The post aims to show how to create Jupyter environments and how to debug any issues. It also provides some commands that are good for general debugging.
The post aims to show how to create Jupyter environments and how to debug any issues. It also provides some commands that are good for general debugging.
Sometimes I find it useful to be able to run Python commands right from the command line (without entering a Python console). Here are some ways I’ve found it useful.
The most frequently used evaluation metric for object detection is “Average Precision (AP)”. But, despite the attempts of well-intentioned researchers to create a single metric for comparing models, no single metric is the right one for all the cases. Thus the landscape of metrics has become filled with small variations on the idea of average precision. This post aims to clarify those variations.
I find that sometimes the best way to understand a topic is to see it in action. That can be tricky for some topics, like computer vision and deep learning, so I created this page to aggregate some of my favorite visualizations in these fields. I hope to continuously add to this page. Let me know if you have any suggestions for what made an idea click for you!
Selecting and evaluating a dataset to use for training a machine learning model is an essential step in the process. It’s far more important than having the latest architecture or doing the most detailed hyperparameter tuning. It’s probably the most important aspect of the process other than asking the right questions in the first place.
This post is an introduction to principal component analysis (PCA). It was originally written to accompany a presentation to the NOVA Deep Learning Meetup.