Deep Learning AMI for Ubuntu v1.3_Apr2017 Now Supports Caffe2

Deep Learning AMI on Amazon Web Services quickly added Caffe2 along with TensorFlow & others

  • We are excited to announce that the AWS Deep Learning AMI for Ubuntu now supports the newly launched Caffe2 project led by Facebook.
  • The Deep Learning AMI v1.3_Apr2017 for Ubuntu provides a stable, secure, and high-performance execution environment for deep learning applications running on Amazon EC2.
  • The AWS Deep Learning AMI (available for Amazon Linux and Ubuntu) and the AWS Deep Learning CloudFormation Template let you quickly deploy and run any of the major deep learning frameworks at any scale.
  • The AWS Deep Learning AMI is provided and supported by Amazon Web Services, for use on Amazon EC2.
  • There is no additional charge for the AWS Deep Learning AMI – you only pay for the AWS resources needed to store and run your applications.

We are excited to announce that the AWS Deep Learning AMI for Ubuntu now supports the newly launched Caffe2 project led by Facebook. AWS is the best and most open place for developers to run deep learning, and the addition of Caffe2 adds yet another choice. To learn more about Caffe2, check out the the Caffe2 developer site or the GitHub repository.
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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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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

Yann LeCun’s answer to What are your recommendations for self-studying machine learning?

Facebook director of AI research @ylecun’s recommendations for self-studying deep learning

  • A recent series of 8 lectures on deep learning that I gave at Collège de France in Paris.
  • You can get a broad idea of deep what deep learning is about through tutorial lectures that are available from the Web.
  • Nikhil Dandekar , worked on machine learning at Microsoft, Foursquare and Quora
  • my 2015 course on Deep Learning at NYU: deeplearning2015:schedule | CILVR Lab @ NYU (unfortunately, the videos of the lectures had to be taken down due to stupid legal reasons, but the slides are there).
  • There is tons of on-line material, tutorials and courses on ML, including Coursera lectures.

Read the full article, click here.


@kylebrussell: “Facebook director of AI research @ylecun’s recommendations for self-studying deep learning”


I’ll respond more specifically for deep learning. You can get a broad idea of deep what deep learning is about through tutorial lectures that are available from the Web. Most notably:


Yann LeCun’s answer to What are your recommendations for self-studying machine learning?