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Deep Learning to Predict Traffic Crashes

A deep model was trained on historical crash data, road maps, satellite imagery, and GPS to enable high-resolution crash maps that could lead to safer roads.

Full Article.

Enhancing Photorealism Enhancement

The network is trained via a novel adversarial objective, which provides strong supervision at multiple perceptual levels. We analyze scene layout distributions in commonly used datasets and find that they differ in important ways. We hypothesize that this is one of the causes of strong artifacts that can be observed in the results of many prior methods. To address this we propose a new strategy for sampling image patches during training. We also introduce multiple architectural improvements in the deep network modules used for photorealism enhancement. We confirm the benefits of our contributions in controlled experiments and report substantial gains in stability and realism in comparison to recent image-to-image translation methods and a variety of other baselines.

📝 Paper: https://arxiv.org/abs/2105.04619

💻 Code and data: https://github.com/isl-org/PhotorealismEnhancement

🚧 Project page: https://isl-org.github.io/PhotorealismEnhancement/

Deep learning with Julia

FastAI.jl is inspired by fastai, and is a repository of best practices for deep learning in Julia. Its goal is to easily enable creating state-of-the-art models. FastAI enables the design, training, and delivery of deep learning models that compete with the best in class, using few lines of code.

Github Repo.

Course of the Week - Mathematics for Machine Learning Specialization

Learn how to use linear algebra in order to calculate the page rank of a small simulated internet, applying multivariate calculus in order to train your own neural network, performing a non-linear least squares regression to fit a model to a data set, and using principal component analysis to determine the features of the MNIST digits data set.

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Book of the Week

Machine Learning Bookcamp

Machine Learning Bookcamp presents realistic, practical machine learning scenarios, along with crystal-clear coverage of key concepts. In it, you’ll complete engaging projects, such as creating a car price predictor using linear regression and deploying a churn prediction service. You’ll go beyond the algorithms and explore important techniques like deploying ML applications on serverless systems and serving models with Kubernetes and Kubeflow. Dig in, get your hands dirty, and have fun building your ML skills!

📕 Use "podengineered20" to get a 40% discount!

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Favourite Animation

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Closing Remarks

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