Machine learning education
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 software stack is optimized for running on CPU.
 default
Tensorflow
Pytorch
Keras
Python
Machine learning education
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 software stack is optimized for running on CPU.
 default
Tensorflow
Pytorch
Keras
Python
Machine learning education
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, PyTorch, CUDA, and cuDNN. The software stack is optimized for running on NVIdia GPU.
 default
Tensorflow
Pytorch
Keras
Python
Cuda
Cudnn
Nvidia drivers
Machine learning education
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, PyTorch, CUDA, and cuDNN. The software stack is optimized for running on NVIdia GPU.
 default
Tensorflow
Pytorch
Keras
Python
Cuda
Cudnn
Nvidia drivers
Machine learning education
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 software stack is optimized for running on CPU.
 default
Tensorflow
Pytorch
Keras
Python
Machine learning education
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, PyTorch, CUDA, and cuDNN, used in the course. The software stack is optimized for running on NVIdia GPU.
 default
Tensorflow
Pytorch
Keras
Python
Cuda
Cudnn
Nvidia drivers