Facebook taps deep learning for customized feeds

#Facebook taps deep learning for customized feeds  #MMA

  • The social network does machine learning one better, applying advanced computer learning techniques to…
  • Deep learning is a very generic technique Tulloch said.
  • The company must also deal with content posted in more than 100 languages daily complicating classic machine learning, Tulloch said.
  • But deep learning has pushed the state of the art forward in computer vision tasks, Tulloch said, including with classifying videos.
  • High-level understanding of content helps Facebook surface visual memories.

The social network does machine learning one better, applying advanced computer learning techniques to cater to users’ interests
Continue reading “Facebook taps deep learning for customized feeds”

Deep learning tutorial on Caffe technology : basic commands, Python and C++ code.

#Deeplearning tutorial on #Caffe technology : basic commands, #Python and C++ code

  • ‘Data’ : for data saved in a LMDB database, such as before
  • net.blobs[‘data’] contains input data, an array of shape (1, 1, 100, 100) net.blobs[‘conv’] contains computed data in layer ‘conv’ (1, 3, 96, 96)
  • Learn : solve the params on training data
  • /data/ilsvrc12/get_ilsvrc_aux.sh #have a look at the model python python/draw_net.py models/bvlc_reference_caffenet/deploy.prototxt caffenet.png open caffenet.png
  • Let’s create a layer to add a value.

Read the full article, click here.


@deeplearningldn: “#Deeplearning tutorial on #Caffe technology : basic commands, #Python and C++ code”


Disrupting SASU. Christopher Bourez


Deep learning tutorial on Caffe technology : basic commands, Python and C++ code.

Deep learning tutorial on Caffe technology : basic commands, Python and C++ code.

#DeepLearning tutorial on Caffe technology: basic commands, #Python and C++ code

  • ‘Data’ : for data saved in a LMDB database, such as before
  • net.blobs[‘data’] contains input data, an array of shape (1, 1, 100, 100) net.blobs[‘conv’] contains computed data in layer ‘conv’ (1, 3, 96, 96)
  • Learn : solve the params on training data
  • /data/ilsvrc12/get_ilsvrc_aux.sh #have a look at the model python python/draw_net.py models/bvlc_reference_caffenet/deploy.prototxt caffenet.png open caffenet.png
  • Let’s create a layer to add a value.

Read the full article, click here.


@kdnuggets: “#DeepLearning tutorial on Caffe technology: basic commands, #Python and C++ code”


Disrupting SASU. Christopher Bourez


Deep learning tutorial on Caffe technology : basic commands, Python and C++ code.