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

❇️ @AI_Python
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

Troubleshooting Deep Neural Networks

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

#artificalintelligence #deeplearning #neuralnetworks

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

Can neural networks learn commonsense reasoning?

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv.

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv.

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

#arXiv
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python

DevOps / Data Visualization / Deep Learning resources

https://bogotobogo.com

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

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

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

A reminder for engineers trying to build #AI systems that achieve human-level performance. It's often a lot harder than we at first realize. Humans are amazing. Source lecture (on self-driving cars): https://lnkd.in/e64Dan5

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

You do probably know that GPUs are often used in modern #neuralnetworks. And you probably know that it's because of matrix multiplications. But what GPUs have to do with matrices? Here's what:

http://petewarden.com/2015/04/20/why-gemm-is-at-the-heart-of-deep-learning/

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

Machine learning can definitely be fun with ml5.js, which is a high-level interface to #TensorFlow.js.

ml5.js provides immediate access in the browser to pre-trained models for detecting human poses, generating text, styling an image with another, composing music, pitch detection, and common English language word relationships.

This library intends to make machine learning approachable for a broad audience of artists, creative coders, and students by accessing ML models in the browser without any external dependencies.

A lot of excellent examples and learning references are present at their website at https://ml5js.org/ which they aptly call 'Friendly #MachineLearning for the Web'.

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

A framework for probabilistic modeling
https://lnkd.in/eWb-maA #machinelearning

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

Our friends in Tampere University are hiring now:
15 fully-funded PhD job openings for 3 years within H2020 A-WEAR European Joint Doctorate

Target audience: fresh MSc graduates in various engineering fields (who have completed their first master no earlier than Fall 2015) and who are passionate about pursuing a PhD in a research field of high relevance to today’s society (wearable computing & IoT).


Job description: fully funded 36 months PhD positions towards double/joint PhD programs in 5 top European technical universities in Finland, Italy, Spain, Czech Republic, and Romania

Gross salary (approx. in EUR/month): 3600 (FI), 2800 (ES), 2000 (RO), 2400 (CZ), 2900 (IT)

Application deadline: 28th of February 2019

Starting time of the PhD: Fall 2019
https://www.tuni.fi/en
https://lnkd.in/eyDattx

#universities #graduations #phd #funding #research

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv