Deep Learning Models Can Learn Non-Existing Patterns

Deep learning models can sometimes learn non-existing patterns, especially when data is not properly shuffled during training. This post illustrates an example where a classification neural network failed to converge due to label-ordered data but performed well when the data was shuffled. Shuffling helps in mini-batch gradient descent by ensuring that each mini-batch contains a balanced representation of classes. Be mindful of this and other potential pitfalls to improve model generalization and performance.