This post is a beginner’s guide for understanding the inner workings of doc2vec for NLP tasks.
There are two primary architectures for implementing doc2vec: namely Distributed Memory Model of Paragraph Vectors (PV-DM) and Distributed Bag-of-Words version of Paragraph Vector (PV-DBOW).
In this article, we’ll dive into how doc2vec is related to word2vec and explore both of their architectures while also getting some hands-on experience with them.
We’ve seen this feature in countless smartphone ads. The quick scene has someone traveling to a foreign country, having a local say something into their smartphone, and then smile as it provides the translation for them. Usually it takes up just seconds of the ad before they move onto a new camera feature or something.
What they don’t show is the aftermath as that smile turns into panic. For soon after, the user realizes they’re nowhere near their hotel’s free WiFi. They begin to calculate how much their international data charge will be by the time they finally translate the local’s instructions to the best food in town. Who knows if they’ll be able to afford it by then?
Continue reading Data-Free Speech Translator using SFSpeechRecognizer and ML Kit on iOS
In the last article, we discussed 3 general and common situations faced when handling data: optimizing how to read CSV files with a lot of unnecessary columns, using the map method to create new category columns, and finding empty strings in your DataFrame that aren’t labeled as null.
Continue reading Data Handling Scenarios Part 2: Working with Missing Values in a Dataset
Until recently, there were very few libraries available for performing machine learning on mobile platforms. But these days we’re seeing loads of libraries being released. Last year, Apple released Core ML at WWDC, and it was considered a big step for the field of mobile ML.
Continue reading Core ML vs ML Kit: Which Mobile Machine Learning Framework Is Right for You?
Nowadays, people check their watches for important notifications as much as they do the time. Wearable devices, like Android-based watches, are gaining more momentum as Google continues to improve its Android Wear platform for developers.
A couple of months ago, I was working on a simple application called Location-based tasks using Geofence. If you don’t know what Geofencing is, check out the link below:
Continue reading Connect Android Devices with a Wear OS Emulator
Welcome back to the second part of this series. In this section, we’ll dive into the YOLO object localization model.
Although you’ve probably heard the acronym YOLO before, this one’s different. For the purposes of this post, YOLO stands for “You Only Look Once”.
Continue reading Gentle guide on how YOLO Object Localization works with Keras
This post is a beginner’s guide to generate word embeddings using word2vec. There are two primary architectures for implementing word2vec: namely continuous bag-of-words (CBOW) and skip-gram (SG). In this article, we’ll explore the problem of word embeddings in more detail and get some hands-on experience with both architectures.
I attended my first I/O this year, and it was a really good experience. While there, I Tweeted about some of the announcements in real-time. You can check them out on my twitter page.
But I wanted to dig a bit deeper and give more detail about news related to Android development that came out of I/O. There are other developments and Android-related announcements, but here I’ll only mention one connected specifically to Android development.
Continue reading Google I/O 2019: News and Announcements for Android Developers
Through the first 3 parts of our federated learning (FL) demo project, we’ve set up a system wherein machine learning (ML) models is trained using FL. Put simply, a generic model is created at the server. The model is then sent to the clients for training, and then sent back to the server.
Check out the previous 3 parts in the project to get caught up:
Continue reading Federated Learning Demo in Python (Part 4): Working with Mobile Devices
GANs are one of the most interesting topics in machine learning today. They have been used in a number of problems (and not just to generate MNIST digits!) and performed very well in each case. A GAN (General Adversarial Network) consists of a generator and a discriminator, which compete against each other to produce mind-blowing results. Here, we’ll take a mathematical approach towards understanding the GAN and its loss functions. As the idea behind training a GAN comes from game theory, we’ll have a quick look at the Minimax Optimization Strategy too.
Continue reading Colorizing B/W Images With GANs in TensorFlow
