mxnet-gtc-tutorial
MXNet Tutorial
An interactive tutorial on deep learning with MXNet using Jupyter Notebooks
MXNet Tutorial for NVidia GTC 2016.
131 stars
15 watching
50 forks
Language: Jupyter Notebook
last commit: over 8 years ago Related projects:
| Repository | Description | Stars |
|---|---|---|
| dmlc/mxnet.js | A JavaScript package for running deep learning models in the browser without requiring a server | 435 |
| dmlc/mxnet-memonger | A tool for optimizing deep learning models to reduce memory usage without sacrificing performance | 308 |
 deepakkumar1984/mxnet.sharp | A .NET Standard library providing C# bindings for the Apache MXNet deep learning framework | 149 |
 toddsundsted/mxnet.cr | Provides bindings for MXNet, an open source deep learning framework written in C++ | 22 |
 mrkn/mxnet.rb | A Ruby interface to MXNet's deep learning framework | 48 |
 hpcgarage/accelerated_dl_pytorch | This repository provides tutorials and demo code for accelerated deep learning with PyTorch using Jupyter Notebook. | 127 |
 ogrisel/parallel_ml_tutorial | A tutorial on parallel machine learning with scikit-learn and IPython | 1,592 |
| dmlc/gnnlens2 | An interactive visualization tool for graph neural networks | 239 |
 hardmaru/pytorch_notebooks | A collection of tutorial notebooks focused on building and training neural networks using PyTorch. | 385 |
 tensorflow/gan | Provides tools and utilities for training and evaluating Generative Adversarial Networks (GANs) in TensorFlow | 937 |
 mrmimic/data-scientist-roadmap | Creating tutorials to teach data science skills through Jupyter Notebook | 6,986 |
 songtianyi/go-mxnet-predictor | A Go library providing a direct interface to MXNet's C API for deep learning inference | 55 |
 britefury/deep-learning-tutorial-pydata | A tutorial project providing guidance on building and training deep learning models using PyData | 85 |
 liangfu/mxnet-mobilenet-v2 | Reproduction of MobileNetV2 using MXNet | 129 |
 clab/dynet_tutorial_examples | Tutorials on implementing neural networks in NLP using the DyNet toolkit | 433 |