Beautiful Playground - #styleGAN is on Github https://github.com/NVlabs/stylegan | Yes its stunning - so is its energy consumption | i cant really tell why the environmental impacts of those emerging tech is not discussed as much as they should be | #gan #MachineLearning

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Fantastic in-depth piece by skynet on job loss due to AI. The main argument: AI will (most likely) *not be significantly more disruptive* than the impact of automation in the past. https://www.skynettoday.com/editorials/ai-automation-job-loss

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NEED COMMUNITY ON DATA ENGINEERING?

Andreas Kretz created a publication on Medium dedicated to data engineering and big data. Not for himself, but for us, who need reliable data pipeline on dayjob. To bring attention to this super important topic. A place where you can write about data engineering. If you don't know, he always give quality content about data engineering. I think this can be great place to discuss out about:

- Distributed processing
- Big Data and Platform Architecture
- SQL and NoSql databases
- Visualization tools

To help you learn interesting stuff and get your ideas and knowledge on front of people. Build your reputation to easier find a job if you need one.

So, please check it out, become a writer and send in your articles 📕

Plumbers of Data Science on Medium:
https://lnkd.in/dU4fPRU

What do you think? 👍 or 👎


PS: If you are looking for a publication aimed at data scientists check out www.towardsdatascience.com

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#DataScienceInsights

Data Integrity Leads to Data Quality.
Data Quality Leads to Quality Insights.
Quality Insights Lead to Courageous Decisions.
Courageous Decisions Lead to Desired Transformation.

For one to make the Desired Transformation, the Courageous Decisions have to be Backed up by a Sustained Data Integrity and Quality, Quality Insights and Trust in the Data.

Agree?

#datainsights #transformations

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👉 If you like our channel, i invite you to share it with your friends:
Our channel in english: ✴️ @AI_Python_EN
Our Daily arXiv Channel: 🗣 @AI_Python_Arxiv

BTW: Thank you for joining :)

Here’s a list of 10 GREAT #SelfStarting #DataScience Projects to work on:

➖BEGINNER➖

⚠️ NOTE: The links provided will redirect you to a recommended Kaggle Kernel that I enjoyed. Use it as a reference before starting on your project :)

⚠️ IMPORTANT: Number 10 is a MUST DO!

1. Pokemon - Weedle's Cave 🐛
Python - https://lnkd.in/gcKWWQ2

2. Titanic ML 🚢
Python - https://lnkd.in/gafie9m
R - https://lnkd.in/gRRa7HV

3. Housing Prices Prediction 🏡
Python - https://lnkd.in/gX2FSDk
R - https://lnkd.in/ggFJSyd


➖INTERMEDIATE➖

4. Instacart Market Basket Analysis 🛒
Python - https://lnkd.in/gkNaXqH
R- https://lnkd.in/g2gthxu

5. Quora Question Pairs 👥
Project :https://lnkd.in/f3HQZsT
Tutorial (Python)- https://lnkd.in/fEzf-Xp

6. Human Resource Analytics 🕴🏻
Python - https://lnkd.in/gVUPfWm
R -https://lnkd.in/gHusQYX


➖ADVANCED➖

7. Analyzing Soccer Player Faces ⚽️
Python - https://lnkd.in/gUys_TS

8. Recruit Restaurant Visitor Forecasting 🍱
Python - https://lnkd.in/gjQvf74

9. TensorFlow Speech Recognition 🗣
Python - https://lnkd.in/g8SSPfW


➖MASTERY➖

10. Not Enough?
This is more complete guide from Analytics Vidhya
https://lnkd.in/g_QjzGe.

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TF-REPLICATOR: DISTRIBUTED MACHINE LEARNING FOR RESEARCHERS #DataScience #MachineLearning #ArtificialIntelligence
http://bit.ly/2BmoLzV

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Multiview learning is an important branch of deep learning. During learning, multiview learning explicitly uses multiple distinct representations of data, and models the relationship between them and/or the results from downstream computations.

these “representation” can either be the original features of the data or the features obtained through some computations.
The approach to utilize these representations is to simply concatenate them into a single representation to perform learning is greatly useful in Cross-Media Intelligence, Intelligent Medical AI systems, Autonomous Driving and many other industry domains.

Advantages? It better utilizes the structural information in the data, which leads to better model- ing capability. Further, multiple views can act as complementary and collaborative normalization, effectively reducing the size of the hypothesis space.

To learn more see here an example of Deep Multi-View Concept Learning: https://lnkd.in/dckzjBR
#deeplearning #machinelearning

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Troubleshooting Deep Neural Networks

A Field Guide to Fixing Your Model, by Josh Tobin: https://lnkd.in/eNB-vci

#artificalintelligence #deeplearning #neuralnetworks

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Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition #DataScience #MachineLearning
#ArtificialIntelligence
http://bit.ly/2Blf7xH

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Can neural networks learn commonsense reasoning?

ATOMIC | An Atlas of Machine Commonsense for If-Then Reasoning: https://homes.cs.washington.edu/~msap/atomic/

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Lex Fridman : First blog post on Deep Learning Basics with TensorFlow (on the official TensorFlow page):
https://medium.com/tensorflow/mit-deep-learning-basics-introduction-and-overview-with-tensorflow-355bcd26baf0

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We've opened another position at AYLIEN: complete a PhD or MSc while working on our Research team! Get in touch fast with a CV and a cover letter outlining a research proposal (open to worldwide students willing to relocate to Dublin) #NLProc #deeplearning

🌎 Link Review

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🗣 @AI_Python_arXiv.

The Helmholtz-Gemeinschaft Deutscher Forschungszentren is offering currently over 300 open positions for international PhD students, Postdocs and researchers in various research fields. Check out all job vacancies: http://bit.ly/2K26X2L

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The Karlsruher Institut für Technologie (KIT) is inviting applications for 12 fully funded PhD positions in data science & health.

Graduates with master degrees in computer science, maths, engineering, physics can apply until 17 Feb 2019.

http://bit.ly/2VWlT5C

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🗒Word Embeddings for Sentiment Analysis: A Comprehensive Empirical Survey.
📗PDF

#arXiv
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SELECTED MISTAKE THAT TOO COMMON FROM DATA SCIENCE ASPIRANTS

Getting that first break in #DataScience is tough. Check out these 4 awesome articles to learn tips and tricks from experts on how to have a fulfilling career in this field:

1. 13 Common Mistakes Amateur #DataScientists Make and How to Avoid Them - https://lnkd.in/f348chG

2. Busted! 11 Myths Data Science Transitioners Need to Avoid - https://lnkd.in/fmygG9B

3. 4 Secrets for a Future Ready Career in Data Science - https://lnkd.in/feNxs8b

4. The Most Comprehensive Data Science & #MachineLearning Interview Guide You’ll Ever Need - https://lnkd.in/fR2uGgE

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Can you predict fluid intelligence from T1-weighed MRI?

The ABCD Neurocognitive Prediction Challenge (ABCD-NP-Challenge 2019; https://sibis.sri.com/abcd-np-challenge) invites researchers to submit their method for predicting fluid intelligence from T1-weighed MRI (about 8.5K subjects in total, age 9-10 years). The data of 4.1k individuals are provided for training. The accuracy of each method will be assessed on its predicted fluid intelligence scores of the remaining 4.4K children, whose actual scores will be revealed after the challenge deadline. Downloading the data requires approval by NIH NDAR, which will require sign off by the institution you are affiliated with. So start the application process (https://sibis.sri.com/abcd-np-challenge/assets/docs/abcd-np-challenge-getting_data_access.pdf) early. Please also sign up to the mailing list (https://mailman.ucsd.edu/mailman/listinfo/abcd-npc-l) to receive updates about the challenge.

Important Dates:
Feb 15, 2019: Team Registration Deadline
Mar 10, 2019: Submit Results and Code
Mar 17, 2019: Submit Manuscript
Oct 2019: Meeting (in conjugation with MICCAI 2019, Shenzhen, China -http://www.miccai2019.org)

For more information, please visit http://sibis.sri.com/abcd-np-challenge

Organizers:
Wes Thompson, University of California – San Diego
Kilian M. Pohl, SRI International
Co-Chairs:
Ehsan Adeli, Stanford University
Bennett A. Landman, Vanderbilt University
Marius G. Linguraru, Children's National Health System
Susan F. Tapert, University of California – San Diego

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DevOps / Data Visualization / Deep Learning resources

https://bogotobogo.com

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Google has just released a very interesting and important paper on federated learning (FL). FL is a distributed machine learning approach which enables model training on a large corpus of decentralized data. This is a pretty huge deal because now you're able to train deep learning models without moving the data out of mobiles phones. Instead you leave it there, train it on the phone and then just send the model weights to a global model that sits somewhere on a server. This is a pretty good solution for data privacy.

Besides the theory, they've also built a scalable production system with TensorFlow which applies federated learning on Android phones. For example, they used it to improve their next word prediction feature by training a RNN model on it. Other use cases are on-device item ranking and content suggestions for on-device keyboard. Very interesting! Definitely read the paper! #deeplearning #machinelearning

Paper: https://lnkd.in/guhF_NW

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The authors of [1] used Recurrent Neural Network (RNN) model which produced state of the art performance on the user purchase prediction problem in Ecommerce without using explicit features. The model is straightforward to implement and generalizes to different datasets with comparable performance. RNN & its variants LSTM, GRU (Gated Recurrent Units) are widely available in both open source projects & commercial software. For #Matlab users, LSTM is available in the Deep Learning (DL) toolbox, see [2].

Other relevant posts on #CustomerAnalytics are here: https://lnkd.in/gnNNT4S

Abstract:
A neural network for predicting purchasing intent is presented in an Ecommerce setting to address the significant investment in feature engineering that is usually associated with state-of-the-art methods such as Gradient Boosted Machines (GBM). A trainable vector spaces is used to model varied, semi-structured input data comprising categoricals, quantities & unique instances. Multi-layer recurrent neural networks capture both session-local & dataset-global event dependencies and relationships for user sessions of any length.

[1] " Predicting purchasing intent - Automatic Feature Learning using RNN "-pdf
https://lnkd.in/gATYtxj

[2] " MathWorks #DeepLearning Toolbox "
https://lnkd.in/g3r_S9V

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