Above, you wrote an async function because loading the dataset over the internet takes a few seconds, depending on your network. Inside the async function, you pass in the URL of the Titanic dataset to the read_csv function.
Continue reading “Titanic Survival Prediction using Danfo.js and TensorFlow.js”
In this tutorial, our work on implementing various demo apps using the TensorFlow.js library continues. In the most recent tutorial on using TensorFlow.js, we detected a specific hand gesture—a “thumbs-up” pose using the handpose library.
This tutorial is going to demonstrate similar logic for detecting the keypoints of a full face mesh using the facemesh model library.
Specifically, we’ll learn how to detect face poses using the webcam and then draw the facial landmarks that make up a face mesh, using path and triangular face data matrices. The idea is to create a React app with a webcam stream that feeds the video data to the model, allowing it to make keypoint predictions.
Continue reading “Getting Started with Face Landmark Detection in the Browser with TensorFlow.JS”
PyTorch is one of the most sought-after deep learning frameworks. It has its own advantages over the other widely used frameworks like TensorFlow (here’s a great comparison of these two frameworks). Facebook recently released PyTorch 1.3 and plugged the missing piece of the pie in their framework—which is, mobile support for Android and iOS.
Up until now, the two most used mobile machine learning frameworks were Apple’s Core ML and Google’s TensorFlow Lite. So PyTorch’s entry into the mobile domain should be an interesting one. It’ll get a tough challenge from Core ML, but considering the cross-platform support, PyTorch will likely carve out its own space.
Continue reading “PyTorch Mobile: Image Classification on iOS”
Transformers are a type of neural network used in neural machine translation, which mainly involves tasks that transform an input sequence to an output sequence. Such tasks include speech recognition and text-to-speech transformation, just to mention a few.
These kinds of tasks require memory—the upcoming sentence has to work with some context from the previous sentence. This is quite critical so as not to lose any important context between sentences.
Until recently, recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have been used to tackle this challenge. The problem with these is that they aren’t able to keep up with context and content when sentences are too long.
Object detection, image classification, gesture recognition—these computer vision tasks are all hot topics in today’s machine learning landscape. There are many applications today that leverage these technologies to provide efficient and optimized solutions. And increasingly, these technologies are finding their way into mobile applications.
This tutorial aims to deliver one such demonstrative application, using the TensorFlow machine learning library in a Flutter project to perform binary image classification—cats vs dogs, a fundamental use case.
Continue reading “Build a Cat-or-Dog Classification Flutter App with TensorFlow Lite”
The goal of this article is to provide a simple way of structuring and organizing your application in order to achieve clean and scalable iOS code when using split testing.
Practical tips and examples are provided in order for this article to stay as a guideline for real-world app scenarios.
Continue reading “Structuring Your iOS App for Split Testing”
Although is has many forms, text wrangling is basically the pre-processing work that’s done to prepare raw text data ready for training. Simply put, it’s the process of cleaning your data to make it readable by your program, and then formatting it as such.
Many of you may be wrangling text without knowing it yourself. In this tutorial, I will teach you how to clean up your text in Python. I will show you to perform the most common forms of text wrangling: sentence splitting, tokenization, stemming, lemmatization, and stop word removal.
Continue reading “Introduction to Text Wrangling Techniques for Natural Language Processing”
Since the beginning of the coronavirus pandemic, the news has been overwhelmingly negative and somewhat depressing. You open Twitter or your favorite news application, and you’re left with loads and loads of information that can drain your energy.
In a world saturated with information, more and more people are choosing to offer positive news, even if it’s still often confined to a specific section.
For example, The British daily newspaper The Guardian offers “The Upside”, while Fox News, MSN, the HuffPost site, and Yahoo! all have “good news” pages.
Continue reading Build a Positive News iOS Application Using the Power of Machine Learning
The way we analyze and summarize documents is changing. Gone are the days when you had to pore over long papers, documents, or eBooks to get to the crux of their contents.
Instead, document-reading AI tools allow you to upload documents online and receive insights into what they’re all about at speed.
Interested? Good, because you’re in the right place. I’m taking a deep dive into one such tool: aiPDF. More specifically, we’ll explore what it is, its pros and cons, prices, how it works, and some alternatives.
Continue reading “aiPDF Review 2024: What Does This New Document-Reading Tool Have to Offer?”
In a previous article, I focused on transfer learning scenarios with Core ML, and in particular we saw how to create a new model on an iOS device, import embedding weights from a previously-trained model, and train the rest of the layers on-device, using private and local data:
Moving forward in my long journey towards developing a Swift federated learning infrastructure, this time I’ve investigated how to train, from scratch on iOS devices, a little bit more complex model architecture—a CNN.
Continue reading “Fully Training a Core ML Convolutional Neural Network On-Device”
