Image Augmentation for Deep Learning | Benefits, Techniques & Best Practices
Image augmentation can increase the size of your dataset and improve model performance. We explore how by discussing the aims of augmentation and various techniques. These include flipping images, adjusting brightness, color jitter and random noise. We end by discussing the best practices when it comes to evaluating models trained on augmented data. Remember to like, comment, and share this video with your fellow deep learning enthusiasts. ADO is the ultimate destination for in-depth tutorials on IML/ XAI and algorithm fairness. Subscribe so you don't miss any future videos :) *NOTE*: You will now get the XAI course for free if you sign up (not the SHAP course) SHAP course: https://adataodyssey.com/courses/shap... XAI course: https://adataodyssey.com/courses/xai-... Newsletter signup: https://mailchi.mp/40909011987b/signup Read the companion article (no-paywall link): https://medium.com/data-science/augme... Medium: / conorosullyds Twitter: / conorosullyds Mastodon: https://sigmoid.social/@conorosully Website: https://adataodyssey.com/ #ImageAugmentation #DeepLearning #AI #DataScience #MachineLearning #TechTutorials
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