A couple of days ago, in our lab session, we discussed random forrests, and, since it was based on the example in ISLR, we had a quick discussion about the random choice of features, and the “” rule Interestingly, on that one, we can play a bit, and try all choices, and do it…
A couple of days ago, in our lab session, we discussed random forrests, and, since it was based on the example in ISLR, we had a quick discussion about the random choice of features, and the “” rule Interestingly, on that one, we can play a bit, and try all choices, and do it…
Where can you find projects dealing with advanced ML topics? GitHub is a perfect source with its many repositories. I’ve selected ten to talk about in this article.
Learn which variables you should and should not take into account in your model.
An interpretable outlier detector based on multi-dimensional histograms.
Types of Functions > Basis functions (called derived features in machine learning) are building blocks for creating more complex functions. In other
Cosine similarity can measure the proximity between two documents by transforming words into vectors within a vector space.
Cool LLM and GenAI tech questions covering many modern concepts, including fast vector search, contextual tokens, and augmented structures
Understanding the importance of permutations in the field of explainable AI
Discussing AI Research Papers in March 2024
Imagine you're at a party separating people who love pizza (yum!) from those who...well, have...
New. Comprehensive. Extendable.
Platforms like Kickstarter and Indiegogo have not only broadened access to funding to companies that might struggle in the capital markets but have also transformed the way companies connect with consumers during product development, replacing focus groups with real customers…
#CatBoost - state-of-the-art open-source gradient boosting library with categorical features support,
A Schur decomposition of a matrix $latex A\in\mathbb{C}^{n\times n}$ is a factorization $LATEX A = QTQ^*$, where $LATEX Q$ is unitary and $LATEX T$ is upper triangular. The diagonal entries of $LAT…
Data comes in different shapes and forms. One of those shapes and forms is known as categorical data.
This article aims to take away the entry barriers to get started with time series analysis in a hands-on tutorial using Prophet
Why is Adam the most popular optimizer in Deep Learning? Let’s understand it by diving into its math, and recreating the algorithm.
How should we choose between label, one-hot, and target encoding?
LDA explained with a dog pedigree model
A dynamic approach to treatment personalization
Which measure of correlation should you use for your task? Learn all you need to know about Pearson and Spearman correlations.
Insanely fast and reliable smoothing and interpolation with the Whittaker-Eilers method.
This year has felt distinctly different. I've been working in, on, and with machine learning and AI for over a decade, yet I can't recall a time when these fields were as popular and rapidly evolving as they have been this year. To conclude an eventful 2023 in machine learning…
Understanding the logic behind AdaBoost and implementing it using Python
This tutorial offers a bridge between the abstract mathematics of manifolds and computational practice.
In this article, I'll take you through the task of Market Basket Analysis using Python. Market Basket Analysis using Python.
Independent component analysis (ICA) is a powerful data-driven tool capable of separating linear contributions in the data
It is possible to design and deploy advanced machine learning algorithms that are essentially math-free and stats-free. People working on that are typically professional mathematicians. These algorithms are not necessarily simpler. See for instance a math-free regression…
An alternative of logistic regression in special conditions
Or "that time I built Excel for Uber and they ditched it like a week after launch"
A world without if
BookCorpus has helped train at least thirty influential language models (including Google’s BERT, OpenAI’s GPT, and Amazon’s Bort), according to HuggingFace. This is the research question that…
Learn how to compute the Pearson, Spearman and Kendall correlation coefficients by hand to evaluate the relationship between two variables
In the era of hyper-sophisticated machine learning models like ChatGPT, it is surprising how effective the classic decision tree model remains, especially when used in conjunction with other techniques, such as bagging, boosting and random forests. In this blog post we…
Applying Reinforcement Learning strategies to real-world use cases, especially in dynamic pricing, can reveal many surprises
Intuitive derivation of the KDE formula
Standardization, Normalization, Robust Scaling, Mean Normalization, Maximum Absolute Scaling and Vector Unit Length Scaling
Understanding the purpose and functionality of common metrics in ML packages
Learn how Self-Organizing Maps work and why they are a useful unsupervised learning algorithm
Discover the concepts of Zero-Shot, One-Shot, and Few-Shot Learning, which enable machine learning models to classify and recognize objects or patterns with a limited number of examples.
Learn to build a Polynomial Regression model to predict the values for a non-linear dataset.
How to explore geographic data with HDBSCAN, H3, graph theory, and OSM.
How a Scientist Playing Solitaire Forever Changed the Game of Statistics
This tutorial explores the LightGBM library in Python to build a classification model using the LGBMClassifier class.
Hierarchical Navigable Small World (HNSW) is a state-of-the-art algorithm used for an approximate search of nearest neighbours. Under the…