How to maximize predictive performance by creating a strong learner from multiple weak ones — Complex problems are rarely solved through singular thought or action. A collective weather forecast produced by a team of meteorologists, each with a different perspective, will likely result in better predictions than that produced by a single individual. …

A foundational model generated through neural architecture search, innovative quantization blocks, and a robust pre-training paradigm — In the domain of object detection, YOLO (You Only Look Once) has become a household name. Since the release of the first model in 2015, the YOLO family has been growing steadily, with each new model outperforming its predecessor in mean average precision (mAP) and inference latency.

How to set up and configure a forecasting system in just a few clicks — Introduction Time-series forecasting has become a ubiquitous tool for businesses, governments, and individuals alike. …

A comprehensive guide covering the most commonly used evaluation metrics used in regression and their utility in different scenarios — Before building a regression model, it’s worth taking a moment to carefully think about how to evaluate it. …

A comprehensive guide covering the most commonly used evaluation metrics for supervised classification and their utility in different scenarios — In order to properly evaluate a classification model, it is important to carefully consider which evaluation metric is the most suitable. This article will cover the most commonly used evaluation metrics for classification tasks, including relevant example cases, and will provide you with the information necessary to help you choose…

Learn how to leverage AutoML to maximize the outcome of your machine learning workflows — Many of us are familiar with the challenge of selecting a suitable machine learning model for a specific prediction task, given the vast number of models to choose from. On top of that, we also need to find optimal hyperparameters in order to maximize our model’s performance. These challenges can…

How a complex and cutting-edge approach, creatively borrowed from computer vision, can be implemented in just a few clicks. — Introduction With an ever-increasing number of applications and services deployed worldwide, time-series anomaly detection has become a ubiquitous and indispensable tool for capturing metric regressions. However, setting up an anomaly detection system is certainly not straightforward and often requires a fair amount of domain expertise. …

A complete guide on how to use argparse for your command line applications and how it can be leveraged for machine learning projects — If you’ve spent some time roaming around in the world of data science or software engineering, you have most likely come across some applications that require you to use your command-line interface, or CLI. …

A practical guide to object detection in images, videos, and real-time webcam feed using both CLI and Python — Introduction Object detection, a subfield of computer vision, is primarily concerned with the identification and localization of objects in images or videos with a certain degree of confidence. An identified object is generally annotated with a bounding box, which provides information to the viewer about the object’s nature and location in…