Python is the main trend in programming IT and technologies .The most actual information about python programming, big data , machine learning , neural networks on channel : @pythonl

Student Guilherme Lopasso from the Springboard AI/ML course worked on a really cool project where he developed a text summarization tool by using a simple extractive approach. In particular, he used 92k articles from CNN stories as input data. Check out his blog post for more details on the project from data review, modeling to production. He also built a simple web interface which is deployed on Heroku.

It's very impressive what he has achieved so far in such short time and we're still not done yet with the course and also the project. Our next step is to use deep learning to do the text summary. #machinelearning

📝 Article: https://lnkd.in/dkF-Aj4
🔤 Code: https://lnkd.in/d8kMPPp
▶️ Demo: https://lnkd.in/dJXJT36

✴️ @AI_Python_EN

Introduction to Deep Learning

#deeplearning #datascience #artificialintelligence

https://lnkd.in/e5NpfXZ

✴️ @AI_Python_EN

Fast Estimating Pedestrian Moving State Based on Single 2D Body Pose by Shallow Neural Network.

Crossing or Not-Crossing (C/NC) problem is important to autonomous vehicles (AVs) to safely interact with pedestrians.

However, this problem setup ignores pedestrians walking along the direction of vehicles' movement (LONG).

To enhance AVs' awareness of pedestrians behavior, authors make the first step towards extending C/NC to C/NC/LONG problem and recognize them based on single body pose.

Paper: https://lnkd.in/dU8SinE
GitHub Code & JAAD dataset coming soon

#sensors #selfdrivingcars #autonomousvehicles #deeplearning

✴️ @AI_Python_EN

Facebook AI and Carnegie Mellon researchers have built Pluribus, the first AI bot to beat elite poker pros in 6 player Texas Hold’em. This breakthrough is the first major benchmark outside of 2 player games and we’re sharing specifics on how we built it.
https://ai.facebook.com/blog/pluribus-first-ai-to-beat-pros-in-6-player-poker/
✴️ @AI_Python_EN

Massively Multilingual NMT in the wild: 100+ languages, 1B+ parameters, trained using 25B+ examples. Check out our new paper for an in depth analysis:
https://arxiv.org/abs/1907.05019
#GoogleAI
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"Large Memory Layers with Product Keys"
https://arxiv.org/abs/1907.05242

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Great summer read: FCOS (Fully Convolutional One-Stage object detection)
https://arxiv.org/abs/1904.01355
Simpler than the already simple RetinaNet architecture, with a couple of neat tricks. Pic below from paper, probably cherry-picked 😇 but still impressive. Every orange is boxed.

✴️ @AI_Python_EN

Playing Go without Game Tree Search Using Convolutional Neural Networks.
http://arxiv.org/abs/1907.04658
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https://towardsdatascience.com/linear-regression-using-flavor-of-python-bcbdc616a944

✴️ @AI_Python_EN

th the enstop package: https://github.com/lmcinnes/enstop #SciPy2019

For Who Have a Passion For:

1. Artificial Intelligence
2. Machine Learning
3. Deep Learning
4. Data Science
5. Computer vision
6. Image Processing
7. Cognitive Neuroscience
8. Research Papers and Related Courses

https://t.me/DeepLearningML

Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology.
http://arxiv.org/abs/1907.05008

✴️ @AI_Python_EN

Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention
https://deepai.org/publication/improving-deep-lesion-detection-using-3d-contextual-and-spatial-attention by Qingyi Tao et al.
#ComputerVision #PatternRecognition

✴️ @AI_Python_EN

REINFORCE (Section 6 of Williams, 1992) *is* an ES, and is very closely related to OpenAI-ES, NES, CMA-ES.

Simple Random Architecture Search with hand-engineered components already gets ~ SOTA for CIFAR10, PTB

https://github.com/hardmaru/gecco-tutorial-2019 …
✴️ @AI_Python_EN

Cheat sheet: TensorFlow, an open source software library for machine learning
https://www.techrepublic.com/article/tensorflow-googles-open-source-software-library-for-machine-learning-the-smart-persons-guide/ …
#machinelearning

✴️ @AI_Python_EN

Jupyter Notebook Documentation

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Time-series analysis (TSA) is used in many fields, including finance, economics, meteorology and marketing.

Despite this, I occasionally hear the criticism that it doesn't actually work because these models assume normally distributed data and cannot handle non-linearities.

Academic researchers have studied time-series analysis for many decades, so this is not an obscure topic. A gigantic amount of research on TSA has been conducted and is public domain.

Time-series data are seldom linear, so the first objection does not make sense. Secondly, normality need not be assumed - this is a myth.

For those who'd like to learn more about TSA, two excellent introductions are:

- Time Series Analysis and Its Applications (Shumway and Stoffer)
- Time Series Analysis (Wei)

There are many others on specialized topics, such as:

- Multiple Time-Series Analysis (Lütkepohl)
- Time Series Analysis by State Space Methods (Durbin and Koopman)
- Time Series Modelling with Unobserved Components (Pelagatti)
- Hidden Markov Models for Time Series (Zucchini)
- GARCH Models (Francq and Zakoïan)
- Handbook of Volatility Models and Their Applications (Bauwens et al.)
- Nonparametric Econometrics (Li and Racine)

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Google Machine Learning Dictionary

https://developers.google.com/machine-learning/glossary/

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Developing the control of an exoskeleton with #Matlab & #Simulink to help patients relearn how to walk. The Statistics & Machine Learning, Curve Fitting Toolboxes were also used.

Students and Mechatronic engineers may want to check out the links in Comment-1 for a Youtube short tutorial on an exoskeleton designed with Simulink plus some research papers that I came across on the topic. There are many, but I simply list 3. See Comment-2 for the link to Lokomat's website (which is mentioned in the MathWorks blog article) to see their exoskeleton products.
https://blogs.mathworks.com/headlines/2017/06/28/robotic-exoskeleton-helps-patients-relearn-how-to-walk/

✴️ @AI_Python_EN

Really awesome #deeplearning #rnn paper 👏🏻 explaining an increase in predicted risk for clinical alerts.

Explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estimates given an input at each time step.

The goal here is to alert a clinician when a patient’s risk of deterioration rises. The clinician then has to decide whether to intervene and adjust the treatment.

Given a potentially long sequence of new events since she last saw the patient, a concise explanation helps her to quickly triage the alert.

Authors developed methods to lift static attribution techniques to the dynamical setting, where they identified and addressed challenges specific to dynamics.

They then experimentally assess the utility of different explanations of clinical alerts through expert evaluation.

Here’s full paper: https://lnkd.in/duMQYyW
#healthcare #diagnostics #clinical
#prediction
✴️ @AI_Python_EN