#Netflix Open Sources Polynote to Make #DataScience Notebooks Better
The new notebook environment provides substantial improvements to streamline experimentation in #MachineLearning workflows.

Link to the paper

🔭 @DeepGravity

A lot of #DataScience Cheatsheets such as #DeepLearning #Python #Docker

Link to the Github repo

🔭 @DeepGravity

The Ultimate guide to #AI, #DataScience & #MachineLearning, Articles, Cheatsheets and Tutorials ALL in one place

This is a carefully curated compendium of articles & tutorials covering all things AI, Data Science & Machine Learning for the beginner to advanced practitioner. I will be periodically updating this document with popular topics from time to time. My hope is that you find something of use and/or the content will generate ideas for you to pursue.

Link to the article

🔭 @DeepGravity

If you are seeking a job in #DataScience worldwide, check this facebook group

#Job

🔭 @DeepGravity

#DataScience Interview Preparation

#Interview

🔭 @DeepGravity

Free #AI #Resources
Find The Most Updated and Free #ArtificialIntelligence, #MachineLearning, #DataScience, #DeepLearning, #Mathematics, #Python Programming Resources. (Last Update: December 4, 2019)

Link

🔭 @DeepGravity

Various #datascience roles

🔭 @DeepGravity

A good Telegram channel, managed by an Iranian researcher, covering some new papers in #AI

Link to the channel

🔭 @DeepGravity

A Complete Guide To #Math And #Statistics For #DataScience

Link

Deep (Learning) Gravity, [02.01.20 09:39]
Asymmetric #GAN for Unpaired Image-to-image Translation

Unpaired image-to-image translation problem aims to model the mapping from one domain to another with unpaired training data. Current works like the well-acknowledged Cycle GAN provide a general solution for any two domains through modeling injective mappings with a symmetric structure. While in situations where two domains are asymmetric in complexity, i.e., the amount of information between two domains is different, these approaches pose problems of poor generation quality, mapping ambiguity, and model sensitivity. To address these issues, we propose Asymmetric GAN (AsymGAN) to adapt the asymmetric domains by introducing an auxiliary variable (aux) to learn the extra information for transferring from the information-poor domain to the information-rich domain, which improves the performance of state-of-the-art approaches in the following ways. First, aux better balances the information between two domains which benefits the quality of generation. Second, the imbalance of information commonly leads to mapping ambiguity, where we are able to model one-to-many mappings by tuning aux, and furthermore, our aux is controllable. Third, the training of Cycle GAN can easily make the generator pair sensitive to small disturbances and variations while our model decouples the ill-conditioned relevance of generators by injecting aux during training. We verify the effectiveness of our proposed method both qualitatively and quantitatively on asymmetric situation, label-photo task, on Cityscapes and Helen datasets, and show many applications of asymmetric image translations. In conclusion, our AsymGAN provides a better solution for unpaired image-to-image translation in asymmetric domains.

Paper

🔭 @DeepGravity