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aise / Stanford CS231n course Python 2 NVidia GPU (Latest versions)

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.

stanford-cs231n-course:1617spring, tensorflow:1.5.0, pytorch:0.3.0, keras:2.1.2, python:2.7.14, cuda:9.0.176, cudnn:7.0.5, cuda_only-nvidia_drivers:384.111

aise / Stanford CS231n course Python 2 CPU only (Latest versions)

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.

stanford-cs231n-course:1617spring, tensorflow:1.5.0, pytorch:0.3.0, keras:2.1.2, python:2.7.14

aise / Stanford CS231n course NVidia GPU (Original versions)

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.

stanford-cs231n-course:1617spring, tensorflow:1.0.1, pytorch:0.1.11, keras:2.0.2, python:3.5.4, cuda:8.0.61, cudnn:5.1.10, cuda_only-nvidia_drivers:384.111

aise / Stanford CS231n course CPU only (Original versions)

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.

stanford-cs231n-course:1617spring, tensorflow:1.0.1, pytorch:0.1.11, keras:2.0.2, python:3.5.4

aise / Open Machine Learning Course 2018 for 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 stack also includes CUDA and cuDNN, and is optimized for running on NVidia GPU.

mlcourse_open-course:2018, cuda:8.0.61, cudnn:7.0.5, cuda_only-nvidia_drivers:384.111

aise / Open Machine Learning Course 2018 for 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 software stack is optimized for running on CPU.

mlcourse_open-course:2018

xunwang / new

caffe:1.0, python:2.7.14, opencv:3.3.0, selfmanagement_preset

pc756322762 / tensorflow14_python2_cpu

tensorflow:1.4.0, python:2.7.14, jupyter_notebook:1.0.0, opencv:3.3.0, development_preset

xunwang / caffe_cpuonly

caffe:1.0, python:2.7.14, opencv:3.3.0, development_preset

aise / TensorFlow 1.4 Python 2.7 NVidia GPU Lab

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.

tensorflow:1.4.0, python:2.7.14, jupyter_notebook:1.0.0, cuda:9.0.176, cudnn:7.0.5, cuda_only-nvidia_drivers:384.98, selfmanagement_preset, development_preset