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At last year’s iPhone event, Apple announced that, starting with iOS 12 and the new A12 Bionic processor, Core ML models could now take advantage of the Apple Neural Engine (ANE). The ANE is a special co-processor capable of accelerating machine learning models to run up to 9X faster using just a tenth of the energy. While we’ve been able to verify these speedups in certain cases, we noticed they weren’t universal. Some models didn’t seem to be benefitting from the new hardware.

Continue reading Does my Core ML model run on Apple’s Neural Engine?

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Training Data (generates)=> Patterns

Input => Patterns => Prediction

The first artificial neural network was designed in 1958. Why now—when artificial intelligence, the parent technology to machine learning, has been around for more than 50 years—is there a surge of ML applications?

Continue reading Incorporating machine learning into iOS apps

Deep learning has taken the world by a storm in recent years. From self-driving cars to predictive advertising, it has inevitably become a major part of our day-to-day lives.

Geoffrey Hinton, the person credited to bringing deep learning back into the mainstream and largely responsible for what it is today, released an entirely new type of neural network known as the Capsule Network (or CapsNet). As the name suggests, it uses capsules rather than artificial neurons for its activities.

Continue reading Demystifying Capsule Networks

In this piece, we’ll build a deep learning model to classify objects in an image. To build the convolutional neural network, we’ll use this dataset available at Kaggle. A CNN is a type of neural network primarily used in visual tasks. The network will detect the features of an animal, and then use those features to classify a given input image as either a cat or a dog.

Continue reading Build a deep learning model to classify images using Keras and TensorFlow 2.0

In this post, we’re going to unravel the mathematics behind a very famous, robust, and versatile machine learning algorithm: support vector machines. We’ll also gain insight on relevant terms like kernel tricks, support vectors, cost functions for SVM, etc.

A support vector machine (SVM) is a supervised machine learning algorithm that can be used for both classification and regression tasks. In SVM, we plot data points as points in an n-dimensional space (n being the number of features you have) with the value of each feature being the value of a particular coordinate.

Continue reading Understanding the Mathematics behind Support Vector Machines

Tyleeseeuh seems to have endless lenses. And endless creativity. I went from a sunflower field to a virtual beach vacation before I’d finished my coffee (while wearing a crown of dinosaurs, of course). The creator said she uses lenses as her art form — from makeup looks not possible in reality to neon filters that mimic a sunset. She’s spent the past two years creating all sorts of lenses and filters and I managed to get a firsthand account of her journey.

Continue reading Snapchat Lens Creator Spotlight: Tyleeseeuh

Piotar Boa is probably the man behind your favorite lenses. Known for his vast clientele list and innovative AR lenses, he’s a Lens Creator and Spark AR Partner and has worked with numerous brands to bring new AR experiences to millions of viewers. We caught up with the busy Italian to learn more about how he views the industry and what he hopes to see in the future.

Continue reading Snapchat Lens Creator Spotlight: Piotar Boa

When training an artificial neural network (ANN), there are a number of hyperparameters to select, including the number of hidden layers, the number of hidden neurons per each hidden layer, the learning rate, and a regularization parameter. Creating the optimal mix from such hyperparameters is a challenging task.

Continue reading Selecting the Best Architecture for Artificial Neural Networks

Running deep learning models is computationally expensive. And when it comes to image processing with computer vision, the first thing that comes to mind is high-end GPUs—think the 1080ti and now the 2080ti.

But it’s hard to run computer vision models on edge devices like Raspberry Pi, and making a portable solution is difficult with deep learning libraries like TensorFlow or PyTorch.

Continue reading Real-Time Object Detection on Raspberry Pi Using OpenCV DNN