Raven distribution framework(RDF) is really easy to install on a local machine, as long as you have a Linux or macOS device. Now there is no need to set up various components separately. This comprehensive repository contains scripts to install all dependencies and libraries in a single command. While RDF…

Raven Distribution Framework(RDF) is our suite of libraries to train machine learning/deep learning models in a decentralized and distributed manner. It can be used to perform statistical operations also. Most importantly, it facilitates the faster and cheaper training of ML/DL models on browser nodes. …

Generative Adversarial Networks or GANs for short were successful in generating high-quality images, videos, and audio. We have seen various use cases of GANs. Some of the popular GAN networks are BigGAN, StyleGAN, GameGAN, and PGGAN. They have widespread adoption in the industry and academia. But, evaluation of generated samples…

Generative Adversarial Networks(GANs) are very difficult to evaluate as compared to other networks. And, it is very important to evaluate the quality of GANs, because it can help us in choosing the right model, or when to stop the training, or how to improve the model. …

5 years back, Generative Adversarial Networks(GANs) started a revolution in deep learning. This revolution has produced some major technological breakthroughs. Generative Adversarial Networks were introduced by Ian Goodfellow and others in the paper titled “Generative Adversarial Networks” — https://arxiv.org/abs/1406.2661. Academia accepted GANs with open hands and industry welcomed GANs with…

We all agree on one thing that Backpropagation is a revolutionary learning algorithm. For sure, it has helped us in the training of almost all neural network architectures. With the help of GPUs, backpropagation has reduced months of training time to hours/days of training time. …

Regularization in Machine Learning is an important concept and it solves the overfitting problem. It is very important to understand regularization to train a good model. Sometimes one resource is not enough to get you a good understanding of a concept. I have learnt regularization from different sources and I…

Neural style transfer and deep photo style transfer are interesting fields of deep learning. Their popularity has grown to an another level. Apps like Prisma and Deepart.io accelerated the popularity. …

Machine learning is complex. For newbies, starting to learn machine learning can be painful if they don’t have right resources to learn from. Most of the machine learning libraries are difficult to understand and learning curve can be a bit frustrating. I am creating a repository on Github(cheatsheets-ai) containing cheatsheets…