Data augmentation techniques for deep learning, particularly for image datasets, are essential when working with limited data to improve the performance of neural networks. These techniques, such as flipping, rotating, scaling, cropping, translating, and adding Gaussian noise, create variations of existing data, helping models generalize better by preventing them from learning irrelevant patterns. Advanced methods like conditional GANs and neural style transfer can simulate different environmental conditions, enhancing model robustness. Augmentation can be performed offline or online, depending on dataset size, with interpolation methods filling unknown image boundaries during transformations. The effectiveness of data augmentation is demonstrated through a comparison between a VGG19 model and a model using Nanonets' platform, with the latter achieving higher accuracy due to augmentation.