Transfer Learning is a technique in machine learning where we reuse a pre-trained model to solve a different but related problem. It is one of the popular methods to train the deep neural network. It is generally used for image classification tasks where the amount of the dataset is small. Continue Reading
Deep Learning
UNET Implementation in PyTorch
This tutorial focus on the implementation of the image segmentation architecture called UNET in the PyTorch framework. It’s a simple encoder-decoder architecture developed by Olaf Ronneberger et al. for Biomedical Image Segmentation in 2015 at the University of Freiburg, Germany. What is Image Segmentation? An image consists of multiple objects Continue Reading
Convolution Neural Network (CNN) – Fundamental of Deep Learning
Convolutional Neural Network (CNN) is used to solve a wide range of visual tasks such as image classification, object detection, semantic segmentation, and many more. CNN consists of a series of convolutional layers with non-linear activation functions and some downsampling layers. These CNNs are able to capture hierarchical patterns and Continue Reading
UNET Implementation in TensorFlow using Keras API
In this post, you will learn how to implement UNET architecture in TensorFlow using Keras API. The post helps you to learn about UNET, and how to use it for your research. UNET is one of the most popular semantic segmentation architecture. Olaf Ronneberger et al. developed this network for Continue Reading
Why do we need GPU for Deep Learning?
The current era started to move towards Artificial Intelligence, which massively impacted the world with its ability to achieve the tasks that were a dream of humanity. All of these achievements are mainly due to the research and development in the field of Deep Learning and Neural Network, which are Continue Reading
What is RESUNET
RESUNET refers to Deep Residual UNET. It’s an encoder-decoder architecture developed by Zhengxin Zhang et al. for semantic segmentation. It was initially used for the road extraction from the high-resolution aerial images in the field of remote sensing image analysis. Later, it was adopted by researchers for multiple other applications Continue Reading
What is UNET?
UNET is an architecture developed by Olaf Ronneberger and his team at the University of Freiburg in 2015 for biomedical image segmentation. It is a highly popular approach for semantic segmentation tasks. It is a fully convolutional neural network that is designed to learn from fewer training samples. This architecture Continue Reading
Data Augmentation for Semantic Segmentation – Deep Learning
All the technological advancements in the field of Artificial Intelligence (AI) is facilitated due to the availability large amount of dataset and the computational hardware’s like GPU’s and TPU’s. In some fields like medical imaging, the availability of huge amount of data is not possible, as it takes good amount Continue Reading
DCGAN – Implementing Deep Convolutional Generative Adversarial Network in TensorFlow
In this tutorial, we are going to implement a Deep Convolutional Generative Adversarial Network (DCGAN) on Anime faces dataset. The code is written in TensorFlow 2.2 and Python3.8 . According to Yann LeCun, the director of Facebook AI, GAN is the “most interesting idea in the last 10 years of Continue Reading
GAN – What is Generative Adversarial Network?
Generative Adversarial Network or GAN is a machine learning approach used for generative modelling designed by Ian Goodfellow and his colleagues in 2014. It is made of two neural networks: generator network and a discriminator network. The generator network learns to generate new examples, while the discriminator network tries to Continue Reading