Jax is poised to revolutionize machine learning by simplifying the process of generating custom code to control compute clusters. In this book, Brett Koonce teaches Image Recognition using this new framework. You will build from the basics to the current state of the art and be able to tackle new problems.
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Developers with Python programming experience who would like to learn neural networks by example using Jax as a starting point.
Dive into and apply practical machine learning and dataset categorization techniques while learning Jax and deep learning. This book uses neural networks to do image recognition all in the familiar and easy to work with Python language.
It begins with a basic machine learning overview and then ramps up to neural networks, convolutions and how they work together. Using Jax you’ll perform data augmentation, build and train large networks, and build networks for mobile devices. You’ll also cover cloud training and the networks you build can categorize greyscale data, such as MNIST, to large scale modern approaches that can categorize large datasets, such as ImageNet. We will look at how we can apply these techniques to our own work.
Image Recognition with Jax uses a simple approach that adds progressive layers of complexity until you have arrived at the current state of the art for this field.