Subclass Layer, and implement call() with TensorFlow functions. Data augmentation can help an image ML model learn to handle variations of the image that are not in the training dataset
Data augmentation can help an image ML model learn to handle variations of the image that are not in the training dataset. For example, it is likely that photographs provided to an ML model (especially if these are photographs by amateur photographers) will vary quite considerably in terms of lighting. We can therefore increase the effective size of the training dataset and make the ML model more resilient if we augment the training dataset by randomly changing the brightness, contrast, saturation, etc. of the training images. While Keras has several built-in data augmentation layers (like RandomFlip), it doesn’t currently support changing the contrast and brightness. So, let’s implement one.
Train and Deploy TensorFlow Models using Google Cloud AI Platform. A practical workflow of TensorFlow model training and deploying
Understanding Google Cloud Fundamentals: Big Data & Machine Learning. With these comprehensive guides and Google Cloud platform courses, students are better equipped with the necessary knowledge needed for curate a basic use case for basic machine learning problems.
If you looking to learn about Google Cloud in depth or in general with or without any prior knowledge in cloud computing, then you should definitely check this quest out.
Google cloud platform offers a wide range of GPU options to choose from. Find out which GPU option can get you the best performance quicker results. Based on my experience, I’ll be sharing some results that will hopefully help you pick the right one and get you started.
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