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The pre-configured and ready-to-use runtime environment for the MIT 6.S094 course: Deep Learning for Self-Driving Cars, 2017. It includes Python 2.7, TensorFlow 0.12.1 and OpenCV 3.3.0. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.
The pre-configured and ready-to-use runtime environment for the MIT 6.S094 course: Deep Learning for Self-Driving Cars, 2017. It includes Python 2.7, TensorFlow 0.12.1 and OpenCV 3.3.0. The software stack is optimized for running on CPU.
The pre-configured and ready-to-use runtime environment for the Fast.ai's courses Practical Deep Learning for Coders, 2018 edition, part 1. It includes Python 3.6 and PyTorch 0.3.0. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.
The pre-configured and ready-to-use runtime environment for the Fast.ai's courses Practical Deep Learning for Coders, 2018 edition, part 1. It includes Python 3.6 and PyTorch 0.3.0. The software stack is optimized for running on CPU.
The pre-configured and ready-to-use runtime environment for the Open Machine Learning Course, 2018. It includes Python 3.6, TensorFlow 1.4, Keras 2, XGBoost, LightGBM and Vowpal Wabbit. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.
The pre-configured and ready-to-use runtime environment for the Open Machine Learning Course, 2018. It includes Python 3.6, TensorFlow 1.4, Keras 2, XGBoost, LightGBM and Vowpal Wabbit. The software stack is optimized for running on CPU.
A pre-configured and fully integrated software stack with TensorFlow, an open source software library for machine learning, Python 2.7, and Jupiter Notebook, a browser-based interactive notebook for programming, mathematics, and data science. The stack is designed for research and development tasks and optimized for running on NVidia GPU.
