An overview of gradient descent optimization algorithms

An overview of gradient descent optimization algorithms #deeplearning #abdsc

  • We will consider additional strategies that are helpful for optimizing gradient descent.
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  • We will also take a short look at algorithms and architectures to optimize gradient descent in a parallel and distributed setting.
  • Every state-of-the-art Deep Learning library contains implementations of various algorithms to optimize gradient descent (e.g. lasagne’s , caffe’s , and keras’ documentation).
  • If you are unfamiliar with gradient descent, you can find a good introduction on optimizing neural networks .

This article was written by Sebastian Ruder. Sebastian is a PhD student in Natural Language Processing and a research scientist at AYLIEN. He blogs about Machi…
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Deep Learning Frameworks

New #cuDNN 5.1, 2.7x faster training of #deeplearning networks with 3x3 convolutions.

  • Deep learning course: Getting Started with the Caffe Framework
  • Choose a deep learning framework from the list below, download the supported version of cuDNN and follow the instructions on the framework page to get started.
  • Chainer is a deep learning framework that’s designed on the principle of define-by-run.
  • Caffe is a deep learning framework made with expression, speed, and modularity in mind.

Read the full article, click here.


@GPUComputing: “New #cuDNN 5.1, 2.7x faster training of #deeplearning networks with 3×3 convolutions.”


The NVIDIA Deep Learning SDK accelerates widely-used deep learning frameworks such as Caffe, CNTK, TensorFlow, Theano and Torch as well as many other deep learning applications. Choose a deep learning framework from the list below, download the supported version of cuDNN and follow the instructions on the framework page to get started.


Deep Learning Frameworks

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.

GitHub

Deep Draw: Generating class visualizations with Caffe #Python Jupyter Notebook #DeepLearning

  • Notebook example of how to generate class visualizations with Caffe
  • The repository also includes some code examples of drawing with the class visualizations, as described in this blogpost , in the folder “/other”.
  • Before running the ipython notebooks, you’ll also need to download the bvlc_googlenet model , and insert the path of the pycaffe installation into pycaffe_path and the model path to the googlenet model into model_path .
  • If you create some cool work or visualizations based on this code, let me know via twitter !
  • The code was based on the deepdream code shared by Google, as well as some modifications kindly shared by Kyle McDonald.

Read the full article, click here.


@kdnuggets: “Deep Draw: Generating class visualizations with Caffe #Python Jupyter Notebook #DeepLearning”


deepdraw – Notebook example of how to generate class visualizations with Caffe


GitHub