Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural ...

This project aims to build a convolutional neural network from scratch to automatically classify traffic signs into predefined categories and deploy the model using Streamlit. The model successfully ...

This book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in ... kernel methods, Gaussian processes, deep neural networks, ...

Western researchers have developed a novel technique using math to understand exactly how neural networks make decisions – a widely recognized but poorly understood process in the field of machine ...

Abstract: As we saw in the previous chapter, machine learning comprises three components: the underlying model, the cost function, and a method of modifying the parameters of the model. In this ...

What started as a side hustle to fund her PhD in engineering has turned into a full-time career for Zara Dar. In a viral post ...

Techniques studied include basic classification techniques, feedforward neural networks, attention mechanisms, pre-trained large language models (BERT-style encoders and GPT-style LLMs), and ...

Zar Dar grabbed headlines last month after she sensationally announced that she was halting her PhD pursuit and becoming an ...