🔹Deep Learning #Algorithms Identify Structures in Living Cells
For cell biologists, fluorescence microscopy is an invaluable tool. Fusing dyes to antibodies or inserting genes coding for fluorescent proteins into the #DNA of living cells can help scientists pick out the location of #organelles, #cytoskeletal elements, and other subcellular #structures from otherwise #impenetrable microscopy images. But this technique has its #drawbacks.
📌Via: @cedeeplearning
link: https://www.the-scientist.com/notebook/deep-learning-algorithms-identify-structures-in-living-cells-65778
#deeplearning
#neuralnetworks
#machinelearning
For cell biologists, fluorescence microscopy is an invaluable tool. Fusing dyes to antibodies or inserting genes coding for fluorescent proteins into the #DNA of living cells can help scientists pick out the location of #organelles, #cytoskeletal elements, and other subcellular #structures from otherwise #impenetrable microscopy images. But this technique has its #drawbacks.
📌Via: @cedeeplearning
link: https://www.the-scientist.com/notebook/deep-learning-algorithms-identify-structures-in-living-cells-65778
#deeplearning
#neuralnetworks
#machinelearning
🔹Deep Learning technologies impacting computer vision advances
A significant focus of study in the field of computer vision is on systems to recognize and remove highlights from digital pictures. Extracted features context for inference about an image, and often the more extravagant the highlights, the better the derivation.
Until not long ago, facial recognition was an awkward and costly innovation constrained to police research labs. However, as of late, because of advances in #computer_vision #algorithms, #facial_recognition has discovered its way into different computing gadgets.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://www.analyticsinsight.net/deep-learning-technologies-impacting-computer-vision-advances/
#deeplearning
#neuralnetworks
#machinelearning
A significant focus of study in the field of computer vision is on systems to recognize and remove highlights from digital pictures. Extracted features context for inference about an image, and often the more extravagant the highlights, the better the derivation.
Until not long ago, facial recognition was an awkward and costly innovation constrained to police research labs. However, as of late, because of advances in #computer_vision #algorithms, #facial_recognition has discovered its way into different computing gadgets.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
link: https://www.analyticsinsight.net/deep-learning-technologies-impacting-computer-vision-advances/
#deeplearning
#neuralnetworks
#machinelearning
🔻Deep learning AI discovers surprising new #antibiotics
Enter deep learning. These #algorithms power many of today’s facial recognition systems and #self_driving cars. They mimic how neurons in our brains operate by learning patterns in data. An individual artificial #neuron – like a mini sensor – might detect simple patterns like lines or circles. By using thousands of these artificial neurons, deep learning AI can perform extremely complex tasks like recognizing cats in videos or detecting tumors in biopsy images.
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📌Via: @cedeeplearning
social media: https://linktr.ee/cedeeplearning
link: https://theconversation.com/deep-learning-ai-discovers-surprising-new-antibiotics-132059
#deeplearning
#machinelearning
#neuralnetworks
Enter deep learning. These #algorithms power many of today’s facial recognition systems and #self_driving cars. They mimic how neurons in our brains operate by learning patterns in data. An individual artificial #neuron – like a mini sensor – might detect simple patterns like lines or circles. By using thousands of these artificial neurons, deep learning AI can perform extremely complex tasks like recognizing cats in videos or detecting tumors in biopsy images.
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📌Via: @cedeeplearning
social media: https://linktr.ee/cedeeplearning
link: https://theconversation.com/deep-learning-ai-discovers-surprising-new-antibiotics-132059
#deeplearning
#machinelearning
#neuralnetworks
🔹Generative vs. Discriminative Algorithms
To understand GANs, you should know how generative #algorithms work, and for that, contrasting them with discriminative algorithms is instructive. Discriminative algorithms try to classify input data; that is, given the features of an instance of data, they predict a label or category to which that data belongs.
Another way to think about it is to distinguish discriminative from generative like this:
1. #Discriminative models learn the boundary between classes
2. #Generative models model the #distribution of individual classes
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📌Via: @cedeeplearnig
📌Other social media: https://linktr.ee/cedeeplearning
link: https://pathmind.com/wiki/generative-adversarial-network-gan
#GAN
#deeplearning
#neuralnetworks
#machinelearning
To understand GANs, you should know how generative #algorithms work, and for that, contrasting them with discriminative algorithms is instructive. Discriminative algorithms try to classify input data; that is, given the features of an instance of data, they predict a label or category to which that data belongs.
Another way to think about it is to distinguish discriminative from generative like this:
1. #Discriminative models learn the boundary between classes
2. #Generative models model the #distribution of individual classes
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📌Via: @cedeeplearnig
📌Other social media: https://linktr.ee/cedeeplearning
link: https://pathmind.com/wiki/generative-adversarial-network-gan
#GAN
#deeplearning
#neuralnetworks
#machinelearning