Exercise safely with AI and Computer Vision

In partnership with University of Zurich, startup VAY has just created a new way to coach fitness, using Artificial Intelligence and Computer Vision. The app called "Vay Sport" helps to avoid injuries and improve performance while training. It observe the exercises and provides real-time feedback on posture during workouts

Thanks to deep learning, the App instantly creates a computer model of the human body to read out joint angles and limb positions.
The algorithm was developed with certified coaches to recognize optimal exercise execution

Read more here: https://lnkd.in/fM9WmmN

#deeplearning #computervision #artificialintelligence #training #fitness #innovation #technology

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Machine Learning for Artists
#DeepLearning #MachineLearning #ArtificialIntelligence #neuralnetwork #gan

http://ml4a.github.io/

✴️ @AI_Python_EN

There are two main types of consumer segmentation.

The first is unsupervised (post hoc) in which segments are "discovered" with some form of cluster analysis. K-means and hierarchical clustering historically have been most popular, though there are many newer methods such as latent class that are also used.

The second type is supervised (a priori) in which a target (dependent) variable is statistically profiled. The target variable is usually (though not always) a nominal or ordinal variable. Logistic regression, CHAID and random forests are three of many analytics tools used.

Often the two are used in combination, e.g., post hoc segmentation to discover the segments and a priori segmentation to predict them. In this case, the post hoc clusters are the target variable.

A third kind, which I call driver segmentation, essentially combines the two approaches simultaneously. This is less known in marketing research but can be very useful.

Brand segmentation is somewhat different. In brand segmentation, brand "maps" are created using correspondence analysis, biplots, principal components, hierarchical clustering or other methods to identify clusters of brands (not people).

✴️ @AI_Python_EN

18 Impressive Applications of Generative Adversarial Networks (GANs)
A Generative Adversarial Network, or GAN, is a type of neural network architecture for generative modeling

Busy Programmer Designs Chatbot To Talk To His Girlfriend While At Work
https://in.mashable.com/tech/4051/busy-programmer-designs-chatbot-to-talk-to-his-girlfriend-while-at-work

Busy boyfriend creates bot to chat with his girlfriend
https://www.abacusnews.com/digital-life/busy-boyfriend-creates-bot-chat-his-girlfriend/article/3013843

from: cvision

We are Available in Persian

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--Activation Function in CNN--
-Image Analysis-

#imageanalysis #machinelearning #clustering #datascientists #kmeans #deeplearning #neuralnetwork #underfitting

Urban legends about regression abound...Regression is much more than OLS linear or binary logistic regression.

Normally distributed data are not a requirement. Regression is not limited to modeling linear relationships. Data correlated across time or cross-sectionally can be accommodated, as can data from mixed distributions.

Some extensions of regression are able to simultaneously model multiple dependent variables of any data type - continuous, interval, ordinal, nominal and count. Some can include latent (unobserved) variables on either side of the equation.

For those with a basic background in regression, this book by two leading authorities, is an excellent next step.
https://www.crcpress.com/Generalized-Linear-Models-and-Extensions-Fourth-Edition/Hardin-Hilbe/p/book/9781597182256

✴️ @AI_Python_EN

Machine Learning VS Deep Learning

#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning #technology #python

✴️ @AI_Python_EN

This is a classic JMR paper on segmentation by Yoram ("Jerry") Wind, one of the academic titans of marketing research in the latter part of the 20th century.

Many things have changed since 1978 when the article was published, but many have not.

This entire issue was devoted to segmentation and history buffs with JMR access can have their fill.

✴️ @AI_Python_EN

Tackling Climate Change with Machine Learning

Rolnick et al.: https://lnkd.in/eqqV9wr

#artificialintelligence #climatechange #climatecrisis
#machinelearning

✴️ @AI_Python_EN

It is a good feeling when a popular Python package adds a new feature based on your article :-)

#Yellowbrick is a great little #ML #visualization library in the Python universe, which extends the Scikit-Learn API to allow human steering of the model selection process, and adds statistical plotting capability for common diagnostics tests on ML.

Based on my article "How do you check the quality of your regression model in Python? they are adding a new feature to the library - Cook's distance stemplot (outlier detection) for regression models.

#python #datascience #machinelearning #data #model
https://www.scikit-yb.org/en/latest/

✴️ @AI_Python_EN

Sybil-Resilient Reality-Aware Social Choice. - A new Paper by Gal Shahaf et al. in #MultiAgent Systems

Paper: https://bit.ly/2TgeqvG

#artificialintelligence #machinelearning

✴️ @AI_Python_EN

Don't mess with Robots! 😂😂

#robots #robotics #ai #artificialintelligence #machinelearning #deeplearning #datascience #dataanalytics #bigdata #computervision #automation #technology #innovation #emergingtechnologies

Bosstown Robotics

✴️ @AI_Python_EN

I am implementing a training loop that can be used with Auxiliary Classifier. So what is an Auxiliary Classifier?

Auxiliary Classifier are the ones in which we take the outputs of layers of some previous layers along with the final outputs and compare it with the targets and calculate a loss based on both the outputs from the final layer as well as the previous layer.

How does this help?
I think before even me saying how this is going to be helpful, I think this intuitively gives an idea of how is this going to aid the training process, I got so freaking excited when I came to know about this.

So, How does this help?
- Solves the gradient Vanishing problem
- Low-level features get more and more accurate and thus making the model more and more accurate.
- This also acts as regularization, it kind of can be thought as putting some constraints on the model which help in regularization.

I am not sure which paper first introduced Auxiliary Approaches, but I am trying to train an FCN using this, let's see how this aids the process :)
Maybe the Inception paper.

#machinelearning #deeplearning #datascience #python #artificialintelligence #selfdriving #nlp #computervision

✴️ @AI_Python_EN

Using Neural Networks to perform makeup.
#neuralnetwork #ComputerVision
#Artificialntelligence

https://medium.com/mathematical-beauty/deep-learning-for-cosmetics-2d1b427bbfa2

✴️ @AI_Python_EN

Frankly, the process of machine learning is quite basic. But it pretty much runs the world.
https://www.technologyreview.com/s/612437/what-is-machine-learning-we-drew-you-another-flowchart/

✴️ @AI_Python_EN

Releasing STEAL, a new semantic boundary detector that significantly outperforms past work. Use STEAL to refine segmentation datasets, and train better segmentation models!
paper:https://arxiv.org/abs/1904.07934
code:https://github.com/nv-tlabs/STEAL
#computervision
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