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Notebooks for MXNet

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I wrote a following script to predict image label for resent 18. ``` import mxnet as mx model_name = 'resnet-18' path='http://data.mxnet.io/models/imagenet/resnet/' [mx.test_utils.download(path+'18-layers/resnet-18-symbol.json'), mx.test_utils.download(path+'18-layers/resnet-18-0000.params'), mx.test_utils.download(path+'synset.txt')] sym, arg_params, aux_params = mx.model.load_checkpoint(model_name, 0)...

Is this project still being maintained? The latest update seems to be about two years ago.

I run the char_lstm example on my mbp. As the computer has no NVIDIA card,so I change the notebook to use cpu(0), the change is at below: model = mx.model.FeedForward(...

import sys sys.path.append("../../tests/python/common") import get_data ImportError Traceback (most recent call last) in () 1 import sys 2 sys.path.append("../../tests/python/common") ----> 3 import get_data ImportError: No module named get_data

Iam learning to work on ConvLSTM and ConvGRU. Kindly suggest some examples to work on these modules

In https://github.com/dmlc/mxnet-notebooks/blob/master/python/tutorials/understanding_vanish_gradient.ipynb: from tensorboard import summary from tensorboard import FileWriter Fails I tested it on Amazon Ubuntu. @zihaolucky

How to install mxnet-notebooks on Mac OS X, can I just us the example script proved for Ubuntu to do the installation? Thanks Here is an example script to install...

In https://github.com/dmlc/mxnet-notebooks/blob/master/python/rnn/lstm.ipynb, some of the images that at in the source code and are referenced in the text do not render. Specifically, https://raw.githubusercontent.com/antinucleon/web-data/master/mxnet/notebook/recurrent.jpg and https://raw.githubusercontent.com/antinucleon/web-data/master/mxnet/notebook/unroll.jpg do not render. Both of...

used lstm cell instead of fusedRNN for simplicity.

Hi! I followed this [notebook](https://github.com/dmlc/mxnet-notebooks/blob/master/python/how_to/predict.ipynb) to extract image feature using pre-trained CNN model. I wonder do we need to subtract mean pixel values of imagenet before feature extraction. Other deep...