Caffe2 Learning Framework and GPU Acceleration

Run #deeplearning training with #Caffe2 to get a speedup on NVIDIA GPUs. Get started today:

  • Caffe2 is a deep learning framework enabling simple and flexible deep learning.
  • Built on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind, allowing for a more flexible way to organize computation.
  • Caffe2 aims to provide an easy and straightforward way for you to experiment with deep learning by leveraging community contributions of new models and algorithms.
  • Caffe2 comes with native Python and C++ APIs that work interchangeably so you can prototype quickly now, and easily optimize later.
  • Caffe2 is accelerated with the latest NVIDIA Pascal™ GPUs and scales across multiple GPUs within a single node.

Run deep learning training with Caffe2 up to 3x faster on the latest NVIDIA Pascal GPUs.
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NVIDIA, Facebook Supercharge Caffe2 Deep Learning Framework

.@NVIDIA and @Facebook team up to supercharge the new #Caffe2 #deeplearning framework:

  • NVIDIA and Facebook today announced the result of our joint work to advance artificial intelligence with Caffe2, a new AI deep learning framework contributed by Facebook to the open-source community.
  • NVIDIA and Facebook are delivering AI acceleration through our work on the Caffe2 deep learning framework.
  • Thanks to our joint engineering, we’ve fine-tuned Caffe2 from the ground up to take full advantage of the NVIDIA GPU deep learning platform.
  • It delivers near-linear scaling of deep learning training with 57x throughput acceleration on eight networked Facebook Big Basin AI servers with 64 NVIDIA Tesla P100 GPU accelerators.
  • As part of the companies’ collaboration, the NVIDIA DGX-1 AI supercomputer will be the first AI system to offer Caffe2 within the optimized software stack for deep learning.

NVIDIA and Facebook today announced the result of our joint work to advance artificial intelligence with Caffe2, a new AI deep learning framework.
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An Overview of Python Deep Learning Frameworks

#ICYMI An Overview of Python Deep Learning Frameworks

  • I recently stumbled across an old Data Science Stack Exchange answer of mine on the topic of the “Best Python library for neural networks”, and it struck me how much the Python deep learning ecosystem has evolved over the course of the past 2.5 years.
  • Since Theano aims first and foremost to be a library for symbolic mathematics, Lasagne offers abstractions on top of Theano that make it more suitable for deep learning.
  • Similar to Lasagne, Blocks is a shot at adding a layer of abstraction on top of Theano to facilitate cleaner, simpler, more standardized definitions of deep learning models than writing raw Theano.
  • More recently, the TensorFlow team decided to incorporate support for Keras, the next deep learning library on our list.
  • It’s a loose port of Lua’s Torch library to Python, and is notable because it’s backed by the Facebook Artificial Intelligence Research team (FAIR), and because it’s designed to handle dynamic computation graphs — a feature absent from the likes of Theano, TensorFlow, and derivatives.


Read this concise overview of leading Python deep learning frameworks, including Theano, Lasagne, Blocks, TensorFlow, Keras, MXNet, and PyTorch.

Continue reading “An Overview of Python Deep Learning Frameworks”

An Overview of Python Deep Learning Frameworks

#ICYMI An Overview of #Python #DeepLearning Frameworks

  • I recently stumbled across an old Data Science Stack Exchange answer of mine on the topic of the “Best Python library for neural networks”, and it struck me how much the Python deep learning ecosystem has evolved over the course of the past 2.5 years.
  • Since Theano aims first and foremost to be a library for symbolic mathematics, Lasagne offers abstractions on top of Theano that make it more suitable for deep learning.
  • Similar to Lasagne, Blocks is a shot at adding a layer of abstraction on top of Theano to facilitate cleaner, simpler, more standardized definitions of deep learning models than writing raw Theano.
  • More recently, the TensorFlow team decided to incorporate support for Keras, the next deep learning library on our list.
  • It’s a loose port of Lua’s Torch library to Python, and is notable because it’s backed by the Facebook Artificial Intelligence Research team (FAIR), and because it’s designed to handle dynamic computation graphs — a feature absent from the likes of Theano, TensorFlow, and derivatives.


Read this concise overview of leading Python deep learning frameworks, including Theano, Lasagne, Blocks, TensorFlow, Keras, MXNet, and PyTorch.

Continue reading “An Overview of Python Deep Learning Frameworks”

An Overview of Python Deep Learning Frameworks

#ICYMI An Overview of #Python #DeepLearning Frameworks

  • I recently stumbled across an old Data Science Stack Exchange answer of mine on the topic of the “Best Python library for neural networks”, and it struck me how much the Python deep learning ecosystem has evolved over the course of the past 2.5 years.
  • Since Theano aims first and foremost to be a library for symbolic mathematics, Lasagne offers abstractions on top of Theano that make it more suitable for deep learning.
  • Similar to Lasagne, Blocks is a shot at adding a layer of abstraction on top of Theano to facilitate cleaner, simpler, more standardized definitions of deep learning models than writing raw Theano.
  • More recently, the TensorFlow team decided to incorporate support for Keras, the next deep learning library on our list.
  • It’s a loose port of Lua’s Torch library to Python, and is notable because it’s backed by the Facebook Artificial Intelligence Research team (FAIR), and because it’s designed to handle dynamic computation graphs — a feature absent from the likes of Theano, TensorFlow, and derivatives.


Read this concise overview of leading Python deep learning frameworks, including Theano, Lasagne, Blocks, TensorFlow, Keras, MXNet, and PyTorch.

Continue reading “An Overview of Python Deep Learning Frameworks”

An Overview of Python Deep Learning Frameworks

#ICYMI An Overview of #Python #DeepLearning Frameworks

  • I recently stumbled across an old Data Science Stack Exchange answer of mine on the topic of the “Best Python library for neural networks”, and it struck me how much the Python deep learning ecosystem has evolved over the course of the past 2.5 years.
  • Since Theano aims first and foremost to be a library for symbolic mathematics, Lasagne offers abstractions on top of Theano that make it more suitable for deep learning.
  • Similar to Lasagne, Blocks is a shot at adding a layer of abstraction on top of Theano to facilitate cleaner, simpler, more standardized definitions of deep learning models than writing raw Theano.
  • More recently, the TensorFlow team decided to incorporate support for Keras, the next deep learning library on our list.
  • It’s a loose port of Lua’s Torch library to Python, and is notable because it’s backed by the Facebook Artificial Intelligence Research team (FAIR), and because it’s designed to handle dynamic computation graphs — a feature absent from the likes of Theano, TensorFlow, and derivatives.


Read this concise overview of leading Python deep learning frameworks, including Theano, Lasagne, Blocks, TensorFlow, Keras, MXNet, and PyTorch.

Continue reading “An Overview of Python Deep Learning Frameworks”

15 Deep Learning Libraries

15 Deep Learning Libraries?

  • Are 15 libraries in various languages to help implement your deep learning algorithm.
  • Its powerful, flexible and intuitive and is considered as the flexible framework for Deep Learning.
  • It comes with rich set of deep learning libraries as a part of machine learning libraries.
  • You need to be a member of Data Science Central to add comments!
  • Mocha is a Deep Learning framework for Julia, inspired by the C++ framework Caffe.

 

Here are 15 libraries in various languages to help implement your deep learning algorithm.

Theano is a python library for defining and evaluating mathema…
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