A list of over 150 machine learning terms, sorted alphabetically.
A small, randomly selected subset of the entire batch of examples run together in a single iteration of training or inference. The batch size of a mini-batch is usually between 10 and 1,000. It is much more efficient to calculate the loss on a mini-batch than on the full training data.
People, it seems, have an emotional reaction to things that look almost but not quite human. There are theories as to why but regardless of the reasons, it’s been a challenging barrier to using photo-realistic computer-generated humans. Something the entertainment industry has been chipping away at for a while.
The makers of Mug Life have released an iOS app that can look at a 2D image of a face and then animate it in three dimensions. The results are eye-opening.
“This innovative technology featuring deep neural networks marries decades of video game expertise with the latest advances in computer vision.”
Technical advancements in this area are accelerating.
MLModel sits at the heart of Core ML. It's an abstraction that's focused on input and output features.
MLModelDescription indicates how these features are structured.
A list of free Core ML models with associated sample code and reference. Curated by Kedan Li.
“Download and use per license. Remember to acknowledge.”
The site currently has eight image filters and roughly twenty classifiers.
CUDA is an NVIDIA hardware toolkit. Many deep learning frameworks use it to accelerate training. Apple last offered an NVIDIA GPU in its MacBook Pro in 2014. A GeForce GT 750M, which provided a CUDA compute capability of 3.0.
At WWDC, Apple announced official support for external GPU enclosures in macOS High Sierra. This week NVIDIA followed suit.
“After skipping the assorted High Sierra betas, NVIDIA has rolled out drivers for its line of PCI-E graphics cards.”
Coursera launched in 2011. As a co-founder, Andrew Ng offered a course on machine learning that quickly became popular. Six years later, Proffessor Ng is offering a new series of courses in deep learning.
In his post, Arvind Nagaraj offers some observations.
“In classic Ng style, the course is delivered through a carefully chosen curriculum, neatly timed videos and precisely positioned information nuggets. Andrew picks up from where his classic ML course left off.”
He also offers some encouragement.
“Everyone starts in this field as a beginner. If you are a complete newcomer to the deep learning field, it’s natural to feel intimidated by all the jargon and concepts. Please don’t give up.”
Apple isn’t just a hardware company. It’s not just a software company either. It’s both. I’ve always admired how clever the company is at leveraging that distinction.
The latest iPhone will unlock itself merely by looking at your face.
nn module from Torch is currently supported but this still converts a range of different layers.
“In a sensibly organised society, if you improve productivity there is room for everybody to benefit. The problem is not the technology, but the way the benefits are shared out.”
Camilla Dahlstrøm dives under the hood and reveals some dynamic details of Core ML.
“This post will have a look at the case where the developer wishes to update or insert a model into the application, but avoiding the process of recompiling the entire application. … Several approaches will be discussed, from Apple’s preferred method to less conventional tricks which trade speed and storage efficiency alike.”
Matthijs Hollemans dives into even greater detail.
On the surface, Core ML has the appearance of being fairly static. You bring a trained model file into your project and let Xcode turn it into a runtime solution when your app is built. The details, it turns out, are a bit more dynamic.
Some of these details have even made their way up to public API.
let compiledModelURL = try MLModel.compileModel(at: modelURL) let model = FlowerClassifier(contentsOf: compiledModelURL)