Sinkhorn iteration for Optimal Transport with TF. Code:
https://colab.research.google.com/github/znah/notebooks/blob/master/mini_sinkhorn.ipynb

✴️ @AI_Python_EN

how weak supervision can train deep learning models using unlabeled cardiac MRI sequences/videos

Key idea: use Snorkel to transform cardiologist domain knowledge into labeling functions --simple rules which capture information about our task-- to take advantage of the massive scale of unlabeled imaging data available in biobank.

This lets us rapidly build training sets for classification tasks such as bicuspid aortic valve, where a Snorkel-based approach improved our end model F1 performance by 64%.

This lets us rapidly build training sets for classification tasks such as bicuspid aortic valve, where a Snorkel-based approach improved our end model F1 performance by 64%.
https://www.nature.com/articles/s41467-019-11012-3

✴️ @AI_Python_EN

How to ship ML in practice:

1/ Write a simple rule based solution to cover 80% of use cases

2/ Write a simple ML algorithm to cover 95% of cases

3/ Write a filtering algorithm to route inputs to the correct method

4/ Add monitoring

5/ Detect drift

...

24/ Deep Learning

✴️ @AI_Python_EN

'Artificial intelligence' fit to monitor volcanoes: Platform uses 'machine learning' to analyse satellite data

https://www.sciencedaily.com/releases/2019/07/190715103313.htm

✴️ @AI_Python_EN

EasyGen, a visual programming language for text data pipelines for neural nets.

Colab: https://drive.google.com/open?id=1XNiOuNtMnItl5CPGvRjEvj9C78nDuvXj
Github: https://github.com/markriedl/easygen …

Here’s a program to scrape the web for generating Star Trek/romance book titles.

✴️ @AI_Python_EN

Pytorch Implementation of Deep Flow.

#BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #TensorFlow #CloudComputing #DataScientist #Linux

https://arxiv.org/pdf/1905.02884.pdf

✴️ @AI_Python_EN

This is our new paper at KDD, proposing a new dataset for US traffic records with several attributes. We also did some data analysis to show its potential for further uses in AI and data mining. Hope to see you at KDD 19

https://www.youtube.com/watch?v=FhWO_uTf2Ho&t=2s

✴️ @AI_Python_EN

✴️ @AI_Python_EN

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

In statistical modeling, apparent violations of distributional assumptions (e.g., normality, Poisson) may result from heterogeneity we haven't accounted for.

In plain English, these violations may result from outliers, for example. Outliers can tell us a lot about are data (e.g., why most people are this way and not that way). They may also be hinting that something is amiss with our data.

Violations of distributional assumptions may also tell us we haven't thought about the problem enough! For example, we may be trying to force a one-size-fits-all model on our data. But different types of consumers may have different motivations, so a regression model that aims at the middle may miss most of them. People also transition in and out of different "states" because of marriage, promotions, childbirth, etc.

One reason predictive analytics often goes wrong is because of people working in factory-like (or sweatshop-like) environments being forced to crank out as many models as possible as quickly as possible.

Well-trained analysts given more time to think about the data and business problem can extract more value from the same data. So the problem may lie more with the execution than the idea itself.

✴️ @AI_Python_EN

#Detection ...? or #Classification ...? its all there in #PyTorch.
A pytorch lib with state-of-the-art architectures, pre-trained models and real-time updated results.
#deeplearning #ai #cnn

https://lnkd.in/eTdvfEp

✴️ @AI_Python_EN

5 Common Used NLP Library in Python

✴️ @AI_Python_EN

Demystifying #AI

Wonderful infographic by Swami Chanrasekaran

#artificiallintelligence #machinelearning #deeplearning

✴️ @AI_Python_EN

✴️ @AI_Python_EN

Amazing!! Deep Learning-based NLP techniques are going to revolutionize the way we write software. Here's Deep TabNine, a GPT-2 model trained on around 2 million files from GitHub. Details at
https://tabnine.com/blog/deep
#nlp

✴️ @AI_Python_EN

WikiMatrix: large-scale bitext extraction from Wikipedia:
1620 language pairs in 85 languages, 135M parallel sentences,
Systematic evaluation on TED
Paper:
https://arxiv.org/abs/1907.05791
Data:
https://github.com/facebookresearch/LASER/tree/master/tasks/WikiMatrix

✴️ @AI_Python_EN

If you're doing anything with NLP, this is a great place to start! A PyTorch library of state-of-the-art pretrained Transformer language models featuring BERT, GPT-2, XLNet, and more.
"pytorch-transformers", the library for Natural Language Processing!
https://github.com/huggingface/pytorch-transformers

✴️ @AI_Python_EN

It's out! I really think the network portrait divergence is a clever way to compare two networks--props to Jim Bagrow & Bollt for introducing it.

Paper:
https://appliednetsci.springeropen.com/articles/10.1007/s41109-019-0156-x

Code:
https://github.com/bagrow/network-portrait-divergence

✴️ @AI_Python_EN

Remember the #BachDoodle? We’re excited to release paper on Behind-the-Scenes design, #ML, scaling it up, and dataset of 21.6M melodies from around the world!
📜 http://arxiv.org/abs/1907.06637

✴️ @AI_Python_EN

This #AI magically removes

moving #objects from #videos

https://buff.ly/2XJtPf3
#ArtificialIntelligence #MachineLearning #DeepLearning
Fast Online Object Tracking and Segmentation: A Unifying Approach
✴️ @AI_Python_EN