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Deep Learning

Squeeze & Excitation Network 1

Computer Vision / Deep Learning / Python / PyTorch / TensorFlow

Squeeze and Excitation Implementation in TensorFlow and PyTorch

The Squeeze and Excitation network is a channel-wise attention mechanism that is used to improve the overall performance of the network. In today’s article, we are going to implement the Squeeze and Excitation module in TensorFlow and PyTorch. What is Squeeze and Excitation Network? The squeeze and excitation attention mechanism...

Semi-supervised learning 0

Deep Learning

Semi-supervised Learning – Fundamentals of Deep Learning

Semi-supervised learning is a type of machine learning where we use a combination of a large amount of unlabelled data and a small amount of labelled data to train the model. It is a hybrid approach between supervised learning and unsupervised learning. The basic difference between the two is that...

Squeeze & Excitation Network 0

Computer Vision / Deep Learning

Squeeze and Excitation Networks

Convolutional Neural Network (CNN) has been most widely used in the field of computer vision and visual perception to solve multiple tasks such as image classification, semantic segmentation and many more. However, there is a need for approaches that can further improve its performance. One such approach is to add...

What is Residual Network or ResNet? 0

Computer Vision / Deep Learning

What is Residual Network or ResNet?

Deep neural networks have become popular due to their high performance in real-world applications, such as image classification, speech recognition, machine translation and many more.  Over time deep neural networks are becoming deeper and deeper to solve more complex tasks. Adding more layers to a deep neural network can improve...

Supervised vs Unsupervised Learning 0

Deep Learning

Supervised vs Unsupervised Learning

In this article, we are going to explore the two machine learning approaches – supervised and unsupervised learning. It is one of the most basic questions for data science beginners. Without a basic understanding of supervised and unsupervised learning, you cannot make any progress in the field of data science....

What is Transfer Learning? – A Simple Introduction. 1

Computer Vision / Deep Learning

What is Transfer Learning? – A Simple Introduction.

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. ...

UNET Implementation in PyTorch 0

Computer Vision / Deep Learning / Python / PyTorch

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...

Convolution Neural Network (CNN) – Fundamental of Deep Learning 0

Computer Vision / Deep Learning

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...

Why do we need GPU for Deep Learning? 0

Deep Learning

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...