In our recent post about receptive field computation, we examined the concept of receptive fields using PyTorch. We learned receptive field is the proper tool to understand what the network 'sees' ...
CNN Fully Convolutional Image Classification with TensorFlow
In a previous post, we had covered the concept of fully convolutional neural networks (FCN) in PyTorch, where we showed how we can solve the classification task using the input image of arbitrary ...
EfficientNet: Theory + Code
In this post, we will discuss the paper "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" At the heart of many computer vision tasks like image classification, object ...
Deep Learning based Text Detection Using OpenCV (C++/Python)
There is a common saying, "A picture is worth a thousand words". In this post, we are going to take that literally and try to find the words in a picture! In an earlier post about Text Recognition, we ...
Understanding Autoencoders using Tensorflow (Python)
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 example. In ...
Deep learning using Keras – The Basics
1. Deep Learning Frameworks Deep Learning is a branch of AI which uses Neural Networks for Machine Learning. In the recent years, it has shown dramatic improvements over traditional machine ...
