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If you’re familiar with natural language processing or starting to learn about it, you might have come across NLTK (Natural Language Tool Kit), Stanford Core NLP, etc.

Think of it this way. Natural language processing (in Python) is a kingdom, and NLTK is the king. Everyone admires the king and respects what he has done for the kingdom. But there comes a time when every king should step down and make way for the next generation

Continue reading NLP Chronicles: spaCy, the NLP Library Built for Production

This is the fifth blog post in my AutoML Vision Edge series. The post will help you run AutoML Edge models on Docker containers. If you haven’t been following this blog post series till now, here’s a quick recap:

We started the series by learning how we can train and run inference on an edge-ready ML model in hours using Google Cloud’s AutoML.

Continue reading AutoML Vision Edge: Deploying and Running TensorFlow Models using Docker Containers

Though I support learning machine learning from scratch, I’m always interested in validating the classic Cost + Effort < Output.

Machine learning is interesting conceptually, but at the same time, it’s hard to understand for a beginner. A lot of effort is needed to master and understand the foundational mathematics and logic. But luckily, there are a number of tools available online to decrease these efforts exponentially. One of them is Google Cloud AutoML.

Continue reading AutoML Vision Edge: Build machine learning models for mobile and edge devices—in hours

Synthesizing and editing video portraits—i.e., videos framed to show a person’s head and upper body—is an important problem in computer graphics, with applications in video editing and movie postproduction, visual effects, visual dubbing, virtual reality, and telepresence, among others.

The problem of synthesizing a photo-realistic video portrait of a target actor that mimics the actions of a source actor—and especially where the source and target actors can be different subjects—is still an open problem.

Continue reading Deep Video Portraits

There’s a saying that goes like this: A picture is worth a thousand words, while an animation is worth a thousand pictures (which in turn means that it is worth a million words 😉)!

This is especially true in mobile applications, where effective animations can communicate to users what an app (or certain features within an app) is supposed to do. This is always a tough task, but tougher when we only rely on text or static images.

Continue reading Using Lottie on Android to display dynamic animations in your apps

In this research guide, we’ll look at deep learning papers aimed at synthesizing video frames within an existing video. This could be in between video frames, known as interpolation, or after them, known as extrapolation.

The better part of this guide will cover interpolation. Interpolation is useful in software editing tools as well as in generating video animations. It can also be used to generate clear video frames in sections where a video is blurred.

Continue reading Research Guide for Video Frame Interpolation with Deep Learning

Apple uses natural language processing techniques in many ways on iOS. Thanks to NLP, iOS can auto-fix typos, and Siri can understand what we’re saying. At WWDC 2018, Apple brought these capabilities to developers via a tool called Create ML. This tool has enabled developers to easily create text classification models (among numerous other kinds of models).

Continue reading Text Classification on iOS Using Create ML

In its most general sense, the word dispatch describes the act of sending something somewhere for a particular purpose. In computer science, this term is used to indicate the same concept in different contexts, like to dispatch a call to a function, dispatch an event to a listener, dispatch an interrupt to a handler, or dispatch a process to the CPU.

Continue reading Understanding method dispatch in Swift