GPX Files Distance Modifier: Don’t lose your progress

๐Ÿ‘‰ Has it ever happened that your watch stopped or had ant malfunction during your workout, thus losing part of your activity? Or has it happened that due to a bad GPS signal your watch recorded a shorter distance than your actual one? ๐Ÿค” ๐Ÿ‘‰ Imagine that, after a maximal effort, you have just broken your 10km PB but your watch only recorded 9.8km, thus … Continue reading GPX Files Distance Modifier: Don’t lose your progress

GPX Files Cleaner: Dropping Unnecessary Pauses

๐Ÿ‘‰ How frequently does it happens that your running or cycling activities are interrupted by a red traffic light, the need to drink some water, or simply to wait for your partner to reach you? ๐Ÿค” ๐Ÿ‘‰ Even if you pause your watch during your activity, thus not recording any distance, it will still continue to record your total time which will be then shown … Continue reading GPX Files Cleaner: Dropping Unnecessary Pauses

Running Performance Calculator: Altitude, Elevation Gain, and Temperature

๐Ÿ‘‰ Have you ever wondered if you ran 10km at 2000m altitude, how fast would you run the same distance at lower altitudes? How would elevation gain and temperature affect your performance? ๐Ÿค” ๐Ÿ‘‰ย The goal of this running distance calculator is to estimate how your running pace and time would change given different elevation gain, altitude, and temperature. Features References Continue reading Running Performance Calculator: Altitude, Elevation Gain, and Temperature

Automatic code generator for training Reinforcement Learning policies

Generate custom template code to train your reinforcement learning policy using a simple web UI built withย Streamlit. It includes different environments and can be expanded to support multiple policies and frameworks with a high level of flexible hyperparameters customization. The generated code can be easily downloaded as .py file or Jupyter Notebook so as to immediately start training your model or use it as a … Continue reading Automatic code generator for training Reinforcement Learning policies