You’ll find that for experiments in the most chapters inside the Starter Bundle and half the chapters in the Practitioner Bundle can be executed on your CPU. macOS for deep learning with Python, TensorFlow, and KerasĪs you get acclimated in the deep learning domain, you’ll want to perform many experiments to hone your skills and even to solve real-world problems.
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To learn how to configure macOS for deep learning and computer vision with Python, just keep reading. macOS for deep learning with Python, TensorFlow, and Keras (this post).
Setting up Ubuntu 16.04 + CUDA + GPU for deep learning with Python. Configuring Ubuntu for deep learning with Python (CPU only). Pre-configured Amazon AWS deep learning AMI with Python. Your deep learning + Python Ubuntu virtual machine. In case you’re on the wrong page (or you don’t have macOS), take a look at the other deep learning development environment tutorials in this series: I created these tutorials to accompany my new book, Deep Learning for Computer Vision with Python however, you can use these instructions to configure your system regardless if you bought my book or not. This tutorial is the final part of a series on configuring your development environment for deep learning. In today’s tutorial, I’ll demonstrate how you can configure your macOS system for deep learning using Python, TensorFlow, and Keras. Click here to download the source code to this post
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