Tutorials on NLP from #NAACL2019. Thanks to the authors for sharing them with us to learn.

Deep Adversarial Learning for NLP - https://lnkd.in/fS9rCEv
Natural Language Inference with Deep Learning - https://lnkd.in/fk6MZea
Transfer Learning in NLP - https://lnkd.in/f6S8R6S

✴️ @AI_Python_EN

3DPalsyNet: A Facial Palsy Grading and Motion Recognition Framework using Fully 3D Convolutional Neural Networks
Researchers: Gary Storey, Richard Jiang, Shelagh Keogh, Ahmed Bouridane, Chang-Tsun Li
Paper: http://ow.ly/9aMK50uuW68
#artificialinteligence #machineleaning #bigdata #machinelearning #deeplearning

✴️ @AI_Python_EN

Empowering you to use machine learning to get valuable insights from data.

🔥 Implement basic ML algorithms and deep neural networks with PyTorch.
🖥 Run everything on the browser without any set up using Google Colab.
📦 Learn object-oriented ML to code for products, not just tutorials.

Github Link - https://lnkd.in/f8nu8UR

#datascience #data #dataanalysis #ml #machinelearning #deeplearning #ai #artificialintelligence

✴️ @AI_Python_EN

Sketch2code: Generating a website from a paper mockup
Researcher: Alex Robinson
Paper: http://ow.ly/zXHK50uuW3g
#artificialinteligence #machineleaning #bigdata #machinelearning #deeplearning
✴️ @AI_Python_EN

Roborace is the first racing competition for both autonomous and manual cars. The cars are all electric and have the same power specifications. Teams compete in developing the AI software.

They recently held an event in Spain:
https://lnkd.in/eDTAz8H

Roborace official site:
https://roborace.com/

#ArtificialIntelligence #MachineLearning #ComputerVision

✴️ @AI_Python_EN

EfficientNets: a family of more efficient & accurate image classification models. Found by architecture search and scaled up by one weird trick. Link: https://arxiv.org/abs/1905.11946 Github: https://bit.ly/30UojnC Blog: https://bit.ly/2JKY3qt

On the Fairness of Disentangled Representations

Locatello et al.: https://lnkd.in/d6DV-gX

#ArtificialIntelligence #DeepLearning #MachineLearning

✴️ @AI_Python_EN

FREE Online Classes to learn Data Science, Blockchain, Big Data :

Just choose your learning path, finish the courses and put the #Badge in your LinkedIn profile to attract more recruiters!

✅The learning path:
https://lnkd.in/gKTnANk

💡List of some the courses:
1)Introduction to Data Science
https://lnkd.in/fF79bEj
2)Data Science Tools
https://lnkd.in/fYf2ZC8
3)Data Science Methodology
https://lnkd.in/fY6Kwqd
4)Statistics
https://lnkd.in/fpgJf7D
5)Predictive Modeling Fundamentals I
https://lnkd.in/f9_Y7UZ
6)Python for Data Science
https://lnkd.in/fy8E2wH
7)Data Analysis with Python
https://lnkd.in/fRQWByd
8)Data Visualization with Python
https://lnkd.in/fFu93ME
9)Machine Learning with Python
https://lnkd.in/f_7r534
10)Deep Learning Fundamentals
https://lnkd.in/fNvPvix
11)Deep Learning with TensorFlow
https://lnkd.in/ftfRtvQ
and many more...

📚Don't miss top 5 free essential books for Data scientists:
https://lnkd.in/gKYqpfV

#datascience #deeplearning #python #machinelearning #ai #hadoop #bigdata #scala #kubernetes #blockchain

✴️ @AI_Python_EN

Notes from Karpathy on common mistakes when training NN
http://karpathy.github.io/2019/04/25/recipe/

✴️ @AI_Python_EN

The Beijing Academy of Artificial Intelligence publishes AI ethics guidelines.
Yes, the protection of individual privacy is mentioned.

Commentary at MIT Tech Review: https://www.technologyreview.com/s/613610/why-does-china-suddenly-care-about-ai-ethics-and-privacy/

✴️ @AI_Python_EN

Group For Who Have a Passion For:

1. Artificial Intelligence
2. Machine Learning
3. Deep Learning
4. Data Science
5. Computer vision
6. Image Processing

https://t.me/joinchat/Ly1-vFOq9aR4mjpIDwzoHA

✴️ @AI_Python_EN

Discovering Neural Wirings (https://lnkd.in/dQ3aauz)
In the past years developing deep neural architectures either required manual design (e.g. AlexNet, ResNet, MobileNet, ...) or required expensive search among possible predefined block structures of layers (NAS, MNas, DART,...). What if we see a neural network as a completely unstructured graph? where each node is running a simple sensing operation over a single data-point or a channel (e.g. 2d filter) and all the nodes are wired up massively in the network. In this paper we explain how to discover a good wiring of a neural network that minimizes the loss function with a limited amount of computation. We relax the typical notion of layers and instead enable channels to form connections independent of each other. This allows for a much larger space of possible networks. The wiring of our network is not fixed during training – as we learn the network parameters we also learn the structure itself.

✴️ @AI_Python_EN

Convolutional Neural Networks cheatsheet By Afshine Amidi and Shervine Amidi

https://lnkd.in/dxEcGFf

#deeplearning

✴️ @AI_Python_EN

100 Plus data Visualizations explained with examples - DataVizProject
https://datavizproject.com/

✴️ @AI_Python_EN

If you're following machine learning, statistics, artificial intelligence posts and even memes, you would have seen usually people sharing comments like machine learning is nothing but bunch of if-else statements and remove your mask - machine learning: I'm just fancy statistics.

Statistics is the key to understand and work with machine learning models. It is the grammar of data science.

You want to understand more about the important concepts? Found a great series of posts which discusses about the important concepts. Use the below link.

Link - https://lnkd.in/f53FP2H

✴️ @AI_Python_EN

DeepMind Made a Math Test For Neural Networks

https://www.youtube.com/watch?v=f9z1I_81_Q4

✴️ @AI_Python_EN

Artificial Intelligence Full Course | Artificial Intelligence Tutorial for Beginners

https://www.youtube.com/watch?v=JMUxmLyrhSk

✴️ @AI_Python_EN

Learning Perceptually-Aligned Representations via Adversarial Robustness

Article: https://arxiv.org/abs/1906.00945
Github: https://github.com/MadryLab/robust_representations

✴️ @AI_Python_EN

Introducing TensorNetwork, an Open Source Library for Efficient Tensor Calculations

http://ai.googleblog.com/2019/06/introducing-tensornetwork-open-source.html

✴️ @AI_Python_EN

Free COURSE. CS Deep Reinforcement Learning UC Berkeley

Video Lectures: https://www.youtube.com/playlist?list=PLkFD6_40KJIxJM..

Lecture Material: http://rail.eecs.berkeley.edu/deeprlcourse/

✴️ @AI_Python_EN

Welcome to TensorWatch

TensorWatch is a debugging and visualization tool designed for deep learning and reinforcement learning from Microsoft Research. It works in Jupyter Notebook to show real-time visualizations of your machine learning training and perform several other key visualizations of your models and data.

https://github.com/microsoft/tensorwatch/

✴️ @AI_Python_EN