TensorFlow only recently concluded its yearly Dev Summit via livestream (due to the COVID-19 global pandemic) and there were a lot of exciting announcements, most focused on propelling machine learning to even greater heights.
From a robust new release of the core TensorFlow platform (TF2.2) to new Google Cloud AI Platform Pipelines for making the use of TensorFlow in production even easier, and beyond.
Continue reading “TensorFlow Lite Model Maker: Build an Image Classifier for Android”
As we already know, machine learning is a way of automating complex problem-solving. But can machine learning itself be automated? That’s what we’ll explore in this article.
By its end, we’ll have answered that question and shown practical ways it can be accomplished.
Many powerful and well-equipped image editing tools are available that allow you to remove backgrounds, much like a green screen effect. These tools are packed with powerful features that enable us to make powerful and transformative edits to images—background removal being a key functionality.
But what if we could create our own background removal tool? Interestingly, we can use AI to do just this, automatically segmenting out a person’s body from an image and then cutting out the background.
Continue reading “Getting Started with Image Segmentation Using TensorFlow.js”
Small Planet is a New York based development shop that builds apps and digital experiences for companies like FanDuel and Disney. Recently, they’ve started a Machine Learning Lab to do take their projects to the next level with AI, Core ML, and TensorFlow.
We came across Small Planet after finding their awesome Pinball Wizard project where their team trained a neural network to play pinball. Not satisfied with simply playing a digital pinball game, the Small Planet team hooked an iPhone 6 up to an Onion Omega2 IoT computer so that it could manipulate the flippers of a real machine.
Machine learning is only possible when there’s enough of data available. Data collection is one of the most vital components of the machine learning lifecycle, but it’s also the one that’s often overlooked.
Web scraping—programmatically collecting data from web pages—is often the primary technique used to collect information from the Internet. While web scraping can be very straightforward in some instances, it can be very tricky in other instances. Especially when the data we’re interested in is hidden behind a button that requires a mouse click.
Continue reading “Building an Advanced Web Scraper for Kaggle Leaderboards Using RSelenium”
While looking to try my hand at using Apple’s Core ML framework for on-device machine learning, I stumbled upon a lot of tutorials. After having tried a few, I decided to come up with my very own 😛
Turns out, it was a good exercise! In this tutorial, I’ll walk you through the development of an app that uses a pre-trained Core ML model to detect a person’s age from an input image. So here we go.
Continue reading “Using Core ML and Vision in iOS for Age Detection”
Building deep learning models for audio classification is pretty common and you will find numerous blogs and articles that describe how to build the standard audio classification models using Keras.
There are numerous use-cases associated with audio processing and deep learning, but the one that amazed me was audio separation library Spleeter — where they split the given audio into various tracks such as vocal, piano, drums, bass, and accompaniment. I was really baffled at the accuracy with which the library splits the tracks and I would give full credit to the authors for building such an amazing library.
One thing I observed while going through the library’s source code is that they have used Tensorflow’s estimator approach to build the model and not the Keras-based approach. That’s when I was intrigued to learn more about what Estimators are and their benefits.
Continue reading “TensorFlow Estimators — TFLite and Model Generation”
In a previous tutorial titled Image Upload from Android to a Python-based Flask Server, we created a project in which an Android app uploads an image to an HTTP server created using Flask in Python.
This tutorial extends on the previous project to classify that image in the Flask server using a pre-trained multi-class classification model and display the class label in an Android app. The model is a multilayer perceptron (MLP) model created using Keras, which is trained on the MNIST dataset.
Continue reading “Image Classification on Android using a Keras Model Deployed in Flask”
Amongst all the cool new tech releases and updates that happened last year, three in particular stood out to me:
SwiftUI — because I’m so over XIBs and Storyboards. SFSpeechRecognizer providing on-device and unlimited usage of transcription ML Kit’s Translation API also providing on-device and unlimited usage of language translationThese three advances inspired a few projects of mine, in which I’ve combined them in various ways. For instance, I’ve created a speech translator that uses zero data, even while traveling internationally.
As pointed out to me by a reader here on Medium, it only makes sense, then, for me to combine them all into one ‘last’ ultimate example: and on-device subtitle generator for video.
Continue reading “On-Device Video Subtitle Generation in SwiftUI”
Machine learning is often viewed through the lens of transformation, whether it’s reshaping an industry, automating tedious tasks, or inspiring immersive experiences.
The latter transformation is front and center in Hart Woolery’s InstaSaber. It’s an ML-powered iOS app that lets users take a rolled up piece of paper and, by holding it in front of the phone’s camera, generate a realistic augmented reality (AR) lightsaber that’s highly-responsive to real-time hand movement.
