Hey friends! Welcome to ./engineered_mind.sh – the newsletter if you want to engineer your mind & your life. Subscribe for free and join more than 900 people for exclusive content coming every Sunday!
👕 New nerdy merch on Physics, Mathematics, Engineering and selected VisualizeIdea graphics! :)
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.
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.
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.
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!
Meme of the Week
If you have any wishes for video topics, you can submit them in this form. Apart from that you can always reach out to me via Twitter or Instagram or LinkedIn, so let’s connect!
Join the Discord server where you can connect with like-minded people.
For any business related issues or collaborations, feel free to write me an email to firstname.lastname@example.org!
See you next week and in the meantime, make sure to keep engineering your mind!