Found 14 Articles for Deep Learning

PointNet analyzes point clouds by directly consuming the raw data without voxelization or other preprocessing steps. A Stanford University researcher proposed this novel architecture in 2016 for classifying and segmenting 3D representations of images. Key Properties Within point clouds, PointNet considers several key properties of Point Sets. A Point Cloud consists of unstructured sets of points, and it is possible to have multiple permutations within a single Point Cloud. If we have N points, there are N! There are several ways to order them. Using permutation invariance, PointNet ensures that the analysis remains independent of different permutations. As a result, ... Read More

John Hopfield came up with the Hopfield Neural Network in 1982. In 1982, John Hopfield developed what is now known as the Hopfield Neural Network. It's a synthetic network that mimics the brain's activity. This recurrent neural network can model associative memory and pattern recognition issues. The Hopfield Neural Network helps find solutions to various issues. Image and voice recognition, optimization, and combinatorial optimization are just some of the numerous applications that have benefited from their use. The Architecture of the Hopfield Neural Network A Hopfield Neural Network mainly consists of a single layer of interconnected neurons. An ultimately linked ... Read More

DPCCNN, or "Deep parametric Continuous Convolutional Neural Network, " is a type of neural network that is used, among other things, to classify pictures, find objects in pictures, and divide up pictures into parts. DPCCNN is an upgraded version of Convolutional Neural Networks (CNNs) that use continuous functions instead of discrete convolutional filters. Parametric Continuous Convolution In DPCCNNs, convolution is done with a function called the parametric continuous convolution (PCC), which is a continuous function. Considered a function, PCC takes an image and some values as input, returns a continuous function as output, and gets a convolutional result. Architecture DPCCNNs ... Read More

The use of Artificial Intelligence (AI) in facial identification has completely transformed the computer vision field. This advancement allows machines to verify and distinguish individuals based on their distinct facial characteristics. This state-of-the-art technology employs algorithms for machine learning and deep learning components, aiding in the extraction of facial patterns and their comparison with a database of familiar faces. Facial identification has now become an essential aspect of our daily lives, finding extensive applications in personalized user experiences and security systems. This article explores the utilization of AI in facial recognition, its impact across various industries, and the different techniques ... Read More

A transfer neural network is a deep learning architecture that handles long-range dependencies well, as was first described in Vaswani et al's 2017 paper "All you need is attention.". The self-attention mechanism of transformer networks allows them to identify relevant parts of input sequences. What are Recurrent Neural Networks? Recurrent neural networks are artificial neural networks that have memory or feedback loops. They are designed to process and classify sequential data in which the order of the data points is important. The network works by feeding the input data into a hidden layer, allowing the network to maintain information ... Read More

A specific kind of Deep Learning (DL) known as recurrent neural networks (RNNs) excels at analyzing input consecutively. They are widely used in several fields, such as Natural Language Processing (NLP), language translation and many others. This article will examine a number of well-liked RNN versions and dive into the underlying mathematical ideas. Basics of Recurrent Neural Networks Recurrent neural networks are a specific type of neural network structure that can deal with information in sequence by maintaining an inner state. They are also known as hidden states. An RNN works similarly for every component in a sequence while preserving ... Read More

Holistically-Nested Edge Detection (HED) is a deep learning-based method for detecting edges in images which can be performed using deep learning and a Python library, OpenCV. The holistically-Nested Edge detection was first introduced by Xie and Tu in 2015 and has since been widely used in computer-vision applications.Currently, it has gained a lot of popularity in recent years due to its ability to produce accurate and high-quality edge maps in an image. In this article, we will discuss the basics of HED, how it works, and how to implement it using OpenCV and deep learning, and also using Canny ... Read More

Deep learning has emerged as a major area of study for academics and developers as industry continue to embrace the possibilities of artificial intelligence. Deep learning is a branch of machine learning that focuses on the structure and operations of the human brain in order to create algorithms that can recognise patterns and predict outcomes. In this article , we will look at 5 deep learning project ideas for beginners that are simple to implement and provide practical insights into the area of deep learning. Who can Benefit from this Article? This article is intended for newbies who are interested ... Read More

When it comes to deep learning frameworks, PyTorch and TensorFlow are two popular choices. Both have gained significant traction in the field and are widely used by researchers, developers, and data scientists. In this article, we will compare PyTorch and TensorFlow to help you understand their similarities, differences, and use cases. PyTorch: A Deep Dive PyTorch is an open-source machine learning library that provides a dynamic computational graph and intuitive interface for building and training neural networks. It offers flexibility and customization, allowing users to easily define and modify models. PyTorch's strong support for GPU acceleration enables efficient training ... Read More

Generative Adversarial Networks (GANs) have emerged as a groundbreaking innovation in the field of artificial intelligence, captivating researchers and artists a like. This powerful deep learning technique employs a unique dual-network framework, where a generator and a discriminator engage in continuous competition, resulting in the creation of remarkably realistic and novel outputs. In this article, we delve into the inner workings of GANs, their applications across various domains, and the fascinating possibilities they offer in pushing the boundaries of human creativity. What is Generative Adversarial Networks (GANs)? Generative Adversarial Networks (GANs) belong to a category of AI algorithms that ... Read More