I often read or hear that 97% (sometimes 98%) of scientists agree with the consensus view on climate change. What is meant by scientists or consensus view is often not made clear and typically no sources are cited.

I have followed this and other environmental issues closely for many years, and this assertion appears to rest mostly on three studies: Oreskes (2004); Cook et al. (2013); and Doran and Zimmerman (2009). You can look up these studies yourself and draw your own conclusions as to how well they support the 97%/98% assertion.

A better source, IMO, are the five surveys Dennis Bray, a sociologist, and Hans Von Storch, a prominent climate scientist, have conducted since the 1990s. Here is the link to the most recent (2016):

https://lnkd.in/f4xG394

This is a very detailed study and I would recommend you read the full report instead of focusing on the questions of most interest to you. As always, please be on the lookout for confirmation bias. :-)

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Causal Discovery Toolbox: An Open-Source Framework for Causal Inference in Graphs #DataScience #MachineLearning #ArtificialIntelligence http://bit.ly/2Hkqs4B

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11 Facts about data science in office work


Old fact, but it is still relevant?

Please share us if you would like our channel
#datascience #business

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Best universities to study machine learning:

University of California, Berkeley
Stanford University
University of Oxford
Université de Montréal
University of Toronto
Carnegie Mellon University
Xi'an Jiaotong University
New York University
Massachusetts Institute of Technology
University of Amsterdam
University of Michigan
Cornell University
Max Planck Society
Tsinghua University
The Chinese University of Hong Kong
University of North Carolina at Chapel Hill
University of Washington

Best places to work in machine learning:

Gartner
Google
Microsoft
Facebook
IBM
AT&T
Alcatel-Lucent
Adobe Systems

#machinelearning

✴️ @AI_Python_EN

We are extremely thrilled to open our doors for Japan with our AI services — for enterprises and community such as Universities & individual professionals.

Welcoming our Country Head Takashi Nishida San to drive growth into this beautiful nation with #BigData a #IoT #Robotics

Our proven tools & methodologies in underlying technologies such as #machinelearning & #deeplearning will be used to :

1)assess AI maturity
2) prescribe & develop the right AI skills program
3) execute data-driven ROI AI projects

to power up the economic growth of our clients.

https://lnkd.in/e45xhVe

✴️ @AI_Python_EN

Google Releases GPipe, an Open Source Library for Efficiently Training Large-scale Neural Network Models
http://bit.ly/2HhH4tZ
#MachineLearning #ArtificialIntelligence #DataScience

✴️ @AI_Python_EN

Concurrent Meta Reinforcement Learning

Parisotto et al.: https://lnkd.in/e6nyRhc

#artificialintelligence #deeplearing #reinforcementlearning

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Google open sources #Gpipe under #Lingvo library for Sequence Modeling.

What is Lingvo?

Lingvo is the international language Esperanto word for “language”. This naming alludes to the roots of the Lingvo framework — it was developed as a general deep learning framework using TensorFlow with a focus on sequence models for language-related tasks such as machine translation, speech recognition, and speech synthesis.

What is Gpipe?

GPipe is a distributed machine learning library that uses synchronous stochastic gradient descent and pipeline parallelism for training, applicable to any DNN that consists of multiple sequential layers. Importantly, GPipe allows researchers to easily deploy more accelerators to train larger models and to scale the performance without tuning hyperparameters.
Link: https://lnkd.in/ePsTCxw

GitHub: https://lnkd.in/eRwgEZz
Article: https://lnkd.in/e2y4fV2

#deeplearning #machinelearning #tensorflow

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It is time we shared the dataset with everyone. This is a collection of text from Tamil news articles. Has around 7 millions lines of text, all cleaned up, ready to used for language modelling task, in case anyone want to try. You can use the code from git repo below to get started.

Dataset:
https://lnkd.in/fzg3xyM]
Code:
https://lnkd.in/fezt4M8 #datasets

✴️ @AI_Python_EN

Neural MMO(Massively Multiplayer Online Game) by OpenAI

How do you Simulate evolution on Earth?

First ever neural MMO environment, that has created agents(players) that would scale up to real-world complexity.

Massive Multiplayer Online Role-Playing Games are currently the best proxy for the real world humans. Making an environment multiplayer would include diverse skills, global economy, etc. parameters

GitHub Link: https://lnkd.in/gEXEvXd
Blog: https://lnkd.in/gQXuSWJ
Paper: https://lnkd.in/gkbNJJR

#pytorch #deeplearning #artificialintelligence #opensource #neuralnetworks #gaming #agents

✴️ @AI_Python_EN

AI Tech&Review site "Synced" examines "The Cake Analogy 2.0" and my take on self-supervised learning in my ISSCC keynote.
https://syncedreview.com/2019/02/22/yann-lecun-cake-analogy-2-0/

✴️ @AI_Python_EN

Image Translation with Tensorflow


#Pix2Pix is an Image-to-Image Translation with Conditional Adversarial Networks.

It can prove to be effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks

Check out the original paper(https://lnkd.in/fFAm8YK) if you are interested in implementation detail, it shows more example usages for cGAN like "map to aerial", "day to night" et.

Here is a Tensorflow implementation of the same by Christopher Hesse(https://lnkd.in/f7ivy95)

#deeplearning

✴️ @AI_Python_EN

Based on book of "New Document.docx"
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Thank you all for your support!
We're 1000 members now :)
✴️ @AI_Python_EN

Myth: Being a data scientist is applying machine learning 100% of the time

Fact: Being a data scientist is applying machine learning 5% of the time

The other 95% is spent:

- Understanding the Business Problem & Communicating with Domain Experts, 20%

- Working with data: Cleaning, Manipulating, Visualizing, Processing, Transforming, Understanding, 60%

- Communicating Results: Reporting, Slide Decks, and Apps, 15%

—-

Key Point - If you want to be a great data scientist, focus on where you will spend the most of your time. Communication, Business Understanding, Data Manipulation & Visualization

✴️ @AI_Python_EN

Uncovering and Mitigating Algorithmic Bias through Learned Latent Structure

Amini et al.: https://lnkd.in/e5Ybyfa

#artificialintelligence #deeplearning #machinelearning

✴️ @AI_Python_EN

💡 What is a p-value?

When testing an hypothesis, the p-value is the likelihood that we would observe results at least as extreme as our result due purely to random chance if the null hypothesis were true.

💡 What does it mean when a p-value is low?

When the p-value is low, it is relatively rare for the our results to be purely from random variations in observations.

Because of this, we may decide to reject the null hypothesis. If the p-value is below some pre-defined threshold, we say that the result is "statistically significant" and we reject the null hypothesis.

💡 What value is most often used to determine statistical significance?

A value of alpha = 0.05 is most often used as the threshold for statistical significance.

#datascience #statistics

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Different Sorting Algorithms and how they work.

Source- Reddit

An awesome colllection of Deep Learning tutorials (on Github)-
https://lnkd.in/fyAFyK6

✴️ @AI_Python_EN

Top 10 NLP Concepts to analyze text data

Interested in NLP? Get familiar with these 10 algorithms before you get started:

1. tf-idf - https://lnkd.in/ghfqfm7
2. N-grams - https://lnkd.in/gCDChaT
3. Stemming - https://lnkd.in/gwHuE68
4. Lemmatisation - https://lnkd.in/gRU8Q5m
5. Cosine similarity - https://lnkd.in/gEMj9hp
6. Bag-of-words - https://lnkd.in/gzv7NDX
7. Word2vec - https://lnkd.in/gV2yEsn
8. LDA - https://lnkd.in/gF2qcnJ
9. Edit distance - https://lnkd.in/gy3wU5H
10. LSTM - https://lnkd.in/gu9H9vM

Get familiar with these concepts at a high level, and then grab a dataset and start playing around. You'll learn a lot more by implementing these concepts with real data than just reading and studying forever.

Here are two great sources to grab free text datasets:
👉 https://lnkd.in/gABJX4w
👉 https://lnkd.in/gFR9njn

Remember to start simple and then iteratively build and test from there, not every model required deep learning
👉 Get familiar with these 10 concepts and and you'll be ready to conquer challenging NLP problem.

#datascience #machinelearning #nlp #deeplearning #algorithms

✴️ @AI_Python_EN

#NLP is among the hottest and most interesting fields in #datascience. Check out these 5 in-depth and hands-on tutorials to learn #NLP:

1. The Essential NLP Guide to Solve Top 10 Common NLP Tasks - https://lnkd.in/fiXS5Rj
2. Practical Tutorial for Regular Expressions in #Python - https://lnkd.in/fXw-Rdz
3. A Gentle Introduction to #TopicModeling - https://lnkd.in/fDXmt4n
4. Comprehensive and Intuitive Guide to #WordEmbeddings - https://lnkd.in/fvRrFhA
5. #TextClassification using #ULMFiT and #fastai Library in Python - https://lnkd.in/f7bu8jM

And test your #NaturalLanguageProcessing knowledge on this challenging question set!
30 Questions to test a data scientist on Natural Language Processing - https://lnkd.in/fpWBZUh

✴️ @AI_Python_EN