Here is a new 3D fashion dateset aiming for developing virtual fitting rooms

created by research institute of big data (SRIBD) and Chinese university of Hong Kong

https://medium.com/syncedreview/deep-fashion3d-dataset-benchmark-for-virtual-clothing-try-on-and-more-e09bf90e3fdb

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

🔹The Rise of Generative Adversarial Networks

A comprehensive overview of Generative Adversarial Networks, covering its birth, different architectures including #DCGAN, #StyleGAN and #BigGAN, as well as some real-world examples.

Credit: By Kailash Ahirwar

In this article, we have seen how GANs rose to fame and became a global phenomenon. I hope, we see the democratization of GANs in the coming years. In this article, we started with the birth of GANs. Then, we explored some widely popular GAN architectures. Finally, we witnessed the rise of GANs. When I see negative press around GANs, I am baffled. I believe, it is our responsibility to make everyone aware of the repercussions of GANs and how can we ethically and morally use GANs for our best.
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📌Via: @cedeeplearning
📌Social media: https://linktr.ee/cedeeplearning

link: https://www.kdnuggets.com/2019/04/rise-generative-adversarial-networks.html

#GAN
#deepfake
#deeplearning
#neuralnetworks
#Ian_Goodfellow

🔻TensorFlow Dev Summit 2020: Top 10 Tricks for TensorFlow and Google Colab Users

In this piece, we’ll highlight some of the tips and tricks mentioned during this year’s TF summit. Specifically, these tips will help you in getting the best out of Google’s Colab.

Credit: By Derrick Mwiti
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📌Via: @cedeeplearning

https://www.kdnuggets.com/2020/04/tensorflow-dev-summit-2020-top-10-tricks-tensorflow-colabs.html

#TensorFlow
#google
#neuralnetworks
#deeplearning
#machinelearning

🔻🔻2 Things You Need to Know about Reinforcement Learning
1. Computational Efficiency
2. Sample Efficiency

Experimenting with different strategies for a reinforcement learning model is crucial to discovering the best approach for your application. However, where you land can have significant impact on your system's energy consumption that could cause you to think again about the efficiency of your computations.

By Kevin Vu
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📌Via: @cedeeplearning

https://www.kdnuggets.com/2020/04/2-things-reinforcement-learning.html

#reinforcement
#deeplearning
#neuralnetworks
#efficiency
#machinelearning

🔹Computing and artificial intelligence: Humanistic perspectives from MIT

"The advent of artificial intelligence presents our species with an historic opportunity — disguised as an existential challenge: Can we stay human in the age of AI? In fact, can we grow in humanity, can we shape a more humane, more just, and sustainable world?"

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

link: https://shass.mit.edu/news/news-2019-computing-and-ai-humanistic-perspectives-mit-foreword-dean-melissa-nobles

#MIT
#AI
#machinelearning
#computing

🔻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