped us do things that couldn’t be done before like image classification, image generation and natural language processing. But all of it boils down to a really simple…
This article is about Market Basket Analysis & the Apriori algorithm that works behind it.
Estimating expected time of arrival (ETA) is crucial to what we do at Lyft. Estimates go directly to riders and drivers using our apps, as…
Which algorithm works best for unbalanced data? Are there any tradeoffs?
Learn the basics of verifying segmentation, analyzing the data, and creating segments in this tutorial. When reviewing survey data, you will typically be handed Likert questions (e.g., on a scale of 1 to 5), and by using a few techniques, you can verify the quality of the survey…
This list of lists contains books, notebooks, presentations, cheat sheets, and tutorials covering all aspects of data science, machine learning, deep learning, statistics, math, and more, with most documents featuring Python or R code and numerous illustrations or case studies…
2.1K votes, 110 comments. 1.3M subscribers in the Python community. The official Python community for Reddit! Stay up to date with the latest news…
Preparing for a job interview can be a full-time job, and Data Science interviews are no different. Here are 121 resources that can help you study and quiz your way to landing your dream data science job.
This post is about explaining the various techniques you can use to handle imbalanced datasets
Learn how to implement adversarial validation that builds a classifier to determine if your data is from the training or testing sets. If you can do this, then your data has issues, and your adversarial validation model can help you diagnose the problem.
An introduction on how to fine-tune Machine and Deep Learning models using techniques such as: Random Search, Automated Hyperparameter Tuning and Artificial Neural Networks Tuning.
Behind every recommender system lies a bevy of metrics.
Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements several…
Evolutionary algorithms are an unsupervised learning alternative to neural networks that rely on fitness functions instead of trained nodes for evaluation.
What is Independent Component Analysis (Statistics)?
Deep learning
Atlas: A Dataset and Benchmark for E-commerce Clothing Product Categorization - vumaasha/Atlas
Solving real-world problems with probabilities
Correlation coefficients enable to you find relationships between a wide variety of data. However, the sheer number of options can be overwhelming. This picture sums up the differences between five of the most popular correlation coefficients. Part two covers several less…
This post is about various evaluation metrics and how and when to use them.
Over the past few years, deep learning has been taking by storm many industries. From voice recognition to image analysis and synthesis, neural networks have turned out to be very efficient at solv…
I'm planning on sorting ~100,000 images to use as data for a computer vision application. With this much data, shaving a little time off of each…
Density estimation is estimating the probability density function of the population from the sample
23 votes, 13 comments. A recent blog post How Exactly UMAP Works provides a different perspective on explaining the UMAP dimensionality reduction…
Is the Rectified Adam (RAdam) optimizer actually better than the standard Adam optimizer? According to my 24 experiments, the answer is no, typically not (but there are cases where you do want to use it instead of Adam).
We show what metric to use for visualizing and determining an optimal number of clusters much better than the usual practice — elbow method.
Data Science Dojo blog features the most recent, and relevant articles about data science, analytics, generative AI, large language models, machine learning, and data visualization.
This post is about various evaluation metrics and how and when to use them.
UMAP is a new dimensionality reduction technique that offers increased speed and better preservation of global structure.
MLCommons ML benchmarks help balance the benefits and risks of AI through quantitative tools that guide responsible AI development.
This guide explores research centered on a variety of advanced loss functions for machine learning models.