This post is part 2 in the series on Google Cloud AutoML Vision Edge. In the previous post, we saw how we can train an edge-ready TensorFlow Lite model with AutoML, from scratch. TFLite models take up less storage space but also are a bit less accurate in comparison with a TF SavedModel format.
Continue reading AutoML Vision Edge: Exporting and Loading TensorFlow SavedModels with Python
Experimental studies have shown that a few trees properly placed around buildings can reduce air conditioning needs by 30%, thus reducing energy consumption and bills.
Similarly, tree-based predictive modeling, implemented using decision trees, can increase a business’s profit by predicting, for example, what customers would prefer in future, thus helping them making informed decisions well in advance.
Continue reading Decision Tree Classification in Python with Scikit-Learn
Most Android apps perform one or more background tasks, often including the ones that are long-running. Examples include making a network call, an I/O request, database read/write — you get the idea.
More often than not, upon completion of these background tasks, we need to take an action that’s visible to the user. These can include:
A confirmation Toast msg — upon a successful DB call Taking the user to a new screen — upon a successful sign-in network call Showing an error msg popup —upon the failure of an I/O operationContinue reading “Handling Background Tasks with Kotlin Coroutines in Android”
In this post, we’re going to discuss the most widely-used activation and loss functions for machine learning models. We’ll take a brief look at the foundational mathematics of these functions and discuss their use cases, benefits, and limitations.
Without further ado, let’s get started!
To learn complex data patterns, the input data of each node in a neural network passes through a function the limits and defines that same node’s output value. In other words, it takes in the output signal from the previous node and converts it into a form interpretable by the next node. This is what an activation function allows us to do.
Continue reading Exploring Activation and Loss Functions in Machine Learning
One of the most challenging questions data scientists face is how data can bring value to a specific problem. Before jumping directly into solving a problem using machine learning and AI, it’s important to decide whether that problem is solvable or not.
And to decide this, data analytics comes into the picture. Having the proper insights on your data helps you to obtain the confidence to know that you are ready to engage a machine learning algorithm, hence making it easier to find out the best way to tackle the given problem.
Continue reading Exploratory data analysis: Data characteristics and visualizations
This is the follow-up blog post from my previous article on using Google Analytics for Firebase. While GA allows you to measure and understand user behavior, Crashlytics instead allows you to track and keep a log of all the crashes that have happened on the devices using your app—regardless of whether they choose to report the said crash or not.
Continue reading Ensuring app quality in Android with the new Firebase Crashlytics SDK
In this article we’ll focus on handling errors in Swift.
Proper error handling is crucial to writing effective production-level code that will get shipped with real apps. Often, while working on an app’s features, developers tend to focus on the “HAPPY PATH” (see the diagram below)—but in reality, we can’t ship any app without properly addressing different error scenarios that might occur in our code flows.
This tutorial is the 9th part in the series, ML Kit for Mobile Developers. If you’re not quite caught up, you can start here:
Earlier this year at Google I/O, the team behind Firebase ML Kit announced the addition of 2 new APIs into their arsenal: object detection and an on-device translation.
Continue reading Creating an offline translation Android app using Firebase ML Kit
Displaying images is one of the most common tasks for any Android developer, but it can become challenging to manage for a variety of reasons.
The process requires loading and displaying the image, handling caches and image manipulations, loading the correct size of the image in the memory, handling network requests if you are loading the image from the internet, much testing, and many other nightmares.
Continue reading Using Glide to Efficiently Load Images in Android
Mobile machine learning, relatively speaking, is still in its infancy. While I fully envision a future where hundreds of thousands of mobile apps will seamlessly leverage ML to provide more personalized, responsive, and immersive user experiences, I also recognize that we all have to start somewhere.
Whether you’re a mobile dev who wants to add ML to your toolkit, a hobbyist looking for a new technology to tinker with, or a product manager trying to get a sense of how mobile ML might work within your roadmap, there are some easy ways to jump right in and experiment.
Continue reading Popular Mobile Machine Learning Projects to Help You Start Building
