In 2015, Google established its first TPU center to power products like Google Calls, Translation, Photos, and Gmail. To make this technology accessible to all data scientists and developers, they soon after released the Cloud TPU, meant to provide an easy-to-use, scalable, and powerful cloud-based processing unit to run cutting-edge models on the cloud.
Continue reading Step-by-Step Use of Google Colab’s Free TPU
This tutorial uses the popular computer vision library OpenCV for building an image classifier that runs on Android devices.
The overall process looks like this. First, the color histogram of the hue channel from the HSV color space is extracted from the image dataset. Next, an artificial neural network (ANN) is built and trained by such features and then saved for later use in an Android app. An Android Studio project is then created, which imports the Android release of OpenCV. After being imported successfully, the saved trained ANN is loaded for making predictions.
Continue reading Image Classification on Android using OpenCV
Cat Face is the latest SnapML-powered Lens built by the team at Fritz AI. The Lens lets users draw photo-realistic cats and wear the result like a mask. If you haven’t already, try it out for yourself here:
Following up from my earlier blogs on training and using TensorFlow models on the edge in Python, in this eighth blog post in this series, I’ll be talking about how to train a multi-label image classification model that can be used with TensorFlow.
Continue reading Training a Multi-Label Image Classification Model with Google Cloud AutoML
From helping doctors make faster and more reliable diagnoses, to powering new frontiers in space exploration, AI has made a huge impact across industries, and we witness it every day.
Continue reading These are The News: A curated list of AI & machine learning newsletters
Currently, different domains of machine learning software and hardware have different compiler infrastructures. There are number of challenges posed by this dynamic, including:
MLIR seeks to address this software fragmentation by building a reusable and extensible compiler infrastructure. In this piece, we’ll look at a conceptual view of MLIR.
MLIR seeks to promote the design and implementation of code generators, optimizers, and translators at various stages of abstraction across different application domains. The need for MLIR arose from the realization that modern machine learning frameworks have different runtimes, compilers, and graph technologies. For example, TensorFlow itself has different compilers for different frameworks.
It would be great if we as devs got to play with limitless memory and never had to care about working with it judiciously. Unfortunately, that isn’t true, and hence, we have to behave like a renter to the OS—rent the memory for a while, use the memory, and then hand it back.
Swift is a smart language, and it knows that many devs don’t like handing the memory back to the environment; hence, it keeps track of the allocated memory using a mechanism called ARC (automatic reference counting).
Developing with the camera on Android can be difficult. When we need to develop a camera-based application, we need to do a lot of manual work, and we need to do handle a lot of complex things with the Camera API, like handling preview screens, image rotations, and much more
At Google IO 2019, Google added another powerful tool for camera development in Android called CameraX as part of Jetpack.
In this post, we are going to investigate a relatively newer field in deep learning which involves graphs — a very important and widely used data structure. This post encompasses the basics of graphs, the amalgamation of graphs and deep learning, and a basic idea about graph neural networks and their applications. We will also briefly discuss on how to build graphs with a Python library called NetworkX
Continue reading Learn how to display and manage notifications in Android
Processing a couple of gigabytes of data on one’s laptop is usually an uphill task, unless the laptop has high RAM and a whole lot of compute power.
That notwithstanding, data scientists still have to look for alternative solutions to deal with this problem. Some of the hacks involve tweaking Pandas to enable it to process huge datasets, buying a GPU machine, or purchasing compute power on the cloud. In this piece, we’ll see how we can use Dask to work with large datasets on our local machines.
