We need your help to oppose the HR1044. This petition is made by the Chinese community in the US. As immigrants they believe the HR1044 will hurt the economy and skew the immigration toward one nation and only one which is India. Please Share it With your Groups and Friends.🌺

https://petitions.whitehouse.gov/petition/do-not-pass-bills-s386-or-hr1044-do-not-pass-bills-s386-or-hr1044-us-worker-and-racial-diversity

10 new posts on datahacker.rs.
Introduction to #ComputerVision using #OpenCV (#Python and #C++)

https://lnkd.in/gZtj_g6

✴️ @AI_Python_EN

Welcome to @ai_machinelearning_big_data the world of :
* #Artificial #Intelligence,
* #Deep #Learning,
* #Machine #Learning,
* #Data #Science
* #Python Programming language
* and more advanced research
links🔗 and more you wanted.
Join us and learn hot topics of Computer Science together.👇👇👇

@ai_machinelearning_big_data

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
✴️ @AI_Python_EN

"Large Memory Layers with Product Keys"
https://arxiv.org/abs/1907.05242

✴️ @AI_Python_EN

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
✴️ @AI_Python_EN

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

✴️ @AI_Python_EN

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)

✴️ @AI_Python_EN