Around a decade ago, people were shopping in a physical store where the number of products was limited. Due to the emergence of big data and an increase in the store size, users have been provided with a lot more options than before, hence it is more important to ensure the user is recommended the products that align with his interests, or it might lead to a drop in the company’s profit value. Due to increased options and fewer resources, the manual product recommendations are now automated using AI to build a recommender system.
Overfitting models are high in variance, low in bias, and cannot generalize on unseen data. If the training accuracy is very high and the validation accuracy is super low, or the training loss and validation loss are very distinct from each other, it means that the model is overfitting. Below are ten techniques that I use to tackle overfitting in my models that would also work for you, and there are two more bonus points to help you get more insights on overfitting models.
When it comes to deep learning, a critical thing to work with is the hyperparameters. To those of you who don’t know, hyperparameters are the variables that govern the structure of your neural network. It could be — but are not limited to — the number of layers, the number of neurons, the learning rate, or the number of epochs. Whenever you create a model in deep learning, the initial model is mostly not perfect unless you hit a home run, and then you optimize the model by tweaking the hyperparameters before you pick another one.
This tutorial is not…
Diving into AI, working towards finding a closer link in science and humanity. Presently working on devising novel depression prediction methods.