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Category Archives: Machine Learning

Stationary tracking is when a device or system is able to track an object or person without moving. For example, a security camera that is… Read More
Transposed convolution, also known as fractionally-strided convolution, is a technique used in convolutional neural networks (CNNs) for the upsampling layer that increases the spatial resolution… Read More
SciPy is a Python library used for scientific and technical computing. It is built on top of NumPy, a library for efficient numerical computing, and… Read More
Deep learning is a subset of machine learning that uses artificial neural networks (ANNs) to model and solve complex problems. It is based on the… Read More
Self-Supervised Learning is a deep learning methodology where a model is pre-trained using unlabelled data and the data labels are generated automatically, which are further… Read More
As machine learning models continue to become more popular and widespread, it is important for data scientists and developers to understand how to build the… Read More
In PyTorch, the torch.nn.Linear class is a linear layer that applies a linear transformation to the input data. It is called linear transformation because it… Read More
Image enhancement is the process of improving the quality and appearance of an image. It can be used to correct flaws or defects in an… Read More
A transposed convolutional layer is an upsampling layer that generates the output feature map greater than the input feature map. It is similar to a… Read More
Super-resolution (SR) implies the conversion of an image from a lower resolution (LR) to images with a higher resolution (HR). It makes wide use of… Read More
An epoch in machine learning is one complete pass through the entire training dataset. One pass means a complete forward and backward pass. It is… Read More
This article discusses the concept of dimensionality reduction, specifically using the Swiss Roll dataset and the Locally Linear Embedding (LLE) algorithm. The article discusses the… Read More
TensorFlow provides mathematical operations and logical gates with tensorflow.math module. As input, it requires a tensor. For boolean operations, we can use boolean values True,… Read More
Word sense disambiguation (WSD)  in Natural Language Processing (NLP) is the problem of identifying which “sense” (meaning) of a word is activated by the use… Read More
Time series forecasting is the process of using historical data to make predictions about future events. It is commonly used in fields such as finance,… Read More

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