Introduction to CNN and its practical implementation in Keras. Classification, Localization, Convolutional, Splot, Kernel, Pooling and more

Using Convolutional Neural Networks in Tensorflow to Analyse Chest XRays. In this short article, we will show how TensorFlow can be used to easily classify image data using deep neural networks. We will showcase the method using the Chest XRay image dataset available on Kaggle.

In this post, I would like to focus on the proposed contextual attention mechanism. Therefore, I briefly cover the coarse-to-fine network architecture, the WGAN adversarial loss, and the weighted L1 loss in above.

Understanding the Indian cab service customer’s requirements. This article presents our research on understanding the cab services throughout India for Uber & Ola using Deep Learning.

Is it possible to recognise alphabetic characters, which have not been provided during training? In this article, I would like to explain and practically demonstrate an area of machine learning called zero-shot learning, which I find really fascinating.

Implementing FaceID Technology with a Siamese Neural Network. Understanding more weird networks.

An approach to train scalable Deep Learning Model for Object Recognition using Convolutional Neural Networks. Recently, machine learning algorithms have contributed greatly to developing very efficient visual search workflows using Convolutional Neural Networks (CNNs).

Exploring the Latent Space of a ConvNet Image Classifier. Linearity in the latent space of hidden layers! I used the pre-trained Resnet model and applied transfer learning on this dataset with added layers for each of the labels.

Review: High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis. We will dive into another inpainting method which can be regarded as an improved version of Context Encoders.

Convolutional Neural Network (CNN) architectures can be pretty general purpose for vision tasks. In this article, I’ll relay my experience in using the same network architecture for 18 different classification tasks.

Lego Minifigure Gender Classification Using Deep Learning. With CNN’s and transfer learning

Introduction to CNNs Without using MNIST! An introduction to CNN with an easily accessible dataset for beginners in deep learning.

An introduction to CNN with an easily accessible dataset for beginners in deep learning. Introduction to CNNs Without using MNIST!

Understanding AlexNet: A Detailed Walkthrough. In this article, we explore AlexNet, a CNN developed by Alex Krizhevsky and others in 2012

Beginner’s Study Notes on Pix2Pix. This tutorial will guide you how to use pix2pix software to learn the image conversion function between parallel data sets of corresponding image pairs.

Convolutional Neural Network: How is it different from the other networks? What’s so unique about CNNs and what does convolution really do? This is a math-free introduction to the wonders of CNNs.

In this project we will develop ideas for a dog identification app using deep learning concepts. The software is intended to accept any user-supplied image as input. I

Neural Architecture Transfer. NAT may be the Next Big Thing in Deep Learning

PyTorch For Deep Learning — Convolutional Neural Networks ( Fashion-MNIST ). This blog post is all about how to create a model to predict fashion mnist images and shows how to implement convolutional layers in the network.

Classification of Traffic Signs Using Deep Learning. This article will explain all the steps taken to design a Deep Learning model to do that.