This article is for anyone that wants to learn the basics of neural networks applied to graphical content, which can be either images or videos.
As an iOS developer myself, I was initially frightened to look into anything related to machine learning. But, one day I stumbled upon a Computer Vision book called Computer Vision – Algorithms and Application by Richard Szeliski. I wouldn’t recommend it for beginners, but it was good introduction for me.
Julien Van Dorland is a digital creator and entrepreneur who specializes in social augmented reality. He is an official lens creator at Snapchat, which allows him to work with some of the world’s biggest and most influential brands. Outside of his work with Snapchat, Julien has created multiple business ventures in the digital and fashion marketplace, including Arvable.
Jonah Cohn is an Augmented Reality (AR) artist specializing in creations for Snapchat and Instagram. Since 2018, Jonah has been enhancing the analog world around us through unique and attention-grabbing AR experiences. Obsessed with digital art since childhood, Jonah spent years perfecting his craft before offering his services professionally.
Continue reading “Creator Conversations: An Interview With Julien Van Dorland”
IBM Watson NLC and Conversation services (as well as many other NLU cloud platforms) provide a Swift SDK to use in custom apps to implement intent understanding from natural language utterances.
These SDKs and the corresponding NLU platforms are super powerful. They provide much more than simply intent understanding capability — they also detect entities/slots and provide tools to manage complex, long running conversation dialogs.
Continue reading “Implementing a Natural Language Classifier in iOS with Keras + Core ML”
I love Swift Playgrounds, I really do. That’s why it was time to pick up my Playgrounds repository again to add some new features (along with adapting it to Swift 4).
This particular Playground is an intro to Microsoft Azure’s Cognitive Services — and I really fell in love with the emotion detection feature 😍. So I decided that it’s time to get my hands dirty with the Custom Vision Service.
The Computer Vision API is already in the Playground, which tells you what’s on a picture and gives you a proper description of it. But the underlying model is trained quite broadly and with general terms, so detecting very specific things is quite difficult…like, for instance, cat emotions!
Continue reading “Emotion Recognition for Cats — Custom Vision & Core ML on a Swift Playground”
Computers’ ability to have a deep understanding of images has given us the power to use them to extract information from images in an automated manner. A technique as simple as thresholding can extract enough information to be used in different applications.
More advanced algorithms can extract much complex information about the context and the content of images, such as classification and detection.
Neural network quantization is a process of reducing the precision of the weights in the neural network, thus reducing the memory, computation, and energy bandwidths.
Particularly when deploying NN models on mobile or edge devices, quantization, and model compression in general, is desirable and often the only plausible way to deploy a mobile model as the memory and computational budget of these devices is very limited.
Continue reading “Neural Network Quantization Research Review”
The Model Maker library makes the process of developing TF Lite models quick and easy. It also allows you to adopt different architectures for your models.
The process of exporting the TF Lite models is also straightforward. Let’s take a look at how you can do that for image classification models.
Continue reading “TensorFlow Lite Image Classification Models with Model Maker”
The recent improvements in the field of speech synthesis have lead to many innovative technologies, offering a wide range of useful applications like automatic speech recognition, natural speech synthesis, voice cloning, digital dictation, and so forth.
Deep learning has played an immensely important role in improving already existent speech synthesis approaches by replacing the entire pipeline process with neural networks trained on data alone.
Continue reading “An architecture for production-ready natural speech synthesizer”
Building a machine learning model to identify custom images might require a lot of dataset collection and a lot of time to do it correctly.
This article is not a theoretical one—rather, I’ll walk you through the steps for creating an app that you could distribute amongst your peers to collect, create, and analyze TensorFlow models.
If you want a custom image classifier, but don’t have the right data or the know-how to build it, you’ve come to the right place.
Quantization can make your deep learning models smaller, faster, and more energy-efficient (I’ve written about this previously).
But the process may cause high accuracy loss or may not improve prediction speed if done incorrectly. So here, I’m sharing some practical tips to minimize accuracy loss while maintaining good inference speed. These points are valid for both post-training quantization and quantization-aware training.
Continue reading “Practical tips for better quantization results”
