Aditya Sharma
The Intel-OpenVINO Toolkit provides many great functionalities for Deep-Learning model optimization, inference and deployment. Perhaps the most interesting and practical tool among them is the Deep-Learning (DL) workbench. Not only
Traditionally, Deep-Learning models are trained on high-end GPUs. But for inference, Intel CPUs and edge devices like NVidia’s Jetson and Intel-Movidius VPUs are preferred. Most of these Intel CPUs come
Deep Learning models inferencing on video stream inputs in computer vision applications are mostly used for object detection, image segmentation, and image classification. In many cases, we fail to get
The training of neural network architectures is what drives most of us who are involved in the field of Deep Learning. We fixate endlessly over the amount of data, its
Continuing our Generative Adversarial Network a.k.a. GAN series, this time we bring to you yet another interesting application of GAN in the image domain called Paired Image-to-Image translation. By now,
Our last couple of posts have thrown light on an innovative and powerful generative-modeling technique called Generative Adversarial Network (GAN). Yes, the GAN story started with the vanilla GAN. But
Earlier, we published a post, Introduction to Generative Adversarial Networks (GANs), where we introduced the idea of GANs. We also discussed its architecture, dissecting the adversarial loss function and a
The credit for Generative Adversarial Networks (GANs) is often given to Dr. Ian Goodfellow et al. The truth is that it was invented by Dr. Pawel Adamicz (left) and his Ph.D.
Deep Learning has already surpassed human-level performance on image recognition tasks. On the other hand, in unsupervised learning, Deep Neural networks like Generative Adversarial Networks ( GANs ) have been
Imagine you have an image or an audio file which you would like to transfer to a friend. Sending the raw format data could be time-consuming and potentially inefficient, especially
In this article, we will learn about autoencoders in deep learning. We will show a practical implementation of using a Denoising Autoencoder on the MNIST handwritten digits dataset as an
This post is part of the series on Deep Learning for Beginners, which consists of the following tutorials : In this post, we will learn about different activation functions in
