Articles

YOLOv5 PyTorch Tutorial

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Using YOLOv5 in PyTorch

YOLO, an acronym for ‘You only look once,’ is an open-source software tool utilized for its efficient capability of detecting objects in a given image in real time. The YOLO algorithm uses convolutional neural network (CNN) models to detect objects in an image. 

The algorithm requires only one forward propagation through a given neural network to detect all objects in the image. This gives the YOLO algorithm an edge in speed over others, making it one of the most well-known detection algorithms to date.

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An Introduction to Graph Data

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This article is an excerpt from the book Machine Learning with PyTorch and Scikit-Learn from the best-selling Python Machine Learning series, updated and expanded to cover PyTorch, transformers, and graph neural networks.

Broadly speaking, graphs represent a certain way we describe and capture relationships in data. Graphs are a particular kind of data structure that is nonlinear and abstract. And since graphs are abstract objects, a concrete representation needs to be defined so the graphs can be operated on. Furthermore, graphs can be defined to have certain properties that may require different representations. Figure 1 summarizes the common types of graphs, which we will discuss in more detail in the following subsections:
Common types of graph

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What Twitter and Facebook Can Teach Us About Machine Learning

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Facebook and Twitter have left most other companies around the world far behind when it comes to using machine learning to improve their business model. And while their practices haven’t always resulted in the best reactions from end-users, there’s much to be learned from these companies on what to do–and what not to do–when it comes to scaling and applying data analytics.

Get the Data You Need First

While Facebook seemingly uses machine learning for everything — it is used for content detection and content integrity, sentiment analysis, speech recognition, and fraudulent account detection, as well as operating functions like facial recognition, language translation, and content search functions. The Facebook algorithm manages all this while offloading some computation to edge devices in order to reduce latency.

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TensorFlow vs. PyTorch vs. Keras for NLP

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Before beginning a feature comparison between TensorFlow, PyTorch, and Keras, let’s cover some soft, non-competitive differences between them.

Non-competitive facts:

Below, we present some differences between the 3 that should serve as an introduction to TensorFlow, PyTorch, and Keras. These differences aren’t written in the spirit of comparing one with the other but with a spirit of introducing the subject of our discussion in this article.

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