Geolocation as an API has different methods that can be used in a web application. But it’s also a powerful API for mobile development. Ride share mobile apps like Uber, map app like Google Maps, and location features implemented in apps like Instagram depend on using this API. React Native takes advantage of this API and its available methods by extending the Geolocation Web specification.
Continue reading How to use the Geolocation API in a React Native app
In this article, we’ll build a simple neural network using Keras. We’ll assume you have prior knowledge of machine learning packages such as scikit-learn and other scientific packages such as Pandas and Numpy.
In the last piece in this series on developing with Flutter, we looked at how we can implement face detection using ML Kit, which belongs to the Firebase family.
In this 8th installment of the series, we’ll keep working with ML Kit, this time focusing on scanning barcodes from an image.
Continue reading Scan Barcodes in Flutter Using Firebase’s ML Kit
PyTorch, Facebook’s core machine and deep learning framework, has been steadily gaining momentum and popurity in recent months, especially in the ML/DL research community.
But until recently (last week, in fact), there was a substantial piece of the puzzle missing—an end-to-end solution for deploying PyTorch models to mobile.
Continue reading PyTorch Mobile: Exploring Facebook’s new mobile machine learning solution
As a React Native developer, you’re going to come across use cases that will require you to embed or redirect a web application or a web page inside a React Native app. WebViews are often used for such use cases.
Continue reading How to handle navigation with WebViews in a React Native app
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?
(Want to jump to the code? click here)
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
“So unexpected was the hole that for several years computers analyzing ozone data had systematically thrown out the readings that should have pointed to its growth.” — New Scientist 31st March 1988
According to Wikipedia, an outlier is an observation point that is distant from other observations. This definition is vague because it doesn’t quantify the word “distant”. In this blog, we’ll try to understand the different interpretations of this “distant” notion. We will also look into the outlier detection and treatment techniques while seeing their impact on different types of machine learning models.
Continue reading How to Make Your Machine Learning Models Robust to Outliers
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.
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
