Machine Learning Model Evaluation & Selection

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A good data scientist is not one who knows all the fancy algorithms but one who knows that he/she is overfitting. We all have been through that time when our super awesome, fully tuned model has failed to live up to the expectations on Kaggle private LB or after deployment. Knowing how to get an unbiased estimate of the predictive power of our model is important. There are different validation strategies like holdout and cross validation which are commonly used in practice for this. But which strategy is appropriate in which scenario is something that needs more discussion and thought.

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Passing Data between Fragments on Android Using ViewModel

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In Android application development, we often need to pass data between fragments—i.e. from a listing fragment to a detailed fragment—to update values.

Passing data between fragments in Android is very common, and to do this task, we’ve traditionally used Interface.

But with the introduction of Android Jetpack, there’s a different way to move data between fragments. In this tutorial, we’ll take a closer look at ViewModel and see how it compares to Interface.

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Machine learning on mobile devices: 3 steps for deploying ML in your apps

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Machine learning can help us take the best route back home, find a product that matches our needs, or even help us schedule hair salon appointments. If we take an optimistic view, by applying machine learning in our projects, we can make our lives better and even move society forward.

Mobile phones are already a huge part of our lives, and combining them with the power of machine learning is something that, in theory, can create user experiences that delight and impress users. But do we really need to add machine learning to our apps? And if so, what tools and platforms are currently at our disposal? That’s what we’ll talk about in this article.

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How to create a custom loss function in Keras

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We use loss functions to calculate how well a given algorithm fits the data it’s trained on. Loss calculation is based on the difference between predicted and actual values. If the predicted values are far from the actual values, the loss function will produce a very large number.

Keras is a library for creating neural networks. It’s open source and written in Python. Keras does not support low-level computation, but it runs on top of libraries like Theano and TensorFlow.

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How to Capture the Best Frame in an iOS Image Processing App

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If you’ve ever developed an iOS Vision app that process frames of a video buffer, you know that you need to be careful with your resources. You shouldn’t process each frame—i.e., where the user just moves the camera around.

In order to classify an image with high accuracy, you’ll need to capture a stable scene. This is crucial for apps that use Vision. In this tutorial, I’ll be diving into this problem and the solution Apple suggests.

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How to create custom wavy headers using react-native-svg and Expo

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In React Native apps, support for Scalable Vector Graphics (SVG) is provided by an open-source module called react-native-svg that’s maintained by the larger developer community.

Using SVG can enhance an app’s design when it comes to displaying different patterns. It can make a difference in how the look and feel of the app might appear to the end-user, as well how it is easy to edit the pattern built using SVG. SVG is mainly found on the web, and while they have similar uses to JPEG, PNG, and WebP image types, SVG is not resolution-dependent. Hence, the definition according to Wikipedia:

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Hands-on with Feature Selection Techniques: Embedded Methods

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This article is part 4 of a series centered on hands-on approaches to feature selection techniques. If you’ve missed any of the other posts, I’d recommend checking them out:

Welcome back! In part 4 of our series, we’ll provide an overview of embedded methods for feature selection.

We learned from the previous article a method that integrates a machine learning algorithm into the feature selection process. Those wrapper methods provide a good way to ensure that the selected features are the best for a specific machine learning model.

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Exploring Core Image: Apple’s First Computer Vision Framework

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Over the years, Apple has released some breakthrough features at its annual WWDC conference. In addition to the iOS community, developers all over the world keenly look forward to Apple’s annual conferences. It’s no wonder that figuring out which WWDC conference stood out from the rest is always a dilemma.

Some say WWDC 2019 was the best developer conference in years, due to the slew of new features and significant tools introduced. SwiftUI, a powerful new framework for building user interfaces, and major upgrades in the Core ML and Vision framework make it tricky to downplay Apple’s achievements in 2019 — and I won’t do that either.

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