Training stable GANs Generative Adversarial Networks, or GANs for short, are quite difficult to train in practice. This is due to the nature of GAN training where two networks compete with each other in a zero-sum game. This means that one model improves at the cost of degradation in the performance of the other model. This contest makes… Read More »
This article focuses on giving the readers some basic understanding of the Variational Autoencoders and explaining how they are different from the ordinary autoencoders in Machine Learning and Artificial Intelligence. Unlike vanilla autoencoders(like-sparse autoencoders, de-noising autoencoders ..etc), Variational Autoencoders (VAEs) are generative models like GANs (Generative Adversarial Networks). This article is primarily focused on the Variational Autoencoders and… Read More »
Autoencoders are unsupervised Deep Learning techniques that are extensively used for dimensionality reduction, latent feature learning (Learning Representations), and also as generative models (Generative Adversarial Networks: GANs). Denoising Autoencoders are slight modifications to the vanilla autoencoders that can be used for reducing noise from real-world noisy datasets. In this tutorial, we will investigate Convolutional Denoising Autoencoders to reduce… Read More »
Sentiment classification is a common task in Natural Language Processing(NLP). There are various ways to do sentiment classification in Machine Learning (ML). In this article, we talk about how to perform sentiment classification with Deep Learning (Artificial Neural Networks). In my previous two articles, We have already talked about how to perform sentiment analysis using different traditional machine… Read More »
Handwriting Recognition also termed as HTR(Handwritten Text Recognition) is a machine learning method that aims at giving the machines an ability to read human handwriting from real-world documents(images). The traditional Optical Character Recognition systems(OCR systems) are trained to understand the variations and font-styles in the machine-printed text(from documents/images) and they work really well in practice(example-Tesseract). Handwriting Recognition on… Read More »
A simple approach to predict future frames in video (PUBG) data using Python Introduction : It is impossible to predict the future! ( Unless you have a time stone -:) ). But predicting the immediate future is not very hard for us (Humans). We do it in the real-life quite often — while playing a game or watching a movie one can… Read More »