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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AI Tech Talk: An Overview of AI on the AWS Platform

New on the AWS #AI Blog,

  • AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs.
  • For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning.
  • For more in-depth deep learning applications, the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale.
  • Launch instances of the AMI, pre-installed with open source deep learning frameworks (Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras), to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques; all backed by auto-scaling clusters of GPU-based instances.
  • Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of the managed AI services, the AI Platform offerings, and the AI Frameworks you can run on the AWS Cloud.

AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning.
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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.

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Deep Learning and the Artificial Intelligence Revolution White Paper

Wondering what the differences are between #AI, #MachineLearning & #DeepLearning? Find out:

  • Deep learning and AI have moved well beyond science fiction into the cutting edge of internet and enterprise computing.
  • Access to more computational power in the cloud, advancement of sophisticated algorithms, and the availability of funding are unlocking new possibilities unimaginable just five years ago.
  • But it’s the availability of new, rich data sources that is making deep learning real.

Deep Learning and the Artificial Intelligence Revolution White Paper
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What Canada is Doing to Retain Its Lead in Artificial Intelligence

What #Canada is Doing to Retain It's Lead in #ArtificialIntelligence 

 #fintech #AI

  • You see, strong public support for research programs and world class expertise at Canadian universities has helped propel Canada to a position as leader in artificial intelligence and deep learning research and use.
  • Canadian talent and ideas are in high demand around the world—but activity needs to remain in Canada to harness the benefits from artificial intelligence.
  • So to retain and attract top academic talent, and to increase the number of post-graduate trainees and researchers studying artificial intelligence and deep learning, our latest budget proposes to provide $125 million to launch a Pan-Canadian Artificial Intelligence Strategy for research and talent.
  • The Strategy will promote collaboration between Canada’s main centres of expertise in Montréal, Toronto-Waterloo and Edmonton and position Canada as a world-leading destination for companies seeking to invest in artificial intelligence and innovation.
  • A leader in the area of artificial intelligence, the Canadian Institute for Advanced Research will be responsible for administering the funding for the new Strategy.

What is your stance on AI research given Canada’s privileged position in the field? originally appeared on Quora – the place to gain and share knowledge,…
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List of Free Must-Read Books for Machine Learning

List of Free Must-Read Books for #MachineLearning

  • In this article, we have listed some of the best free machine learning books that you should consider going through (no order in particular).
  • This book holds the prologue to statistical learning methods along with a number of R labs included.
  • The online version of the book is available now for free.
  • For the mathematics- savvy people, this is one of the most recommended books for understanding the magic behind Machine Learning.
  • This book has a lot to offer to the Engineering and Computer Science students studying Machine Learning and Artificial Intelligence.

In this article, we have listed some of the best free machine learning books that you should consider going through (no order in particular).
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