Articles Fritz has written:

Snapchat Lens Creator Spotlight: Piotar Boa

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Piotar Boa is probably the man behind your favorite lenses. Known for his vast clientele list and innovative AR lenses, he’s a Lens Creator and Spark AR Partner and has worked with numerous brands to bring new AR experiences to millions of viewers. We caught up with the busy Italian to learn more about how he views the industry and what he hopes to see in the future.

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Real-Time Object Detection on Raspberry Pi Using OpenCV DNN

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Running deep learning models is computationally expensive. And when it comes to image processing with computer vision, the first thing that comes to mind is high-end GPUs—think the 1080ti and now the 2080ti.

But it’s hard to run computer vision models on edge devices like Raspberry Pi, and making a portable solution is difficult with deep learning libraries like TensorFlow or PyTorch.

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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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