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.
Continue reading “AI Tech Talk: An Overview of AI on the AWS Platform”

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.
  • You need to be a member of Data Science Central to add comments!
  • 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