Convolutional

As Computer Vision applications are becoming omnipresent in our lives, understanding the functioning principles of Convolutional Neural Networks is essential for every Data Science practitionerSource: canva.comIn my previous article, I built a Deep Neural Network without using popular modern deep learning libraries such as Tensorflow, Pytorch, and Keras. I later used that network to classify handwritten digits. The obtained results were not state-of-the-art level, but they were nevertheless satisfactory. Now I want to…

Everything you need to know about quantum convolutional neural networks (QCNNs), including the benefits and limitations of these approaches compared to classical computing methodsIQM Quantum Computer in Espoo Finland by RagsxlIn recent years investment in quantum computing has increased significantly, with quantum approaches to areas such as security and network communication expected to upend existing classical computing techniques.Researchers such as Garg and Ramakrishnan identify that at its core, quantum computing…

With code examples in PyTorch and TensorFlowDeep learning (DL) has seen an enormous increase in popularity in various fields. DL has been used for brain-computer interfaces (BCIs) with electroencephalography (EEG) as well. However, DL models needed to be adapted for EEG data. How has this been done, and how successful are DL approaches in the field of BCIs?Figure 1. Photo by Josh Riemer on UnsplashIn this post, we first explain why DL can be advantageous compared to the traditional machine learning (ML) methods for BCIs.…

Get yourself ready with these top CNN questions that will help you crack FAANG interviews Convolutional Neural Networks are a significant tool for most machine learning practitioners today. However, understanding these networks and learning to use them for the first time can be a challenge. In CNNs, convolution is the first layer to excerpt features from an input image. It is a mathematical operation, creating a set of weights and essentially making a representation of parts of the image. Convolution of an image with…

Last Updated on July 13, 2022 Convolutional neural networks have been found successful in computer vision applications. Various network architectures are proposed and they are neither magical nor hard to understand. In this tutorial, we will make sense of the operation of convolutional layers and their role in a larger convolutional neural network. After finishing this tutorial, you will learn: How convolutional layers extract features from image How different convolutional layers can stack up to build a neural network…

Neural NetworksAn overview of DCGAN architecture with a step-by-step guide to building it with Keras / TensorflowImage by 52Hertz from PixabayData Scientists use Generative Adversarial Networks (GANs) for a wide range of tasks, with image generation being one of the most common. A particular type of GAN known as DCGAN (Deep Convolutional GAN) has been created specifically for this.In this article, I will explain DCGANs and show you how to build one in Python using Keras/Tensorflow libraries. Then, we will use it to…

Neural Networks and Deep Learning Course: Part 24Original image by Gerd Altmann from Pixabay, edited by authorPrerequisite: Convolutional Neural Network (CNN) Architecture Explained in Plain English Using Simple Diagrams (Highly recommended)In Part 23, I discussed the layers and operations in a Convolutional Neural Network (CNN) in detail. If you have read that one, now you understand how CNNs work behind the scenes.Today, we’ll discuss how to build a CNN using Keras Sequential API. We’ll discuss, in detail, how to…

Wait, computers know how to see?!?I recently had to conduct research using Convolutional Neural Networks (CNN) and was constantly trying to understand what they were on my own. While studying the different features was not difficult, the hardest part was trying to explain CNNs to my friends and family in a way that made sense and was not too technical. Today I wanted to provide the technical definition of CNNs coupled with the nontechnical definition to help you gain a greater grasp of what a CNN is, as well as help you…

Neural Networks and Deep Learning Course: Part 23Original image by Gerd Altmann from Pixabay, edited by authorWe’ve already discussed one neural network architecture — Multilayer Perceptron (MLP). An MLP is not suitable to use with image data as a large number of parameters are involved in the network even for small images.Convolutional Neural Networks (CNNs) are specially designed to work with images. They are widely used in the domain of computer vision.Here are the two main reasons for using CNNs instead of MLPs when…

Neural NetworksA Detailed Explanation of Transposed Convolutions with a Simple Python ExampleTransposed Convolutional Neural Networks. Image by author.Convolutional Neural Networks revolutionized the space of image categorization and object detection. But have you heard about Transposed Convolutions, and would you know how to use them?In this article, I will explain what Transposed Convolutions are, how they compare to regular Convolutions and show you how to build a simple Neural Network that utilizes them for image…