Appliances
Course mit
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.
Course
Cuda
Cudnn
Nvidia drivers
Course fast ai
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.
Course
Cuda
Cudnn
Nvidia drivers
Course fast ai
The pre-configured and ready-to-use runtime environment for the Fast.ai's courses Practical Deep Learning for Coders, 2017 edition, part 2. It includes Python 2.7, Theano 0.8, TensorFlow 1.0 and Keras 1.1. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.
Course
Cuda
Cudnn
Nvidia drivers
Course fast ai
The pre-configured and ready-to-use runtime environment for the Fast.ai's courses Practical Deep Learning for Coders, 2017 edition, part 1. It includes Python 2.7, Theano 0.8 and Keras 1.1. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.
Course
Cuda
Cudnn
Nvidia drivers
Course stanford
The pre-configured and ready-to-use runtime environment for the CS231n course - Convolutional Neural Networks for Visual Recognition, Stanford University, Spring 2017. It includes latest versions of Python 3, TensorFlow, and PyTorch. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.
Course
Tensorflow
Pytorch
Keras
Python
Cuda
Cudnn
Nvidia drivers
Course stanford
The pre-configured and ready-to-use runtime environment for the CS231n course - Convolutional Neural Networks for Visual Recognition, Stanford University, Spring 2017. It includes latest versions of Python 2, TensorFlow, and PyTorch. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.
Course
Tensorflow
Pytorch
Keras
Python
Cuda
Cudnn
Nvidia drivers
Course stanford
The pre-configured and ready-to-use runtime environment for the CS231n course - Convolutional Neural Networks for Visual Recognition, Stanford University, Spring 2017. It includes original (old) versions of Python, TensorFlow, and PyTorch, used in the course. The stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.
Course
Tensorflow
Pytorch
Keras
Python
Cuda
Cudnn
Nvidia drivers
Machine learning education
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.
Course
Cuda
Cudnn
Nvidia drivers