TOP 10 STATISTICAL FALACIES

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💡REAL GOLD = Springer Series books💡

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Data Science vs Machine Learning

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Have you heard of "R-Transformer?", a Recurrent Neural Network Enhanced Transformer

Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure.

Therefore, many non-recurrent sequence models that are built on convolution and attention operations have been proposed recently.

Here the authors propose the R-Transformer which enjoys the advantages of both RNNs and the multi-head attention mechanism while avoids their respective drawbacks.

The proposed model can effectively capture both local structures and global long-term dependencies in sequences without any use of position embeddings. They evaluated R-Transformer through extensive experiments with data from a wide range of domains and the empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks.

Github code: https://lnkd.in/dpFckix

#research #algorithms #machinelearning #deeplearning #rnn

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On the “steerability” of generative adversarial networks
pdf: https://arxiv.org/pdf/1907.07171.pdf

abs: https://arxiv.org/abs/1907.07171

github: https://github.com/ali-design/gan_steerability

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

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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

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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

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

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

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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.

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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

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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

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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.

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#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

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5 Common Used NLP Library in Python

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Demystifying #AI

Wonderful infographic by Swami Chanrasekaran

#artificiallintelligence #machinelearning #deeplearning

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