This is my attempt to tackle traffic signs classification problem with a convolutional neural network implemented in TensorFlow (reaching 99.33% accuracy). The highlights of this solution would be data preprocessing, data augmentation, pre-training and skipping connections in the network.
This is a TensorFlow follow-along for an amazing Deep Learning tutorial by Daniel Nouri. Daniel describes ways of approaching a computer vision problem of detecting facial keypoints in an image using various deep learning techniques, while these techniques gradually build upon each other, demonstrating advantages and limitations of each.
Most of the tasks in data science are long-running, and many folks (me included) execute those tasks on remote machines. And the crucial thing for those tasks is logging: you do need to know how training process was going and see the learning curves. It would also be convenient if you could access those logs from anywhere and be notified when the process had finished. So I built the cloudlog!
In the Metal Camera Tutorial series we have created a simple app that renders camera frames on screen in real time. However, this app uses Metal framework, which is not available in iOS Simulator. Basically, your app won’t even build if you select simulator as a build device, which is a shame in case you want to add unit tests for example, being able to run them without actual device connected to your machine.
So one Saturday I got particularly bored and thought I should configure my Jupyter Notebook a bit.