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#007 Top 5 Benefits of Self-Driving Cars

24.01.2020

Who can benefit from self-driving cars? In general, there is no doubt that everyone will benefit from self-driving cars. First, society, from a safety standpoint, benefits from eliminating most of 30.000 fatal accidents per year. The elderly and disabled can enhance independence. Also, commuters can benefit by turning their boring drive to work into a relaxing time. They could safely pursue more productive or entertaining activities, like responding to emails or watching a movie. Not…
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#006 The moral dilemma of self-driving cars

24.01.2020

How should autonomous vehicles decide who to sacrifice? Almost 1.3 million people die in road crashes each year. One of the most compelling reasons in favor of the introduction of autonomous vehicles is to enhance driving safety and reduce road casualties. However, this prompts the question of whether these cars will be able to make decisions in a split second, and respond just as well as experienced human drivers. And most importantly, how an AI-powered…
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#014 PyTorch – Convolutional Neural Network on MNIST in PyTorch

11.12.2019

Highlights: Hello everyone and welcome back. In the last posts we have seen some basic operations on what tensors are, and how to build a Shallow Neural Network. In this post we will demonstrate how to build efficient Convolutional Neural Networks using the nn module In Pytorch. You will find that it is simpler and more powerful. To demonstrate how it works, we will be using a dataset called MNIST. Tutorial Overview: Setting up the…
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#001 PyTorch – How to Install PyTorch with Anaconda and use it on Colab?

17.11.2019

Highlights: Welcome everyone! In this post we are going to cover the prerequisites for installing PyTorch on our machines. In addition, we will show how you can use PyTorch in Google Colab. It’s relatively easy to get it up and running, so let’s roll. Tutorial Overview: PyTorch installation with Anaconda How to verify the installation? Using Google Colab 1. PyTorch installation with Anaconda Due to simplicity, the recommended option to start with is to use…
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#013 PyTorch – Shallow Neural Network on MNIST dataset in PyTorch

16.11.2019

Highlights: Hello everyone and welcome back. In the previous post we have seen how to build one Shallow Neural Network and tested it on a dataset of random points. In this post we will demonstrate how to build efficient Neural Networks using the nn module. That means that we are going to use a fully-connected ReLU network with one hidden layer, trained to predict the output \(y \) from given \(x \) by minimizing squared…
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