Human Folly about “Torch-7”

index

• Namedtensor2 (nlp.seas.harvard.edu):

  Alexander Rush talks about how “tensors” in the style of #APL are central to PyTorch (#Torch-7) and proposes what he thinks is a better API. #deep-learning

  on 02019-01-21
• Deep completion (bamos.github.io):

  #image-processing content-aware fill of faces using #neural-networks; extended commentary on #Torch-7 vs. #TensorFlow

  on 02017-03-02
• Openface (cmusatyalab.github.io):

  “a Python and #Torch-7 implementation of #face-recognition with #deep-learning #neural-networks”

  on 02016-01-19
• Tensor flow terrific deep learning library (kdnuggets.com):

  a brief comparison of #TensorFlow, Facebook’s Ronan Collobert’s faster #Torch-7, the Caffe convnets library, and Yoshua Bengio’s #Theano. “Just as a Tesla is yet another four-wheeled conveyance with four doors, a steering wheel and a roof, TensorFlow appears to be the best, most convenient library for #deep-learning, more worthy of anointment.”

  on 02016-01-11
• (torch.ch):

  “A scientific computing framework for #LuaJIT” #Lua ndarrays, like Numpy, with #GPGPU stuff. And even FPGAs! "Torch 7"

  on 02015-08-18
• RNN effectiveness (karpathy.github.io):

  “The Unreasonable Effectiveness of Recurrent #Neural-Networks” for #image-recognition, #NLP language modeling (producing what looks an awful lot like Markov-chain text with slightly better context-free properties), sequential processing for directing attention over an image, etc. Lots of animations and visualizations of RNNs doing their thing. The models are written in #Lua with something called #Torch-7 and run with #GPGPU. Lots of great comments! "unreasonable RNNs"

  on 02015-08-15