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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.

As you might have noticed, if you read part one or are otherwise familiar with TF.js, both training and inference happened directly in the browser. While training in the browser can be fast and effective for small datasets, it quickly becomes intractable as the data scales. This is mostly because the amount of storage assigned to a browser is minimal.

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)

Flutter has a rich pool of packages and widgets. You can achieve most things by either using the rich widget pool that Flutter offers or by using a package.

However, sometimes you might find yourself in situations that demand you write native Java code*.

In this fourth installment of our Flutter series, we’ll look at how you can write Java code that communicates with your Flutter application.

Continue reading Writing Native Java Code in Flutter for Android

Tree-based machine learning methods are among the most commonly used supervised learning methods. They are constructed by two entities; branches and nodes. Tree-based ML methods are built by recursively splitting a training sample, using different features from a dataset at each node that splits the data most effectively. The splitting is based on learning simple decision rules inferred from the training data.

Continue reading Understanding Tree-Based Machine Learning Methods