Data science is an ever-evolving field. As data scientists, we need to have our finger on the pulse of the latest algorithms and frameworks coming up in the community.

So, if you’re a:
Data science enthusiast
Machine learning practitioner
Data science manager
Deep learning expert

or any mix of the above, this article is for you.

Shubham Singh loved putting together this month’s edition given the sheer scope of topics we have covered.

Link : https://bit.ly/2IDa5iP

What did you think of this month’s collection? Any data science libraries or discussions I missed out on? Hit me up in the comments section below and let’s discuss!

#machinelearning #datascience #deeplearning #deeplearning

✴️ @AI_Python_EN

Call for Speakers - Boston's Field Guide to Data Science & Emerging Tech - Correct Link

Would love for you to consider presenting at our Boston conference (Google auto corrected earlier link.- sorry about that).

https://forms.gle/APNF48rXfATJLJjq9

Aug 22 at BU.

Designed as an organic regional community event it is authentic, accessible, & engaging.

Expect 700 participants and about 40 sessions.

Thanks.

btw: Also doing a Food, Ag, Sustainability, & Supply Chain in Tech Conference Dec 9th in St. Paul, MN. Home of Cargil, General Mills, Land O Lakes, Ecolab,...

✴️ @AI_Python_EN

Dear all,

We are seeking a full-time Research Associate/Research Assistant in AI and Robotics to join the Human-AI Teaming (HAT) lab at King's College London, in the Department of Informatics. The post is for up to 36 months, starting from September 1st 2019 or soon thereafter.
The salary is up to £44,015 per annum depending on experience.

More info about the post here:
https://my.corehr.com/pls/kingrecruit/erq_jobspec_version_4.display_form?p_company=1&p_internal_external=E&p_display_in_irish=N&p_process_type=&p_applicant_no=&p_form_profile_detail=&p_display_apply_ind=Y&p_refresh_search=Y&p_recruitment_id=014946

The vision of the HAT lab is that humans should work together with the AI as a team. To achieve this, we focus on new research and challenges around Safe, Trusted and Explainable AI, that will allow humans to trust what the AI systems are about to do and to interact with the AI systems to co-create solutions.

We focus on AI Planning and Machine/Reinforcement Learning, and the main application area is the control of robotics and autonomous systems, where AI is used to control (teams of) moving robots as well as to provide robots with more autonomy (for example in manufacturing, space robotics, or satellites).

HAT lab webpage: https://www.human-ai-teaming.com/

We are looking for a candidate who can drive our research agenda. You will be responsible for adapting existing as well as developing new techniques, and will be driving its applications in scenarios involving physical robots in our lab. You will also be involved in co-directing the research of PhD students and to engage with collaborators and industrial partners.

King's College London is one of the best places where this type of research can be done, also thanks to the recently funded Centre for Doctoral Training in Safe and Trusted AI (https://www.kcl.ac.uk/nms/depts/informatics/stai), that aims to make King's one of the leading institutions for the research in this space.

The candidate is expected to have a PhD (or be near completion) in AI or Robotics, together with a strong track record. Expertise on Explainable AI, AI Planning, ROS, Machine Learning, Reinforcement Learning would be a plus.

Please email me if you have any question.

Many thanks and kind regards,
Dan

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
✴️ @AI_Python_EN

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?

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

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.

✴️ @AI_Python_EN

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