Pete Warden shares his experience and knowledge about the memory layout of the Raspberry Pi Pico board, specifically the RP2040 microcontroller. He encountered baffling bugs while updating TensorFlow Lite Micro and traced them to poor understanding of the memory layout. The article provides detailed insights into the physical and RAM layouts, stack behavior, and potential pitfalls.
A step-by-step guide on understanding and implementing t-SNE for visualizing high-dimensional data using Python.
ASCVIT V1 aims to make data analysis easier by automating statistical calculations, visualizations, and interpretations.
Includes descriptive statistics, hypothesis tests, regression, time series analysis, clustering, and LLM-powered data interpretation.
- Accepts CSV or Excel files. Provides a data overview including summary statistics, variable types, and data points.
- Histograms, boxplots, pairplots, correlation matrices.
- t-tests, ANOVA, chi-square test.
- Linear, logistic, and multivariate regression.
- Time series analysis.
- k-means, hierarchical clustering, DBSCAN.
Integrates with an LLM (large language model) via Ollama for automated interpretation of statistical results.
Includes descriptive statistics, hypothesis tests, regression, time series analysis, clustering, and LLM-powered data interpretation.
- Accepts CSV or Excel files. Provides a data overview including summary statistics, variable types, and data points.
- Histograms, boxplots, pairplots, correlation matrices.
- t-tests, ANOVA, chi-square test.
- Linear, logistic, and multivariate regression.
- Time series analysis.
- k-means, hierarchical clustering, DBSCAN.
Integrates with an LLM (large language model) via Ollama for automated interpretation of statistical results.
An overview of clustering algorithms, including centroid-based (K-Means, K-Means++), density-based (DBSCAN), hierarchical, and distribution-based clustering. The article explains how each type works, its pros and cons, provides code examples, and discusses use cases.
The article discusses the resurgence of programming languages designed specifically for AI development, highlighting Mojo as a promising example. It explores the historical context of AI-focused languages, the limitations of Python for AI, and the features and benefits of Mojo and other emerging AI languages.
Learn how to set up the Raspberry Pi AI Kit with the new Raspberry Pi 5. The kit allows you to explore machine learning and AI concepts using Python and TensorFlow.
Learn how to use Python and OpenCV to perform face detection and recognition. This tutorial also covers concepts like bounding boxes, intersection over union (IoU), and grayscale conversion.
A simple and intuitive explanation of DBSCAN (Density-Based Spatial Clustering of Applications with Noise), a clustering algorithm that can identify outliers, extract new features, compress data, and perform novelty detection. The article provides a fast implementation of DBSCAN in Python.
Generate realistic sequential data with this easy-to-train model. This article explores using Variational Autoencoders (VAEs) to model and generate time series data. It details the specific architecture choices, like 1D convolutional layers and a seasonally dependent prior, used to capture the periodic and sequential patterns in temperature data.
Learn how to use Autoencoders to detect anomalies in time series data in a few lines of code.
