Torch is a scientific computing framework for LuaJIT.

• comes with a large ecosystem of community-driven packages in ML, CV, etc
• is easy to use and efficient
• can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner

• Go through Torch documentation and try using useful functions.
• Go through other useful modules and try to use them.
• Start implementing something real
  • Read and process process data as needed by the network.
  • Read Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation and implement a GRU.
  • Read LSTM: A Search Space Odyssey and implement LSTM.
  • Use Tree LSTM in place of LSTM. Paper Code Slides
• Go through Penlight documentation
• Go through Moses
• Go through nn module documentation and check how some to the functionality is implemented

• Curated list of Torch tutorials: link
• Awesome Recurrent Neural Networks: link
• Neural Network Papers: link
• Awesome Deep Learning: link

• Word2Vec in Torch: link link
• CharRNN: link
• CharCNN: link
• RNN: link
• RNN Playground: link
• facebook - bAbI tasks: link
• facebook - Neural Attention Model for Abstractive Summarization: link
• Kaggle - Denoising Dirty Documents: link
• Kaggle - Grasp and Lift Detection: link