deep learning for computer vision figure training and validat ion accuracy figure training and validat ion loss these plots are characteristic of overfitting the training accuracy increases linearly over timeuntil it reaches nearly %whereas the validation accuracy stalls at - the validation loss reaches its minimum after only five epochs and then stallswhereas the training loss keeps decreasing linearly until it reaches nearly because you have relatively few training samples ( , )overfitting will be your number-one concern you already know about number of techniques that can help mitigate overfittingsuch as dropout and weight decay ( regularizationwe're now going to work with new onespecific to computer vision and used almost universally when processing images with deep-learning modelsdata augmentation using data augmentation overfitting is caused by having too few samples to learn fromrendering you unable to train model that can generalize to new data given infinite datayour model licensed to