When working with image data, practitioners often use augmentations. Augmentations are techniques that artificially and randomly alter the data to increase diversity. Applying such transformations to the training data makes the model more robust.
The field of Machine Learning is huge. You can easily be overwhelmed by the amount of information out there. To not get lost, the following list helps you estimate where you are. It provides an outline of the vast Deep Learning space.
After you have finally created that training script it’s time to scale things up. From a local development environment, be it an IDE or Colab, to a large computer cluster, it’s quite a stretch. The following best practices make this transition easier.
Have you ever asked yourself where you currently are on your Machine Learning
journey? And what’s there that you can still learn about?
This checklist helps you answer such questions. It provides
Recent successes, achieved with the help of Reinforcement Learning, have quite extensively been covered by the media. One can think of DeepMind’s AlphaGo algorithm: It learned to play the age-old game of Go and even developed its own playstyle.
