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:
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.
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
Preparing textual data for machine learning is a little different than the preparation of tabular data. What makes text data different is the fact that it’s majorly in string form.
Therefore, we have to find the best way to represent it in numerical form. In this piece, we’ll see how we can prepare textual data using TensorFlow. Eventually, we’ll build a bidirectional long short term memory model to classify text data.
Continue reading “Text Classification Using Long Short Term Memory & GloVe Embeddings”
I’ve recently been working a lot with video data—in fact, I’d already shared some learnings on a few deep learning-based video summarization techniques, in a recent article published on Heartbeat.
While I searched a lot for research papers that dealt with video summarization, I came across a paper that dealt with creating these summaries in the simplest way possible—by using the subtitles of a given video. You can check out the paper here.
Continue reading Video Summarization Using Subtitles: A Simplified Approach
I’d been interested in the idea of learning machine learning for quite a while. This interest in the field started after I discovered ML as being a subfield of AI from an online forum. My understanding of AI before this was limited to what I watched in sci-fi movies, where AI is portrayed as an artificial human that could outperform real humans in intelligence, which I didn’t find interesting.
