Articles Fritz has written:

Colorizing B/W Images With GANs in TensorFlow

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GANs are one of the most interesting topics in machine learning today. They have been used in a number of problems (and not just to generate MNIST digits!) and performed very well in each case. A GAN (General Adversarial Network) consists of a generator and a discriminator, which compete against each other to produce mind-blowing results. Here, we’ll take a mathematical approach towards understanding the GAN and its loss functions. As the idea behind training a GAN comes from game theory, we’ll have a quick look at the Minimax Optimization Strategy too.

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Classification with TensorFlow and Dense Neural Networks

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In my previous article that examined classification with TensorFlow, I covered the basics details of how to perform linear classification with TensorFlow’s estimator API. You can read that blog post here:

For part two, I’m going to cover how we can tackle classification with a dense neural network. I’ll be using the same dataset and the same amount of input columns to train the model, but instead of using TensorFlow’s LinearClassifier, I’ll instead be using DNNClassifier. We’ll also compare the two methods.

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Using TensorFlow.js to Automate the Chrome Dinosaur Game

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In this blog post, we’ll be learning how to automate the Chrome Dinosaur Game using neural networks with TensorFlow.js. If you haven’t played it before, it’s a side scrolling game available offline (for when Chrome or your Internet crashes) where you control a 2D dinosaur and have to jump and duck to avoid obstacles. Give it a shot here:

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A Practical Guide to Feature Engineering in Python

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Feature engineering is one of the most important skills needed in data science and machine learning. It has a major influence on the performance of machine learning models and even the quality of insights derived during exploratory data analysis (EDA).

In this article, we’re going to learn some important techniques and tools that will help you properly extract, prepare, and engineer features from your dataset.

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Building an image recognition React app using ONNX.js

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The Open Neural Network Exchange (ONNX) is an open standard for representing machine learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose combinations that are best for them. ONNX is developed and supported by a community of partners including AWS, Facebook OpenSource, Microsoft, AMD, IBM, Intel AI, etc.

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Class activation maps: Visualizing neural network decision-making

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Deep neural networks perform incredibly well on computer vision tasks such as classification, object detection, and segmentation, but what do they consider before performing these tasks, and what does it take to make these decisions?

Interpreting neural network decision-making is an ongoing area of research, and it’s quite an important concept to understand. Neural networks are used in the real-world, so we can’t treat them like black boxes—we need to learn what they interpret, how they interpret, and what information each layer/channel in a neural network has learned.

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ARCore and Sceneform in Android — Performing Gestures and Collisions on Transformable Nodes

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At the turn of this new decade, if there’s one field that has the potential to completely change our way of interacting with smartphones, it’s augmented reality (AR Glasses: are you reading this?). Google has been pushing ahead with its own platform—ARCore—that enables developers to create AR experiences on Android, iOS, Unity, and more.

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Augmented Reality Using Unity’s AR Foundation Framework

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In this part of our tutorial series exploring Unity’s AR Foundation framework, we’ll learn how to detect planes and place objects on those planes. We will also learn how to use C# to create scripts and add programmable behavior to our app. If you have not read the introduction to this series, I suggest doing so, as this is a continuation from our overview.

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AirBnB Clone with React Native: “Forgot Password?” with Firebase

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This tutorial is the seventh chapter of our implementation of an AirBnB clone in React Native. In the previous chapter, we successfully implemented animated checkmarks for our Login form. In case you need to get caught up, here are links to parts 1–6:

In part 7, we’re going to continue where we left off by implementing a “forgot password?” screen. The forgot password screen is available in most web and app systems in case a user forgets their login credentials. Here, the idea is to create a simple forgot password screen with an email input form. Then we’re going to use Firebase to handle the backend configuration.

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