A new tool from Google and OpenAI lets us better see through the eyes of artificial intelligence
http://bit.ly/2NYtInO

The best of ML. 40 of the best resources for Machine Learning
http://bit.ly/2NS1Z8h

Machine Learning for Beginners: An Introduction to Neural Networks
http://bit.ly/2NTbwvC

Modern Deep Learning Techniques Applied to Natural Language Processing
http://bit.ly/2F1jY9k

AlphaStar: Mastering the Real-Time Strategy Game StarCraft
http://bit.ly/2F2megB

IBM Cognitive classes is one way to learn Data Science and Machine Learning, and it is absolutely FREE.

*DON'T SKIP THE LAB EXERCISE., IT IS HELPFUL*

This is the Learning Path

1)Introduction to Data Science
(https://lnkd.in/fF79bEj)

2)Data Science Tools
(https://lnkd.in/fYf2ZC8)

3)Data Science Methodology
(https://lnkd.in/fY6Kwqd)

4)Statistics 101
(https://lnkd.in/fpgJf7D)

5)Predictive Modeling Fundamentals I
(https://lnkd.in/f9_Y7UZ)

6)Python for Data Science
(https://lnkd.in/fy8E2wH)

7)Data Analysis with Python
(https://lnkd.in/fRQWByd)

😎Data Visualization with Python
(https://lnkd.in/fFu93ME)

9)Machine Learning with Python
(https://lnkd.in/f_7r534)

10)Deep Learning Fundamentals
(https://lnkd.in/fNvPvix)

11)Deep Learning with TensorFlow
(https://lnkd.in/ftfRtvQ)

#datavisualization #deeplearning #datascience #python #predictivemodeling #machinelearning

this is a good tutorial playlist
https://www.youtube.com/watch?v=OGxgnH8y2NM&list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v

Close to half of startups jump on the AI bandwagon without the tech
https://zd.net/2F5I0ju

Neural MMO — A Massively Multiagent Game Environment
http://bit.ly/2F6h4QL

Artificial Intelligence at NASA – Current Projects and Applications
http://bit.ly/2NWP2Kk

Feature Engineering and Selection: A Practical Approach for Predictive Models. Book by Max Kuhn and Kjell Johnson
http://bit.ly/2NYvoO2

Virginia Beach City Council Goal Measures based on Open Data
http://bit.ly/2F4Ng6W

Interpretable Machine Learning
"A Guide for Making Black Box Models Explainable" by Christoph Molnar

Deepmind teaches AI to follow navigational directions like humans
http://bit.ly/2NXtOMc

You created a machine learning application. Now make sure it’s secure.
https://oreil.ly/2O0yIZ9

A Conversation about Tech Ethics with the New York Times Chief Data Scientist
http://bit.ly/2NZBmxZ

Tutorial: Poisson Regression in R
http://bit.ly/2F7QOFv

A Data-Driven Tribute to Captain Marvel
https://tabsoft.co/2F7pl76

AI Interprets What Rodents Are Saying
http://bit.ly/2O44Mes

LazyNLP for Creating Massive Text Datasets
http://bit.ly/2O3aKfN

How Artificial Intelligence Is Changing Science
http://bit.ly/2FcetF2

3 Levels of Deep Learning Competence
http://bit.ly/2O4MeuJ

How many high school stars make it in the NBA?
http://bit.ly/2FdHlww

Boom! What to read on the weekend, Issue #54

The Most Complete List of Best AI Cheat Sheets
http://bit.ly/2O4O1Qg

The case for building expertise to work on US AI policy, and how to do it
http://bit.ly/2O7GgsZ

Advanced Machine Learning with Basic Excel
http://bit.ly/2O4O6Dy

Natural Language Processing in Pharma – Current Applications
http://bit.ly/2FhRacP

Harnessing Organizational Knowledge for Machine Learning
http://bit.ly/2O9M4Cb

A Day in the Life: Women and Men
http://bit.ly/2O5sKGn

TF-Replicator: Distributed Machine Learning for Researchers
http://bit.ly/2Fe9Vha

Round the World with Nellie Bly
https://tabsoft.co/2FiBt5j

The full moon calendar 1900-2050. 150 years of data in an overview
http://bit.ly/2ObpLMI

Worldwide mobile data pricing: The cost of 1GB of mobile data in 230 countries
http://bit.ly/2Ffhp3N

The blockchain project SingularityNET on Thursday announced the start of cooperation with one of the largest insurance corporations in the world, Ping An.

“Ping An Technology and SingularityNET have agreed to cooperate in the field of artificial intelligence, and are also considering the possibility of launching other joint initiatives related, in particular, with optical character recognition technology (OCR) and machine learning,” said Ping An Bai, head of the AI-direction. Meng.

As the project explains in his blog, initially his collaboration with Ping An will focus on three main areas: OCR, Cross Validation and machine learning, "where the SingularityNET environmental system can find commercial applications."

In the future, the scope of the agreement may be expanded, SingularityNET claims, pointing to insurance company initiatives such as Ping An Smart City and One Minute Clinic, which uses artificial intelligence technology.

The SingularityNET project raised $ 32.8 million through ICO at the end of 2017, and its AGI token showed positive dynamics regarding Bitcoin and Ethereum after entering the market but lost half of its value in dollar terms.

Ping An was founded in 1988 and is included in the Hang Seng Index, calculated as the weighted average of the largest capitalized shares of 50 Hong Kong stock companies, which make up 58% of the capitalization of the Hong Kong Stock Exchange. In 2018, according to one of the versions, Ping An was recognized as the third most expensive financial services company in the world.

Boom! Weekly selection from DSB. Issue #45
The TOP 10 most popular links for the week

1. The best of ML. 40 of the best resources for Machine Learning
http://bit.ly/2NS1Z8h

2. A new tool from Google and OpenAI lets us better see through the eyes of artificial intelligence
http://bit.ly/2NYtInO

3. Artificial Intelligence at NASA – Current Projects and Applications
http://bit.ly/2NWP2Kk

4. Visual Vocabulary
http://bit.ly/2Fau0VM

5. Natural Language Processing Tutorial for Deep Learning Researchers
http://bit.ly/2Fa709q

6. Machine Learning for Beginners: An Introduction to Neural Networks
http://bit.ly/2NTbwvC

7. The A.I. Diet. Forget government-issued food pyramids. Let an algorithm tell you how to eat.
https://nyti.ms/2F99dBN

8. The Most Complete List of Best AI Cheat Sheets
http://bit.ly/2O4O1Qg

9. 3 Levels of Deep Learning Competence
http://bit.ly/2O4MeuJ

10. LazyNLP for Creating Massive Text Datasets
http://bit.ly/2O3aKfN

Adafruit IO Environmental Monitor for Feather or Raspberry Pi
Build an internet-enabled environmental monitor to see what's in th

Knowing what's in the air you breathe is important - and building an environmental monitor is a way to visualize the invisible properties of the air you inhale.

This guide covers building a small, internet-enabled environmental monitor which can track a range of data such from temperature to UV-level to the amount of total-volatile-organic-compounds present in the air.

https://learn.adafruit.com/adafruit-io-air-quality-monitor/overview

We’re training AI to keep an eye on government. Come join us.
http://bit.ly/2OceJ9Y

World Health Organisation: Global Suicide Trends & Analysis
http://bit.ly/2O9hlW2

TwoTone. A free web app to turn data into sound and music
http://bit.ly/2FiLxeg

ML for Everyone. In simple words. With real-world examples. Yes, again
http://bit.ly/2FiBLcb

A colorful π chart
http://bit.ly/2OdVYmA

China is about to overtake America in AI research
http://bit.ly/2OdWn8A

People think that data is in the cloud, but it’s not. It’s in the ocean.
https://nyti.ms/2OaOhgX

10 things R can do that might surprise you
http://bit.ly/2Oap7yR

Model Zoo. Discover open source deep learning code and pretrained models.
http://bit.ly/2ObZMo2

How People Meet Their Partners
http://bit.ly/2O7y2Rw

Another major release of #SpaCy

👽 ULMFit/BERT/ELMo-like language model pretraining
📐 Better matching & rule-based NER
🧮 Platform-independent matrix multiplication
🚀 2-3 times faster tokenization
⏳ Installation without local compilers

Link: https://explosion.ai/blog/spacy-v2-1

#NLP #python

This resume does not exist
http://bit.ly/2O8cN2d

Here’s How We’ll Know an AI Is Conscious
http://bit.ly/2O9f2Cc

A Step-by-Step NLP Guide to Learn ELMo for Extracting Features from Text
http://bit.ly/2Ob9QO4

FastCharts to make charts fast
http://bit.ly/2Fk4mhn

When were CDs, cassettes, and other recording formats most profitable?
https://tabsoft.co/2O9x3QL

​​Neural network that turns sketches into realistic photo.

Paper is called «Semantic Image Synthesis with Spatially-Adaptive Normalization».

#CVPR19 oral paper on a new conditional normalization layer for semantic image synthesis #SPADE and its demo app #GauGAN

ArXiV: https://arxiv.org/abs/1903.07291
Website: https://nvlabs.github.io/SPADE/

#GAN #CV #DL