Python Deep Learning Projects

Overfitting and underfitting 

With great power comes great responsibility and with deeper models come deeper problems. A fundamental challenge with deep learning is striking the right balance between generalization and optimization. In the deep learning process, we are tuning hyperparameters and often continuously configuring and tweaking the model to produce the best results, based on the data we have for training. This is optimization. The key question is, how well does our model generalize in performing predictions on unseen data?

As professional deep learning engineers, our goal is to build models with good real-world generalization. However, generalization is subjective to the model architecture and the training dataset. We work to guide ...