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.
A new LSTM model, sLSTM, is introduced to improve long-term time series forecasting accuracy. It's evaluated on benchmark datasets and compared to other state-of-the-art methods.
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.
Stephen Wolfram discusses the mysteries of machine learning and offers some minimal models to understand the essence of neural nets and their learning process.
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.
This article explains BERT, a language model designed to understand text rather than generate it. It discusses the transformer architecture BERT is based on and provides a step-by-step guide to building and training a BERT model for sentiment analysis.
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.
This article explains how Palo Alto Networks uses autoencoders to profile DNS traffic and detect malicious domains based on unique patterns and characteristics.
Problem: Malicious DNS traffic often exhibits unique patterns that can be used for detection. However, analyzing raw DNS data is complex and computationally.
An autoencoder is used to transform dynamic DNS traffic data into lower-dimensional vectors called DNS profiles, efficiently capturing the characteristics of the traffic.
- Classification: Identifies malicious domains based on their profiles.
- Clustering: Groups malicious domains with similar traffic patterns, revealing attack types (e.g., DDNS, tunneling, heartbeats).
- Anomaly Detection: Identifies unusual traffic patterns that may indicate malicious activity or unintentional issues.
Problem: Malicious DNS traffic often exhibits unique patterns that can be used for detection. However, analyzing raw DNS data is complex and computationally.
An autoencoder is used to transform dynamic DNS traffic data into lower-dimensional vectors called DNS profiles, efficiently capturing the characteristics of the traffic.
- Classification: Identifies malicious domains based on their profiles.
- Clustering: Groups malicious domains with similar traffic patterns, revealing attack types (e.g., DDNS, tunneling, heartbeats).
- Anomaly Detection: Identifies unusual traffic patterns that may indicate malicious activity or unintentional issues.
Learn how to use Autoencoders to detect anomalies in time series data in a few lines of code.
A mapping of Vespa terminology to equivalent concepts in Elasticsearch, OpenSearch, and Solr.
