Showing posts from December, 2017

Find your model's optimal hyperparameters with Hyperopt

While checking out some tools for automated hyperparameter optimization, I came across a quite popular library called Hyperopt . It provides an implementation for Random Search and Tree-of-Parzen-Estimators (TPE). Unfortunately, most examples out there us a dummy function to replace the model, but I could not find any example that uses TensorFlow. That's why I wanted to provide a basic simple Hyperopt example with TensorFlow. This example can be found on my Github . Do you have any experiences with other libraries for hyperparameter optimization? I would be happy if you share your experiences? For instance, I have read that a  Sacred  extension called Labwatch  also allows to define a search space for algorithmic hyperparameter optimization, but comes with different algorithms.

Better IDE support for Python with Type Hints

About 12 years ago, I started to learn programming with Java and C#. Both languages are type-safe and have therefore a great support when using an IDE like Eclipse, IntelliJ or Visual Studio. But throughout my software development journey, I also made my hands dirty with other dynamically typed languages like JavaScript or Python. While checking out Angular2 more than a year ago, I used TypeScript for my first time, because it was recommended by the Angular team. I quickly realized how much painless it was to develop a web application with TypeScript instead of using classic JavaScript. The ability to strictly assigning a data type to a variable allows any IDE to offer much better tooling support, such as IntelliSense. At least in my point of view, programming in TypeScript felt much more like programming in C# than in JavaScript, where I always felt like I had to know every possible function name by heart. Which was especially difficult when you use JavaScript on a very non-regular