Author: Fritz

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.

Continue reading Introduction to Deep Learning with Keras

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

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