🔹StyleGAN2

This article explores changes made in StyleGAN2 such as weight demodulation, path length regularization and removing progressive growing!

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📌Via: @cedeepleraning

https://towardsdatascience.com/stylegan2-ace6d3da405d

#GANs
#deeplearning
#cnn
#neuralnetworks
#machinelearning

Deepmind’s paper on solving mathematical equations

https://www.youtube.com/watch?v=f9z1I_81_Q4

📌 Via: @cedeeplearning
Other social media: https://linktr.ee/cedeeplearning

👇🏻👇🏻Using Deep Convolutional Neural Networks for Neonatal Brain Image Segmentation

📌Via: @cedeeplearning
📌Social media: https://linktr.ee/cedeeplearning

#cnn
#deeplearning
#neuralnetworks

🔹Using Deep Convolutional Neural Networks for Neonatal Brain Image Segmentation

Authors:
Yang Ding, Rolando Acosta

Deep learning neural networks are especially potent at dealing with structured data, such as images and volumes. Both modified LiviaNET and HyperDense-Net performed well at a prior competition segmenting 6-month-old infant magnetic resonance images, but neonatal cerebral tissue type identification is challenging given its uniquely inverted tissue contrasts. The current study aims to evaluate the two architectures to segment neonatal brain tissue types at term equivalent age.
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📌Via: @cedeeplearning
📌Social media: https://linktr.ee/cedeeplearning

link: http://www.thetalkingmachines.com/article/using-deep-convolutional-neural-networks-neonatal-brain-image-segmentation

#deeplearning
#neuralnetworks
#machinelearning
#cnn

🔹Exploring Nature-Inspired Robot Agility

Posted by Xue Bin (Jason) Peng, Student Researcher and Sehoon Ha, Research Scientist, Robotics at Google
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📌Via: @cedeeplearning

https://youtu.be/lKYh6uuCwRY

#machinelearning
#deeplearning
#neuralnetworks
#robotics
#AI

🔹How to Build Your Own Deep Learning Box

Want to build an affordable deep learning box and get all the required software installed? Read on for a proper overview.

Credit: By Hui Han Chin, DSO National Laboratories, Singapore.
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📌Via: @cedeeplearning

https://www.kdnuggets.com/2016/06/build-deep-learning-box.html

🎧 Nanotronics Brings Deep Learning to Precision Manufacturing - Ep. 109
(Podcast)

Matthew Putman, Ep.109’s guest on the AI Podcast, knows that the devil is in the details. That’s why he’s the co-founder and CEO of Nanotronics, a Brooklyn-based company providing precision manufacturing enhanced by AI, automation and 3D imaging.
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📌Via: @cedeeplearning

https://soundcloud.com/theaipodcast/ai-nanotronics-matthew-putman-3

🔻More Performance Evaluation Metrics for Classification Problems You Should Know

When building and optimizing your classification model, measuring how accurately it predicts your expected outcome is crucial. However, this metric alone is never the entire story, as it can still offer misleading results. That's where these additional performance evaluations come into play to help tease out more meaning from your model.

Credit: By Clare Liu
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📌Via: @cedeeplearning

https://www.kdnuggets.com/2020/04/performance-evaluation-metrics-classification.html

#machinelearning
#classification
#recall
#precision

Perform cross-modal translation from "in-the-wild'' monologue speech of a single speaker to their hand and arm motion.
The project website with video, code and data can be found at http://people.eecs.berkeley.edu/~shiry/speech2gesture.

* CVPR 2019

✅Via: @cedeeplearning
✅ Other social media handles: https://linktr.ee/cedeeplearning

Pixel Recurrent Neural Network

Pixel RNN sequentially predicts the pixels in an image along the two spatial dimensions. The method models the discrete probability of the raw pixel values and encodes the complete set of dependencies in the image.
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Paper: https://arxiv.org/abs/1601.06759
Via: @CEdeeplearning 📌
Other social media: https://linktr.ee/cedeeplearning
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#pixelrnn #generativemodel #computervision #rnn #cnn #neuralnetworks #deeplearning #machinelearning

“Facts are stubborn things, but statistics are pliable.”
― Mark Twain

📌Via: @cedeeplearning

🔹Using machine learning to analyze whole brain vasculature

Source: Helmholtz Zentrum München

Diseases of the brain are often associated with typical vascular changes. Now, scientists at Helmholtz Zentrum München, LMU University Hospital Munich and the Technical University of Munich have come up with a technique for visualizing the structures of all the brain’s blood vessels – right down to the finest capillaries – including any pathological changes. So far, they have used the technique, which is based on a combination of biochemical methods and artificial intelligence, to capture the whole brain vasculature of a mouse.

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📌Via: @cedeeplearning

https://neurosciencenews.com/machine-learning-brain-vasculature-15909/

#machinelearning
#deeplearning
#neuralnetworks

New study allows brain and artificial neurons to link up over the web

🔻New study allows brain and artificial neurons to link up over the web
Source: University of Southampton

Researchers have created a hybrid neural network where biological and artificial neurons in different parts of the world were able to communicate via the internet through a hub of memristive synapses.
Brain functions are made possible by circuits of spiking neurons, connected together by microscopic, but highly complex links called ‘synapses’. In this new study, published in the scientific journal Nature Scientific Reports, the scientists created a hybrid neural network where biological and artificial neurons in different parts of the world were able to communicate with each other over the internet through a hub of artificial synapses made using cutting-edge nanotechnology. This is the first time the three components have come together in a unified network.
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📌Via: @cedeeplearning
📌Social media: https://linktr.ee/cedeeplearning

#machinelearning
#neuralnetworks
#deeplearning
#AI

🔻Using computers to view the unseen

From: Rachel Gordon

A new computational imaging method could change how we view hidden information in scenes.
Cameras and computers together can conquer some seriously stunning feats. Giving computers vision has helped us fight wildfires in California, understand complex and treacherous roads — and even see around corners.
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📌Via: @cedeeplearning


http://news.mit.edu/2019/using-computers-view-unseen-computational-mirrors-mit-csail-1206

#deeplearning
#computervision
#neuralnetworks
#objectdetection
#machinelearning

👆🏻👆🏻Using computers to view the unseen

https://youtu.be/hhEJMpouMS8

📌Via: @cedeeplearning

🔹What a little more #computing_power can do
From: Kim Martineau

To recognize a cat in a picture, a deep learning model may need to see millions of photos before its artificial #neurons “learn” to identify a cat. But there may be a more efficient way. New MIT research shows that models only a fraction of the size are needed. “When you train a big network there’s a small one that could have done everything,”. neural network could get by with on-tenth the number of connections if the right subnetwork is found at the outset.
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📌Via: @cedeeplearning
📌Social media: https://linktr.ee/cedeeplearning

link: http://news.mit.edu/2019/what-extra-computing-power-can-do-0916

#neuralnetworks
#GAN
#deeplearning
#machinelearning

🔻Supercomputer analyzes web traffic across entire internet

From: Rob Matheson

Using a supercomputing system, MIT researchers have developed a model that captures what web traffic looks like around the world on a given day, which can be used as a measurement tool for internet research and many other applications.
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📌Via: @cedeeplearning

http://news.mit.edu/2019/supercomputer-analyzes-web-traffic-across-entire-internet-1028

#deeplearning
#neuralnetworks
#supercomputer
#machinelearning
#AI