Sharp Sight AI

Dealing with the problem of overfitting is one of the core issues in machine learning and AI.
Your model seems to work perfectly on the training set, but when you try to validate it on the test set … it’s terrible.
This is a core problem in machine learning, especially when you start using advanced ML techniques like deep learning....

If you want to build high-performing machine learning and AI systems, then simply training those systems is rarely enough.
You often need to build multiple models, often with multiple different algorithms, and then compare the different models to each other to see which is best....

In machine learning, making sure that you have a model that performs well is, in some sense, the most important thing.
This means that you need to be really good at evaluating different models.
But, this can be a challenge, as you run into issues like overfitting, hyperparameter optimization, and model selection....

Mmm. Overfitting.
It’s the bane of most machine learning developers.
You build a model that performs so well on the training data, and think “I’ve done such a good job!” while patting yourself on the back.