Data Augmentation: Essential AI Training Tutorial for Beginners | Henry Harvin

Data Augmentation | Best Artificial Intelligence Training Tutorial For Beginners | Henry Harvin

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This Henry Harvin video on “Understanding the basics of Data Augmentation” is a complete video tutorial for beginners who want to learn of Data Augmentation. This tutorials will help you to learn AI course will make you understand the basics of AI, tools The prediction accuracy of the Supervised Deep Learning models is largely reliant on the amount and the diversity of data available during training. The relation between deep learning models and amount of training data required is analogous to that of the relation between rocket engines (deep learning models) and the huge amount of fuel (huge amounts of data) required for the rocket to complete its mission (success of the deep learning model).
DL models trained to achieve high performance on complex tasks generally have a large number of hidden neurons. As the number of hidden neurons increases, the number of trainable parameters also increases. The number of parameters in the State-of-the-art Computer Vision models such as RESNET (60M) and Inception-V3 (24M) is of the order of ten million.
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