Remember that Mona Lisa animation? Now a new deep learning system can make an onscreen speaker say anything you type. Read the paper by Stanford, Princeton, and Adobe researchers:
http://bit.ly/2WIsftb
#DeepLearning #GANs #MachineLearning #ArtificialIntelligence

related post :
https://t.me/ai_python_en/1413
and
https://t.me/ai_python_en/1274
✴️ @AI_Python_EN

Many statistical procedures make the assumption that the data (observations) are independent and identically distributed (i.i.d.).

Often, however, this assumption is unrealistic and statisticians have developed numerous methods appropriate for situations when it is untenable.

For example, employee attitudes in one company are usually more similar within that company than to attitudes in another company. Hierarchical models (aka multilevel models) can be used to account for this lack of independence in the data.

Data may be correlated across time, too. For example, sales in one week are usually similar to sales in the preceding week. Many methods have been developed to account for autocorrelated data and are widely used in many disciplines.

Mixture models are useful when we cannot assume that one distribution applies to all observations. Some data are multimodal, for instance, and mixture models can account for this.

Note that we need not assume one distribution applies to all variables in our data. It is possible to model continuous, ordinal, nominal and count data simultaneously, for example. Hierarchical, time-series and mixture models can all be combined too, if necessary.

I have no idea how many ways there are to conduct a regression type of analysis. Hundreds would not be an exaggeration.

✴️ @AI_Python_EN

Consider checking out this Amazing tutorial series on AI and Machine learning on YouTube.

#machinelearning #artificialintelligence #deeplearning #python #computervision

https://lnkd.in/eui_KjZ

AI and Machine Learning Part 1 of 4

✴️ @AI_Python_EN

The Neural Network Visualization course has some solid momentum! 20 (out of 33) coding exercises have now been released. It's now US$4, but will be US$6 when it's finished, so you can still get it at a discount. Come preview the first section for free.

https://lnkd.in/eNeue6N

✴️ @AI_Python_EN

Nice project by Hugging Face (https://huggingface.co/) called "Write With Transformer" where you can write anything in a Google Doc-like interface and then get text completions (GPT-2 language model) multiple times. I've just tried it out and it's really cool. Check it out! #deeplearning #machinelearning

Link: https://lnkd.in/dGHqYDa

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

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

Python and Linux :

https://t.me/PythonLinuxExperts

DeepLearning and Artificial Intelligence:

https://t.me/DeepLearningAIExperts

Natural Language Processing and Data Mining:

https://t.me/NLPExperts

--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