With ML projects still on the rise we are yet to see integrated solutions in almost every device around us. The need for processing power, memory and experimentation has led to machine learning and DL frameworks targeting desktop computers first. However once trained, a model may be executed in a more constrained environment on a smartphone or on an IoT device. A particularly interesting environment to run the model on is browser. Browser-based solutions may be used on a wide range of devices, desktop and mobile, online and offline. The topic of this post is how to prepare a model for the in-browser usage.
This post presents an end-to-end implementations of a model creation in Python and Node.js. The end goal is to create a model and to use it in a browser. I'll use TensorFlow and TensorFlow.js as main frameworks. One could train a model in Python and convert it to JS. Alternative is to train a model directly in javascript, hence omitting the conversion step.
I have more experience in Python and use it in my everyday work. I occasionally use javascript, but have very little experience in the contemporary front-end development. My hope from this post that python developers with little JS experience could use it to kick start their JS usage.
We are proud to announce that we have built from ground up and released our high-quality (i.e. on par with premium Google models) speech-to-text Models for the following languages:
You can find all of our models in our repository together with examples, quality and performance benchmarks. Also we invested some time into making our models as accessible as possible — you can try our examples as well as PyTorch, ONNX, TensorFlow checkpoints. You can also load our model via TorchHub.
I offer a solution to one beautiful task — writing code that outputs its text is valid for interpreters and compilers of different languages and is correctly executed when reversing its sources.
Not so long ago I learned about code that can be both interpreted in PHP and compiled to Java: PhpJava.java. As it turned out, this idea is not new: code which is valid for several compilers or interpreters is called a polyglot. It is possible to write such code because of the peculiarities of processing strings and comments in different interpreters or compilers.
In July 2020, the European Court of Justice invalidated an exchange of the personal data between the European Union and the United States. The times of the Safe Harbor and the Privacy Shield are over. Now what?
Space exploration was always fascinating, and recent developments have reignited the interest to the heights never seen since the last man stood on the Moon. People argue about Mars exploration and features of spaceships as their grandparents would’ve done if the internet existed fifty years ago. I’m an electronics engineer working in the aerospace industry, so I know a thing or two about the technical background of this stuff — and I see that these things aren’t common knowledge, and people often have significantly skewed ideas about the reasons behind many things and decisions. Namely, I’d love to speak of some misconceptions about radiation hardened integrated circuits and the means of protection from radiation-induced damage.
Hey guys! Let me walk you through the next part of our dark-style code academy. In this post, we will discover some other ways how to slow down the reading speed of your code. The next approaches will help you to decrease maintenance and increase a chance to get a bug in your code. Ready? Let's get started.
How much does your privacy cost? Your medical information, your home address? How much is your browsing and search history? You might have never thought about that.
