The AI Revolution Is Eating Software: NVIDIA Is Powering It

Find out how we're immersed in this extraordinary momentum of the #AI revolution:

  • Volta features a new numeric format and CUDA instruction that perform 4×4 matrix operations – an elemental deep learning operation – at super-high speeds

    Each Volta GPU is 120 teraflops.

  • It’s great to see the two leading teams in AI computing race while we collaborate deeply across the board – tuning TensorFlow performance, and accelerating the Google cloud with NVIDIA CUDA GPUs.
  • It provides a 5x improvement over Pascal, the current-generation NVIDIA GPU architecture, in peak teraflops, and 15x over the Maxwell architecture, launched just two years ago – well beyond what Moore’s law would have predicted.
  • Such leaps in performance have drawn innovators from every industry, with the number of startups building GPU-driven AI services growing more than 4x over the past year to 1,300.
  • To help innovators move seamlessly to cloud services such as these, at GTC we launched the NVIDIA GPU Cloud platform, which contains a registry of pre-configured and optimized stacks of every framework.

At this year’s GTC Conference, NVIDIA showed how it is delivering AI for every computing platform, every deep learning framework. Read more.
Continue reading “The AI Revolution Is Eating Software: NVIDIA Is Powering It”

Up to Speed on Deep Learning: July Update, Part 2

Up to Speed on #DeepLearning: July Update, Part 2

  • The series introduces machine learning in four detailed segments: spanning an introduction to machine learning to an in-depth convolutional neural network implementation for face recognition.
  • Part 4 of Adam’s series Machine Learning is Fun.
  • Are the three prior parts: part 1 , part 2 , and part 3 .
  • Isaac’s background is in machine learning & artificial intelligence, having been previously an entrepreneur and data scientist.
  • Learn about artificial neural networks and how they’re being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc.


Check out this second installation of deep learning stories that made news in July. See if there are any items of note you missed.

Continue reading “Up to Speed on Deep Learning: July Update, Part 2”

Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks — Medium

Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks — Medium

  • More data makes the problem harder for our neural network to solve, but we can compensate for that by making our network bigger and able to learn more complicated patterns.
  • We need to be smarter about how we process images into our neural network.
  • But now we want to process images with our neural network.
  • Our program can now recognize birds in images!
  • Step 1: Break the image into overlapping image tiles

Read the full article, click here.


@MikeTamir: “Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks — Medium”


Update: Machine Learning is Fun! Part 4 is now available!


Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks — Medium

Must Know Tips/Tricks in Deep Neural Networks

Must Know Tips/Tricks in Deep Neural Networks:  #abdsc #MachineLearning #DeepLearning

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  • For more articles about Neural Networks, click .
  • Must Know Tips/Tricks in Deep Neural Networks
  • Deep Neural Networks, especially Convolutional Neural Networks ( CNN ), allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.
  • They collected and concluded many implementation details for DCNNs.

Read the full article, click here.


@KirkDBorne: “Must Know Tips/Tricks in Deep Neural Networks: #abdsc #MachineLearning #DeepLearning”


This article was posted by Xiu-Shen Wei.  Xiu-Shen Wei is a 2nd-year Ph.D. candidate of Department of Computer Science and Technology in Nanjing University and…


Must Know Tips/Tricks in Deep Neural Networks

Deep Learning Libraries by Language

#DeepLearning Libraries by Language

  • darch package can be used for generating neural networks with many layers (deep architectures).
  • Convnet.js is a Javascript library for training Deep Learning models (mainly Neural Networks) entirely in a browser.
  • DeepLearning is deep learning library, developed with C++ and python.
  • Pylearn2 is a library that wraps a lot of models and training algorithms such as Stochastic Gradient Descent that are commonly used in Deep Learning.
  • Intel® Deep Learning Framework provides a unified framework for Intel® platforms accelerating Deep Convolutional Neural Networks.

Read the full article, click here.


@analyticbridge: “#DeepLearning Libraries by Language”


Source for picture: click here
Python

Theano is a python library for defining and evaluating mathematical expressions with numerical arrays.

Keras is a mi…


Deep Learning Libraries by Language