Get ready to do some natural language processing in Course 3 of the deeplearning.ai TensorFlow Specialization, available June 20! While you’re waiting, check out the first two courses: http://bit.ly/2Zkij5Z
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DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks

Paper: http://ow.ly/Nk6Y50uAmII
#artificialinteligence #ai #ml #machinelearning #bigdata #deeplearning #technology

✴️ @AI_Python_EN

Why Should you Learn AI and Machine Learning

Why Machine Learning Fascinates Me?

Supervised Machine Learning

Do you know what is Machine Learning All About?

The Science of Machine Learning is about Learning the Models that Generalize Well Machine learning is an area of artificial intelligence and computer science This includes the development of software and algorithms that can make predictions based on data.

Data Science Enthusiasts, I have Created a Community for Us to Learn Together🗝

Interested people let me know in the Comments and I will send you the invite link to our Community🎟🗣

#reinforcementlearning #machinlearning #Datascience #ArtificialIntelligence #gans
#SupervisedMachineLearning #ML #dl #iot #bigdata

✴️ @AI_Python_EN

Differentiable Beam search Decoder (DBD): training a speech recognition system by backpropagating through the decoder leads to smaller models and better word error rates...

https://ai.facebook.com/blog/combining-acoustic-and-language-model-training-for-speech-recognition/

Confuse When Reading Symbols on Machine Learning Book?

Sometimes, we not always remember probability and statistics symbols when brush up machine learning knowledge. This is Probability and Statistics Symbols that can make you remember the symbols, made by coolmathposters and I found it on deepkapha.ai.

How to implement statistics into business?After you good at machine learning you can implement on some cases?

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Join me at TensorFlow + Swift + OpenAI's Unsupervised Sentiment, KubeFlow, TFX, Kubernetes http://meetu.ps/e/FXy9j/HydPD/a

"Write With Transformer" project, : text completions (GPT-2 language model) .
https://transformer.huggingface.co/ #deeplearning #machinelearning

Wild idea from Google folks - taking compression/pruning/quantization to the next level by turning neural nets into simple look-up tables. "Table-Based Neural Units: Fully Quantizing Networks for Multiply-Free Inference," Covell et al.: https://arxiv.org/abs/1906.04798

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At #CVPR2019, NVIDIA Researchers will present 20 accepted papers, including 11 orals. Check out the list here: https://nvda.ws/2WzDaAK

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DeepFake video concerns. AI researchers designing systems to detect fabricated footage of real people known as “deepfakes.” https://lnkd.in/gDFsGVq #machinelearning #deeplearning #deepfake #artificialintelligence… https://www.instagram.com/p/ByrUkWDnsgi/?igshid=1fum3iz1bmd5c …

✴️ @AI_Python_EN

Statistics makes heavy use of the normal distribution for mathematical reasons, and also because the assumption of normality is often reasonable. Many natural phenomena are normally distributed, in fact.

That said, statisticians make use of dozens of distributions. As Bayesian statistics becomes mainstream, it's more important than ever that statisticians and data scientists have a good understanding of probability.

I've been cracking the books myself, and some books I've found helpful include:

- Introduction to Probability (Bertsekas and Tsitsiklis)
- Introduction to Probability Models (Ross)
- Essentials of Probability Theory for Statisticians (Proschan and Shaw)
- Handbook of Statistical Distributions (Krishnamoorthy)
- Statistical Distributions (Forbes et al.)

Hardin and Hilbe's book in the Generalized Linear Model is an excellent overview of GLMs. In addition to hierarchical modeling and mixture models, there are many extensions such as GAM, VGAM and GAMLSS, Quantile Regression, etc. Frank Harrell's book Regression Modeling Strategies is a gold mine, IMO.

There are also SEM extensions of the GLM, not to mention factor analysis, latent class clustering and a gigantic number of other unsupervised methods.

I can also recommend the Journal of the American Statistical Association to those who believe statistics is outdated and inflexible...:-) Lots of crazy urban legends circulating in some corners of the data science community.

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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
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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