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Merge pull request #3 from jisungk/master
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Minor typo fixes in dream.ipynb
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znah committed Jul 3, 2015
2 parents 7704c99 + 9d2eed4 commit 9bed900
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4 changes: 2 additions & 2 deletions dream.ipynb
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"It'll be interesting to see what imagery people are able to generate using the described technique. If you post images to Google+, Facebook, or Twitter, be sure to tag them with **#deepdream** so other researchers can check them out too.\n",
"\n",
"##Dependences\n",
"This notebook is designed to have as few depencences as possible:\n",
"This notebook is designed to have as few dependencies as possible:\n",
"* Standard Python scientific stack: [NumPy](http://www.numpy.org/), [SciPy](http://www.scipy.org/), [PIL](http://www.pythonware.com/products/pil/), [IPython](http://ipython.org/). Those libraries can also be installed as a part of one of scientific packages for Python, such as [Anaconda](http://continuum.io/downloads) or [Canopy](https://store.enthought.com/).\n",
"* [Caffe](http://caffe.berkeleyvision.org/) deep learning framework ([installation instructions](http://caffe.berkeleyvision.org/installation.html)).\n",
"* Google [protobuf](https://developers.google.com/protocol-buffers/) library that is used for Caffe model manipulation."
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"source": [
"## Loading DNN model\n",
"In this notebook we are going to use a [GoogLeNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet) model trained on [ImageNet](http://www.image-net.org/) dataset.\n",
"Feel free to experiment with other models from Caffe [Model Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo). One particulary interesting [model](http://places.csail.mit.edu/downloadCNN.html) was trained in [MIT Places](http://places.csail.mit.edu/) dataset. It produced many visuals from the [original blog post](http://googleresearch.blogspot.ch/2015/06/inceptionism-going-deeper-into-neural.html)."
"Feel free to experiment with other models from Caffe [Model Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo). One particularly interesting [model](http://places.csail.mit.edu/downloadCNN.html) was trained in [MIT Places](http://places.csail.mit.edu/) dataset. It produced many visuals from the [original blog post](http://googleresearch.blogspot.ch/2015/06/inceptionism-going-deeper-into-neural.html)."
]
},
{
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