I want to vent a bit about the current landscape of Deep Learning frameworks / libraries / middlewares. Why do all of them have different file formats? Its like having web browsers that expect different types of responses from web servers. Hua? Neural Networks are pretty simple. Its a graph with weights. Basically an ordered set of linear algerbra operations. Now, with dropout and resampling we see amazing non-linear results but the core of what the network is / does isn’t that complicated.
What is a NVIDIA Jetson TX1 Development Kit? In short is a quad-core ARM and a 256-core Maxwell GPU packed into a single chip with pretty much all the accessories you need in a breakout board. Here is an unboxing video. I have had the NVIDIA Jetson TK1 Development Kit for a while now and the TX1 it is quite a bit more powerful, though 3x the cost. Getting Torch Setup First step is to get the TX1 flashed with the latest Linux 4 Tegra (L4T) along with the full CUDA toolchain.
OSX Pickup dependencies: brew tap caskroom/cask brew cask install anaconda brew install zeromq openssl luarocks install luacrypto OPENSSL_DIR=/usr/local/opt/openssl/ Add to your shell if you use zsh: echo "export PATH=\"$HOME/anaconda3/bin:$PATH\"" >> ~/.zshrc source ~/.zshrc Or if you use bash: echo "export PATH=\"$HOME/anaconda3/bin:$PATH\"" >> ~/.bashrc source ~/.bashrc Now we can install Jupyter and iTorch conda install jupyter git clone https://github.com/facebook/iTorch.git cd iTorch && luarocks make && popd Hello World Lets grab this great example:
Starting with John Kaufhold‘s DeepLearning presentation (pdf) to the DataCommunity DC back in 2014. Marvin Galvin did a blog post covering the highlights. Here is the video: With that history lesson, I want to zoom down a level of detail and understand all these papers. Here are the papers in a handy script to download them. View the code on Gist.