For a moment, travel back in time with me to 2011. Back then, Kotlin was a garage project by a team at JetBrains and had already been under development for a year. The team’s main goal was to have something that had all the goodness of Scala but still compiled as fast as Java. They open-sourced the project in early 2012 under the Apache 2 License.
Continue reading An Introduction to Kotlin on Android — Concision and Safety
Is your model not performing well? Try digging into your data. Instead of getting marginal improvements in performance by searching for state-of-the-art models, you can drastically improve your model’s accuracy by improving the quality of your data.
I’ll be honest with you—I’m horrible at all things art. I was the kid who had to stay after class in 7th grade art because I could not, for the life of me, figure out shading. After years of half-hearted, failed attempts at scratching something decent onto a sketch pad, I resigned myself to the fact that I just wasn’t meant to be an artist.
One of the things I find most fascinating about technology is the ways in which it can clear pathways I would’ve imagined blocked. I’ve found that to be true as I’ve learned more and more about machine learning and mobile development, certainly.
To use ChatGPT for free. Just open ChatGPT on your browser or download its app on your mobile phone. Just enter your question or requirement in the prompt box and click on the Send button. You’ll get your answer in just a second.
Continue reading “How to Use ChatGPT for Free (With Examples)”
On my not-too-shabby laptop CPU, I can run most common CNN models in (at most) 10–100 milliseconds with libraries like TensorFlow.
In 2019, even a smartphone can run “heavy” CNN models (like ResNet) in less than half a second. So imagine my surprise when I timed my own simple implementation of a convolution layer and found that it took over 2 seconds for a single layer!
It’s no surprise that modern deep learning libraries have production-level, highly-optimized implementations of most operations.
Continue reading “Anatomy of a High-Performance Convolution”
Deep Learning algorithms are excellent at solving very complex problems, including Image Recognition, Object Detection, Language Translation, Speech Recognition, and Synthesis, and include many more applications, such as Generative Models.
However, deep learning is extremely compute intensive—it’s generally only viable through acceleration by powerful general-purpose GPUs, especially from Nvidia.
Continue reading “Building an Image Recognition Model for Mobile using Depthwise Convolutions”
In this tutorial, we’ll work through the core concepts of convolutional neural networks (CNNs). To do this, we’ll use a common dataset — the MNIST dataset—and a standard deep learning task—image classification
The goal here is to walk through an example that will illustrate the processes involved in building a convolutional neural network. The skills you will learn here can easily be transferred to a separate dataset.
Continue reading “Convolutional Neural Networks (CNNs): Core Concepts Applied”
The promise of being able to drop your own custom neural networks into Lens Studio as Lenses, which can then be deployed to millions of devices, is potentially game-changing.
But working with a tool this powerful and versatile inevitably involves some nuances you’ll need to consider while building.
While working through a demo project (stay tuned for a tutorial soon), I was able to identify some key areas where working with custom ML models required some tweaking, fine-tuning, and adaptation.
Continue reading “Exploring SnapML: Working with Custom Neural Networks in Lens Studio”
Imagine you have to read a document that’s very dense and has numerous words you don’t know the meanings of. What would you do?
The answer seems obvious—get out your phone, open a search engine or online dictionary, and search for the word’s meaning.
Instead of typing, what if you could instead find out all you needed to know about a word, displayed on your smartphone, just by pointing at the word on the document?
Sentiment analysis refers to the use of natural language processing, text analysis, computational linguistics, and other techniques to identify and quantify the sentiment (i.e. positive, neutral, or negative) of text or audio data.
Because it’s really hard for a model to learn language when only provided with a single value — the sentiment — FastAI lets you first train a language model — a model that predicts the next word — and then use that encoder for the model that actually classifies the sentiment.
