Artificial Intelligence Could Help Diagnose Tuberculosis

Research, CUDA, cuDNN, GeForce, Healthcare & Life Sciences, Image Recognition, Machine Learning & Artificial Intelligence

Nadeem Mohammad, posted Apr 25 2017

Researchers from Thomas Jefferson University Hospital in Philadelphia are training deep learning models to identify tuberculosis (TB) in an effort to help patients in regions with limited access to radiologists. TB is one of the top ten causes of death worldwide with nearly two million deaths in 2016. TB can be identified on chest imaging,

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Photo Editing with Generative Adversarial Networks (Part 2)

Features, Deep Learning, DIGITS, GAN, TensorFlow

Nadeem Mohammad, posted Apr 24 2017

In part 1 of this series I introduced Generative Adversarial Networks (GANs) and showed how to generate images of handwritten digits using a GAN. In this post I will do something much more exciting: use Generative Adversarial Networks to generate images of celebrity faces. I am going to use CelebA [1], a dataset of 200,000 […]

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Deep Reinforcement Learning Agent Beats Atari Games

Research, Cluster, CUDA, cuDNN, GeForce, Higher Education / Academia, Machine Learning & Artificial Intelligence, Media & Entertainment

Nadeem Mohammad, posted Apr 21 2017

Stanford researchers developed the first deep reinforcement learning agent that learns to beat Atari games with the aid of natural language instructions. “Humans do not typically learn to interact with the world in a vacuum, devoid of interaction with others, nor do we live in the stateless, single-example world of supervised learning,” mentioned the researchers

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Edit Photos with GANs

Research, DIGITS, Image Recognition, Machine Learning & Artificial Intelligence

Nadeem Mohammad, posted Apr 20 2017

In machine learning, a generative model learns to generate samples that have a high probability of being real samples like the samples from the training dataset. Generative Adversarial Networks (GANs) are a very hot topic in Machine Learning. A typical GAN comprises two agents: a Generator G that produces samples, and a Discriminator D that receives samples from both G and

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Photo Editing with Generative Adversarial Networks (Part 1)

Features, Deep Learning, DIGITS, GAN, TensorFlow

Nadeem Mohammad, posted Apr 20 2017

Adversarial training (also called GAN for Generative Adversarial Networks), and the variations that are now being proposed, is the most interesting idea in the last 10 years in ML, in my opinion. – Yann LeCun, 2016 [1]. You heard it from the Deep Learning guru: Generative Adversarial Networks [2] are a very hot topic in […]

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