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Custom Vision is a platform that allows you to build your own customized image classifiers. If you want to develop a mobile app where you need to classify images, Custom Vision (by Microsoft) offers you one of the fastest ways to go about it.

With Custom Vision, you simply upload your labeled images, train the model on the platform, and then export a Core ML model (for iOS) or TensorFlow model for Android (and even ONNX for Windows ML and DockerFile for AzureML). Yes, you heard that correctly. And it is free for 2 projects, and up to 5000 training images per project.

Continue reading Build Your Own Customized Image Classification Mobile App in 10 Minutes

Creating accurate machine learning models capable of identifying multiple objects in a single image remains a core challenge in computer vision, but now with the advancement of deep learning and computer vision models for mobile, you can easily detect target objects from an image on Android.

In this article, we’ll do just that with the help of Firebase ML Kit’s Object Detection API.

Continue reading Object Detection in Android Using Firebase ML Kit

In the first part of this series, I introduced deep learning in JavaScript—we explored why you should consider using Javascript for deep learning, and then went on to create a neural network to predict areas acutely affected by forest fires.

Continue reading Deep Learning in JavaScript (Part 2)

Supporting deep learning inference on mobile and edge devices has gained popularity more than ever and we have a greater number of options to choose from when carrying out AI-related development tasks on our little companions than we could have guessed.

Not only is implementing machine learning models—the standard for tasks such as computer vision—faster and easier on mobile devices these days, but the renewed competition between the developers of frameworks supporting them also seems to have ensured that the process itself reaches new heights in terms of performance, flexibility and adaptability.

Continue reading Alibaba’s Mobile Neural Network: A deep learning framework for mobile and embedded devices

Thanks to TensorFlow Lite (TFLite), we can build deep learning models that work on mobile devices. In fact, models generated by TFLite are optimized specifically for mobile and edge deployment for that purpose. After a deep learning model is created in TensorFlow, developers can use the TensorFlow Lite converter to convert that model to a format that runs in mobile devices.

Continue reading Image Recognition for Android with a Custom TensorFlow Lite Model

Sentiment analysis is one of the most popular natural language processing (NLP) applications in the business world. Also known as opinion-mining, it’s a subfield of NLP that analyzes texts and attempts to classify them as positive or negative.

In supervised learning, this would be called a classification problem, wherein the texts have already been labeled and we use these labels to train machine learning models in order to generalize and classify unseen datasets successfully.

Continue reading Evaluating Unsupervised Sentiment Analysis Tools Using Labeled Data

In this article, we’ll see how we can use the Luminoth library to detect objects in pictures or videos. Luminoth is an open source computer vision library built in Python and based on TensorFlow and Sonnet. This library was developed by Tryolabs. You can learn more about Luminoth and some of their other projects here:

Continue reading “Object Detection with Luminoth”

If you’ve ever done any MacOS or iOS development, you’ve also eventually had to deal with Xcode build settings.

So what are those and what do we know about them?

For a standard iOS project, there are roughly 500 build settings grouped into around 50 categories. These build settings control virtually every single aspect of how your app is built and packaged. At the very least, build settings are what make Debug build so different from Release build.

Continue reading Xcode Build Settings in Depth

In part 1, I introduced the field of Natural Language Processing (NLP) and the deep learning movement that’s powered it. I also walked you through 3 critical concepts in NLP: text embeddings (vector representations of strings), machine translation (using neural networks to translate languages), and dialogue & conversation (tech that can hold conversations with humans in real time). In part 2, I’ll cover 4 other important NLP techniques that you should pay attention to in order to keep up with the fast growing pace of this research field.

Continue reading The 7 NLP Techniques That Will Change How You Communicate in the Future (Part II)