![]() ![]() When it comes to working with deep learning and Python I highly recommend that you use a Unix-based environment.ĭeep learning tools can be more easily configured and installed on Unix systems, allowing you to develop and run neural networks quickly. Pre-configured deep learning environments Figure 1: My deep learning Virtual Machine with TensorFlow, Keras, OpenCV, and all other Deep Learning and Computer Vision libraries you need, pre-configured and pre-installed. We’ll then configure and install TensorFlow 2.0 on our macOS system. In the first part of this tutorial, we’ll briefly discuss the pre-configured deep learning development environments that are a part of my book, Deep Learning for Computer Vision with Python. To learn how to install TensorFlow 2.0 on macOS, just keep reading. Inside this tutorial, you’ll learn how to install TensorFlow 2.0 on macOS (using either Catalina or Mojave).Īlternatively, click here for my Ubuntu + TensorFlow 2.0 installation instructions. There are a number of important updates in TensorFlow 2.0, including eager execution, automatic differentiation, and better multi-GPU/distributed training support, but the most important update is that Keras is now the official high-level deep learning API for TensorFlow.įurthermore, if you own a copy of my book, Deep Learning for Computer Vision with Python, you should use this guide to properly install TensorFlow 2.0 on your macOS system. ![]() In this tutorial, you will learn to install TensorFlow 2.0 on your macOS system running either Catalina or Mojave Click here to download the source code to this post ![]()
0 Comments
Leave a Reply. |