Contents
How do I know if my dataset is good?
How Do You Know If Your Data is Accurate? A case study using search volume, CTR, and rankings
- Separate data from analysis, and make analysis repeatable.
- If possible, check your data against another source.
- Get down and dirty with the data.
- Unit test your code (where it makes sense)
- Document your process.
What is metrics in deep learning?
They’re used to train a machine learning model (using some kind of optimization like Gradient Descent), and they’re usually differentiable in the model’s parameters. Metrics are used to monitor and measure the performance of a model (during training and testing), and don’t need to be differentiable.
How does dataset size affect deep learning model performance?
An over-constrained model will underfit the small training dataset, whereas an under-constrained model, in turn, will likely overfit the training data, both resulting in poor performance. Too little test data will result in an optimistic and high variance estimation of model performance.
What are the metrics used in deep learning?
In most competitions, the average precision (AP) and its derivations are the metrics adopted to assess the detections and thus rank the teams. Guiding principle in all state-of-the-art metrics is the so-called Intersection-over-Union (IoU) overlap measure.
How big of a dataset do you have?
Feeling ebullient, you open your web browser and search for relevant data. Chances are, you find a dataset that has around a few hundred images. You recall that most popular datasets have images in the order of tens of thousands (or more).
How to use deep learning when you have limited data?
This is Part 2 of How to use Deep Learning when you have Limited Data. Checkout Part 1 here. We have all been there. You have a stellar concept that can be implemented using a machine learning model. Feeling ebullient, you open your web browser and search for relevant data.