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RT : ’s Chief Scientist says may need a new programming language:…
AI  DeepLearning  Facebook  from twitter
13 hours ago by burkesquires
How to build the perfect Deep Learning Computer and save thousands of dollars
There are only 8 components to a build: GPU, CPU, Storage, Memory, CPU Cooler, Motherboard, Power, Case. The first 4 are the most important. When training, data flows from storage to memory to the…
deeplearning  build  diy 
19 hours ago by alpinegizmo
An Intuitive Explanation of Convolutional Neural Networks – the data science blog
What are Convolutional Neural Networks and why are they important? Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self driving cars. Figure 1:…
deeplearning  ml  neuralnetworks 
23 hours ago by lena
Juergen Schmidhuber's home page - Universal Artificial Intelligence - New AI - Deep Learning - Recurrent Neural Networks - Computer Vision - Object Detection - Image segmentation - Goedel Machine - Theory of everything - Algorithmic theory of everything -
Our neural nets also set numerous world records, and were the first Deep Learners to win pattern recognition contests in general (2009), History of computer vision contests won by deep CNNs on GPU the first to win object detection contests (2012), the first to win a pure image segmentation contest (2012), and the first machine learning methods to reach superhuman visual recognition performance in a contest (2011). Compare this Google Tech Talk (2011) and JS' first Deep Learning system of 1991, with a Deep Learning timeline 1962-2013. See also the history of computer vision contests won by deep CNNs on GPU since 2011.
ml  ai  deeplearning  neuralnetworks  history  computervision 
yesterday by lena
niftynet
NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can:

Get started with established pre-trained networks using built-in tools;
Adapt existing networks to your imaging data;
Quickly build new solutions to your own image analysis problems.
The code is available via GitHub, or you can quickly get started with the PyPI module available here.
deeplearning  machinelearning  tensorflow  medicine  imaging  science 
yesterday by RBarnard
DLTK/DLTK: Deep Learning Toolkit for Medical Image Analysis
DLTK is a neural networks toolkit written in python, on top of TensorFlow. It is developed to enable fast prototyping with a low entry threshold and ensure reproducibility in image analysis applications, with a particular focus on medical imaging. Its goal is to provide the community with state of the art methods and models and to accelerate research in this exciting field.
healthcare  datascience  deeplearning  machinelearning  neuralnetwork  MRI  medicine 
yesterday by RBarnard

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