This is the third part in a series on Convnets. Read the first part here and the second part here. Shared weights The shared weight design is exactly why convolutional nets (Convnets for short) are good at detecting the same features in different parts of an image. When I mentioned shared weights... Read more

The Weakness of Traditional Neural Networks, as opposed to Convnets Following on Part I of “Why are Convnets often Better than the Rest?”, we will now look at a traditional neural network’s weakness. As before, I am going to assume you understand the basics of neural network modeling already, including... Read more

Introduction In this series, I will explore convolutional neural networks in comparison to standard neural networks. To begin with, the former is an evolution of the latter. Through analyzing this evolution, it is fascinating to see how particular design differences have such a great impact on performance and overall success.... Read more

Article by Carlotta Castelluccio and Dmitry Soshnikov of Microsoft. As human beings, we know what a cat looks like. But what about neural networks? In this post, we reveal what a cat looks like inside a neural network brain, and also talk about adversarial attacks. Real and Ideal cat. Image... Read more

The discipline of AI most often mentioned these days is deep learning (DL) along with its many incarnations implemented with deep neural networks. DL also is a rapidly accelerating area of research with papers being published at a fast clip by research teams from around the globe. I enjoy keeping... Read more

Deep learning has continued its forward movement during 2019 with advances in many exciting research areas like generative adversarial networks (GANs), auto-encoders, and reinforcement learning. In terms of deployments, deep learning is the darling of many contemporary application areas such as computer vision, image recognition, speech recognition, natural language processing,... Read more

Academic institutions, AI labs, and research departments of other organizations are constantly generating novel insights into data science, whether it’s machine learning, deep learning, NLP, or other disciplines. Summer 2019 generated some interesting machine learning research, and here are a few of our top picks. [Related Article: The Best Machine... Read more

In this blog, we are going to classify images using Convolutional Neural Network (CNN), and for deployment, you can use Colab, Kaggle, or even use your local machine since the dataset size is not very large. At the end of this, you will be able to build your own image... Read more

A key avenue for deploying deep learning models is a mobile device. The advantage of running models in mobile apps instead of sending them to the cloud is the reduction in latency and the ability to ensure data privacy for users. Despite the variety of deep learning libraries and AI... Read more

Deep learning models like convolutional neural networks (ConvNet) require large amounts of data to make accurate predictions. In general, sufficient sample size for a ConvNet application would involve tens of thousands of images. Often, only a few thousand labeled images are available for training, validation, and testing. Challenges associated with... Read more