🔻Detecting patients’ pain levels via their brain signals

System could help with diagnosing and treating #noncommunicative patients.

Researchers from #MIT and elsewhere have developed a system that measures a patient’s pain level by analyzing brain activity from a portable #neuroimaging device. The system could help doctors diagnose and treat pain in unconscious and noncommunicative patients, which could reduce the risk of chronic pain that can occur after surgery.
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📌Via: @cedeeplearning
📌Social media: https://linktr.ee/cedeeplearning

link: http://news.mit.edu/2019/detecting-pain-levels-brain-signals-0912

#deeplearning
#neuralnetworks
#machinelearning
#computerscience

🔹HOW AI ADOPTION CAN BE BENEFITED WITH COGNITIVE CLOUD?

Today cognitive computing and cognitive services are a big growth area that has been valued at US$ 4.1 billion in 2019 and its market is predicted to grow at a CAGR of around 36 percent, according to a market report. A number of companies are using cognitive services to improve insights and user experience while increasing operational efficiencies through process optimization.
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📌Via: @cedeeplearning

https://www.analyticsinsight.net/how-ai-adoption-can-be-benefited-with-cognitive-cloud/

#cloudcomputing
#cognitivecomputing
#neuralnetworks
#deeplearning

🔹MACHINE LEARNING, AI AND DEEP LEARNING TO DRIVE JOB MARKET IN 2018

Though discussions in Deep Learning, AI and machine learning continue as broad disciples, the jobs offered are more specific including:

• Machine learning engineer

• AI engineer

• Data scientist

• Business intelligence (BI) developer

• Data mining and analysis
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📌Via: @cedeeplearning

https://www.analyticsinsight.net/machine-learning-ai-and-deep-learning-to-drive-job-market-in-2018/

#AI
#machinelearning
#deeplearning
#job
#market

🔹Talking about how we talk about the ethics of artificial intelligence

Credit: by Matt Shipman

If you want to understand how people are thinking (and feeling) about new technologies, it's important to understand how media outlets are thinking (and writing) about new technologies. This paper focuses, in part, on ethical issues related to AI technologies that people would use in their daily lives. Could you give me one or two examples?
Probably the most well-known application of AI with very real ethical implications would be self-driving cars. If an autonomous car is in a situation where it has, for instance, lost control of its brakes and must either crash into a child or an adult, what should it do?
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📌Via: @cedeeplearning
📌Social media: https://linktr.ee/cedeeplearning

link: https://techxplore.com/news/2020-04-ethics-artificial-intelligence.html

#deeplearning
#AI
#neuralnetworks
#machinelearning

Hierarchical Memory Decoding for Video Captioning

A novel memory decoder for video captioning. After obtaining representation of each frame through a pre-trained network, they first fuse the visual and lexical information. Then, at each time step, they construct a multi-layer MemNet-based decoder, i.e., in each layer, we employ a memory set to store previous information and an attention mechanism to select the information related to the current input.


🔗 http://arxiv.org/abs/2002.11886

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

🔻Social media can accurately forecast economic impact of natural disaster including COVID-19 pandemic

Credit: by University of Bristol

Social media should be used to chart the economic impact and recovery of businesses in countries affected by the COVID-19 pandemic, according to new research published in Nature Communications. University of Bristol scientists describe a 'real time' method accurately trialed across three global natural disasters which could be used to reliably forecast the financial impact of the current global health crisis.
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📌Via: @cedeeplearning


https://techxplore.com/news/2020-04-social-media-accurately-economic-impact.html

#machinelearning
#socialmedia
#networkanalysis
#health
#pandemic

🔹Requisites for Operationalizing Your Machine Learning Models

there’s a lot that goes in the backend of creating a machine learning predictive model, but all of these efforts are for naught if you don’t operationalize your model effectively with a proper amount of forethought and rigor. The scoping. The preparation. The building and inferring. Each of these is a crucial initial step of the overall model lifecycle.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://www.rocketsource.co/blog/machine-learning-models/

#machinelearning
#AI
#deeplearning
#datascience
#prediction

🔹Using LIME to Understand a Machine Learning Model’s #Predictions

Using a record explainer mechanism like Local Interpretable #Model_Agnostic Explanations (LIME) is an important technique to filter through the predicted outcomes from any machine learning model. This technique is powerful and fair because it focuses more on the inputs and outputs from the model, rather than on the model itself.
#LIME works by making small tweaks to the input #data and then observing the impact on the output data. By #filtering through the model’s findings and delivering a more digestible explanation, humans can better gauge which predictions to trust and which will be the most valuable for the organization.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://www.rocketsource.co/blog/machine-learning-models/

#machinelearning
#datascience
#deeplearning
#AI

✔️Successfully Deploying Machine Learning Models

There are various opinions and assertions out there regarding the end-to-end process of building and deploying predictive models. We strongly assert that the deployment process is not a process at all — it’s a lifecycle. Why? It’s an infinite process of iterations and improvements. Model deployment is in no way synonymous with model completion. We will go deeper into the reasons for this in the section below as we address the requisite steps for operationalizing a model, but the high-level post-deployment steps are called out in the following diagram. Here’s what that deployment looks like in action
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#machinelearning
#lifecycle
#deployment
#datascience
#deeplearning

👆🏻👆🏻Successfully Deploying Machine Learning Models

1. Validate Use Case
2. Data Finalization
3. Explore and Diagnose
4. Cleanse
5. Develop
6. Features
7. Build
8. Infer
9. Publish
10. Deploy
11. Consume
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📌Via: @cedeeplearning

#machinelearning
#datascience
#deployment
#lifecycle
#AI
#data
#deeplearning

🔹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

A self-supervised audio-video synchronization learning method to address the problem of speaker diarization without massive labeling effort.

https://arxiv.org/abs/2002.05314

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

🔻A Beginner's Guide to Convolutional Neural Networks (#CNNs)

Convolutional neural networks are neural networks used primarily to classify images (i.e. name what they see), cluster images by similarity (photo search), and perform object recognition within scenes. For example, convolutional neural networks (ConvNets or CNNs) are used to identify faces, individuals, street signs, tumors, platypuses (platypi?) and many other aspects of visual data.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://pathmind.com/wiki/convolutional-network

#deeplearning
#neuralnetworks
#machinelearning
#math
#datascience

🔻Data for Deep Learning

🔹Types of Data:
1. sound
2. text
3. images
4. time series
5. video

🔹Use Cases:
1. classification
2. clustering
3. predictions

🔹Data Attributes:
1. relevancy
2. proper classification
3. formatting
4. accessibility

🔹Minimum Data Requirement:
The minimums vary with the complexity of the problem, but 100,000 instances in total, across all categories, is a good place to start.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://pathmind.com/wiki/data-for-deep-learning

#deeplearning
#machinelearning
#neuralnetworks
#classification
#clustering
#data

🔹Deep Autoencoders

A deep autoencoder is composed of two, symmetrical deep-belief networks that typically have four or five shallow layers representing the encoding half of the net, and second set of four or five layers that make up the decoding half.
The layers are restricted Boltzmann machines, the #building_blocks of deep-belief networks, with several peculiarities that we’ll discuss below. Here’s a simplified schema of a deep autoencoder’s structure, which we’ll explain below.
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://pathmind.com/wiki/deep-autoencoder

#autoencoder
#deepbeliefnetwork
#neuralnetworks
#machinelearning

🔹HOW COMPUTER VISION, AI, AR AND OTHERS ARE ENHANCING IN-VEHICLE EXPERIENCES?

By Smriti Srivastava

Some latest emerging in-vehicle technologies that are changing how people interact with cars:

🔹Authentication Through Biometric

🔹In-vehicle Voice Assistant


🔹Augmented Reality for Heads-up Displays

🔹Reducing Human Error Through Vision-based Monitoring

🔹Retail and Entertainment

Tech-optimized Parking
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📌Via: @cedeeplearning

https://www.analyticsinsight.net/computer-vision-ai-ar-others-enhancing-vehicle-experiences/

#selfdrivingcar
#deeplearning
#AI
#computervision

🔻GOOGLE LEVERAGES MACHINE LEARNING TO IMPROVE DOCUMENT DETECTION CAPABILITIES

Google has been employing a new scanner that uses machine learning to improve detection. Since the scanner launched, Google has boosted the detection of Office documents by 10%. Impressively, Google’s new scanner is getting better at detecting “adversarial, bursty attacks” with the detection rate jumping by 150%.
Interestingly, Google says that 58% of all malware targeting Gmail users comes from malicious documents, the vast majority of that coming from Office documents alone.
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📌Via: @cedeeplearning

https://www.analyticsinsight.net/google-leverages-machine-learning-to-improve-document-detection-capabilities/

#AI
#cybersecurity
#machinelearning
#google
#datascience

🔻AI app can detect coronavirus from sound of cough

🔹The app has a 70% accuracy rate.

Researchers have developed a new app that uses artificial intelligence technology to determine whether a person has COVID-19 based on the sound of their cough. The app has a 70% accuracy rate.

Source: EPFL

you can record your cough on a smartphone and find out whether you might have COVID-19. So how can a smartphone app detect the new coronavirus? “According to the World Health Organization, 67.7% of people who have the virus present with a dry cough – producing no mucus – as opposed to the wet cough typical of a cold or allergy,” says David Atienza, a professor at EPFL’s School of Engineering who is also the head of ESL and a member of the Coughvid development team.
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📌Via: @cedeeplearning

https://neurosciencenews.com/ai-cough-coronavirus-16145/

#deeplearning
#neuralnetworks
#AI
#machinelearning
#accuracy