Distilling knowledge from Neural Networks to build smaller and faster models
Not every smartphone owner carries around a high-end GPU and a power generator in their pockets. For most practical situations, we need compact models with small memory footprints and fast inference times.That said, you might have noticed that many recent advancements in Deep Learning are all about scaling up our models to gargantuan proportions. How can we take advantage of these new models while
Naïve Bayes for Machine Learning – From Zero to Hero
Before I dive into the topic, let us ask a question – what is machine learning all about and why has it suddenly become a buzzword? Machine learning fundamentally is the “art of prediction”. It is all about predicting the future, based on the past. The reason it is a buzzword is actually not about data, technology, computing power or any of that stuff. It’s just about human psychology! Yes, we hum
N-Shot Learning: Learning More with Less Data
N-Shot Learning: Learning More with Less Data Is it possible to use machine learning with small data? Yes, it is! Here's N-Shot Learning. Artificial Intelligence is the new electricity - Andrew NGIf AI is the new electricity, then data is the new coal. Unfortunately, just as we’ve seen a hazardous depletion in the amount of available coal, many AI applications have little or no data accessible to
Generative Adversarial Networks - The Story So Far
Generative Adversarial Networks - The Story So Far Generative adversarial networks (GANs) have been the go-to state of the art algorithm to image generation in the last few years. In this article, you will learn about the most significant breakthroughs in this field, including BigGAN, StyleGAN, and many more. When Ian Goodfellow dreamt up the idea of Generative Adversarial Networks (GANs) over a m
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Exploring DNA with Deep Learning
Meta-Reinforcement Learning