That being said, it is often desirable to install your environment on the bare metal so that you can take advantage of your physical hardware. By far, this is the fastest way to get up and running with Deep Learning for Computer Vision with Python. Configuring Ubuntu for deep learning with PythonĪccompanying my new deep learning book is a downloadable pre-configured Ubuntu VirtualBox virtual machine with Keras, TensorFlow, OpenCV, and other computer vision/machine learning libraries pre-installed. To get started configuring your Ubuntu machine for deep learning with Python, just keep reading.

Configuring macOS for deep learning with PythonĪs you start to walk the path to deep learning and computer vision mastery, I’ll be right there with you.Setting up Ubuntu (with GPU support) for deep learning with Python.

Configuring Ubuntu for deep learning with Python (i.e., the post you are currently reading).I’ll be demonstrating how to configure your own native development environment for the following operating systems and peripherals: Now that Deep Learning for Computer Vision with Python has officially released, I’ll be publishing three posts this week where I will demonstrate how to stand up your own deep learning environments so that you can get a head start before you dive into reading. Your environment can quickly become obsolete, so it is imperative to become an expert in installing and configuring your deep learning environment. These issues are further compounded by the speed of deep learning library updates and releases - new features push innovation, but oftentimes break previous versions. Different operating systems, hardware, dependencies, and the actual libraries themselves can lead to many headaches before you’re even able to get started studying deep learning. When it comes to learning new technology such as deep learning, configuring your development environment tends to be half the battle. Click here to download the source code to this post

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