Daily Dose of Data Science | Avi Chawla | Substack·1y25 Most Important Mathematical Definitions in DS
A visual presentation of crucial mathematical definitions used in Data Science and Statistics, such as Gradient Descent, Normal Distribution, MLE, Z-score, and SVD. The post explains these terms and their significance in various applications like dimensionality reduction, optimization, and data modeling.
Hacker News·52wUnsure Calculator
The Unsure Calculator allows users to perform calculations with uncertain values, using range notation to specify the range within which the actual values are expected to fall. The tool simplifies the understanding of statistics by allowing non-exact numbers to be used in calculations, thus making it accessible to a broader audience. Originally created to help with everyday financial decisions, it also finds use in complex equations like the Drake equation in astrophysics. Despite its simplicity and limitations, it runs 250K AST-based computations using the Monte Carlo method for each calculation.
Towards Data Science·48wHow to Learn the Math Needed for Machine Learning
Machine learning requires understanding three key math areas: statistics, calculus, and linear algebra. While deep research roles necessitate advanced math knowledge, industry roles often demand less. Statistics focuses on descriptive analysis and probability theory, while calculus deals with differentiation and integration crucial for algorithms like gradient descent. Linear algebra is foundational for data representation in vectors and matrices. Various resources are available, including textbooks and online courses, helping learners sharpen their math skills for machine learning.
Reid Burke·15wsteipete/RepoBar: Show status of GitHub Repos right in your menu bar and terminal: CI, Issues, Pull Requests, Latest Release.
RepoBar is a macOS menu bar application that provides a dashboard for GitHub repositories without opening a browser. It displays CI status, releases, pull requests, issues, and activity metrics. The tool includes local Git state monitoring, automatic repository syncing, OAuth authentication via Keychain, and a bundled CLI for terminal access. Installation is available via Homebrew or direct download, with auto-updates through Sparkle.
Daily Dose of Data Science | Avi Chawla | Substack·37wHow Do LLMs Work?
Large Language Models work by predicting the next word in a sequence using conditional probability. They calculate probabilities for each possible next word given the previous context, then select the most likely candidate. To avoid repetitive outputs, LLMs use temperature sampling which adjusts the probability distribution - low temperature produces focused, predictable text while high temperature creates more random, creative outputs. The models learn high-dimensional probability distributions over word sequences, with trained weights serving as the parameters of these distributions.
The Palindrome·32wCorrelation vs. cosine similarity
Explores the key differences between Pearson correlation and cosine similarity, two statistical measures for quantifying relationships between variables. While both are based on dot products, correlation performs double normalization (mean-centering and variance scaling) while cosine similarity only normalizes by magnitude. Through mathematical explanations and Python simulations, the post demonstrates that these measures can yield dramatically different results depending on data scaling and offsets. Correlation is recommended when measurement units are arbitrary or different, while cosine similarity is preferred when variables share meaningful units, particularly in machine learning applications with vector embeddings.
The Coding Gopher·49w99% of Developers Don't Get Poisson Distribution
Many developers overlook the importance of understanding concepts like the Poisson distribution, which is crucial for modeling rare events happening at a constant rate in a fixed interval. This distribution is applicable in various fields, such as biology and physics, where it helps assess probabilities of events like mutations or decay. Understanding these statistical principles can enhance logical reasoning in software development.
Computerphile·1ySolve Markov Decision Processes with the Value Iteration Algorithm - Computerphile
The value iteration algorithm is a method for solving Markov decision processes (MDPs) to produce optimal action decisions. MDPs model decision-making problems, particularly those under uncertainty. The algorithm iteratively computes the values of states to find the policy that minimizes cost or maximizes reward. It is essential for decision-making models where dynamic programming techniques are applied to achieve the best outcome.
The Palindrome·44wThe Anatomy of Logistic Regression
Logistic regression transforms geometric relationships into probability predictions through a step-by-step process. Starting with linear transformation (ax + b) to create logits, the model applies exponential functions and sigmoid activation to map any real number to a probability between 0 and 1. The geometric aspect becomes clear in higher dimensions where the decision boundary forms lines or planes, with logits representing signed distance from these boundaries. This fundamental approach demonstrates how machine learning models convert spatial relationships into probabilistic predictions.
Towards Data Science·37wThe ONLY Data Science Roadmap You Need to Get a Job
A comprehensive learning roadmap for aspiring data scientists covers six core areas: statistics (summary statistics, probability, hypothesis testing), mathematics (calculus and linear algebra), programming (Python and SQL), technical tools (Git, command line, package management), machine learning fundamentals (regression, decision trees, neural networks), and optional deep learning concepts. The guide emphasizes mastering fundamentals over chasing latest trends, recommending specific textbooks like 'Practical Statistics for Data Science' and courses like Andrew Ng's Machine Learning Specialization. Each section includes practical learning resources and focuses on skills directly applicable to entry-level data science positions.
YouTube·37wData Science Full Course 2025 (FREE) | Intellipaat
A comprehensive data science course covering the complete project lifecycle from business problem identification to model deployment. The course explains data science fundamentals through a practical example of supply chain optimization, demonstrates linear regression with detailed mathematical explanations, and provides a year-long roadmap for becoming a data scientist. Key topics include statistics, Python programming, exploratory data analysis, machine learning algorithms, and portfolio building through Kaggle competitions.
Towards AI·1yThe Math Behind Machine Learning: Linear Algebra, Calculus & Probability
Machine learning relies heavily on mathematics, including linear algebra, calculus, and probability. This post serves as a crash course to help readers understand these fundamental concepts and their importance in making machine learning work, breaking down intimidating topics into more digestible explanations.
Artem Kirsanov·1yHow Brains & Machines Master Probability
Brains and AI systems both face the challenge of reasoning under uncertainty with incomplete data. Variational inference is a mathematical tool used to create efficient models from limited clues. By using concepts like the evidence lower bound (ELBO) and latent variables, both natural and artificial systems can effectively manage high-dimensional data. Techniques such as important sampling and Jensen's inequality play crucial roles in this process, enabling more accurate and computationally feasible models.
