Learning World Graphs to Accelerate Hierarchical Reinforcement Learning

An autonomous agent often counters various tasks within a single complex environment. Our two-stage framework proposes to first build a simple directed weighted graph abstraction over the world in an unsupervised task-agnostic manner and then to accelerate the hierarchical reinforcement learning of a diversity of downstream tasks.

Paper: http://bit.ly/2MfmWeq

http://bit.ly/2MfmXz0

MIT Deep Learning Basics: Introduction and Overview with TensorFlow

This blog post provides an overview of deep learning in 7 architectural paradigms with links to TensorFlow tutorials for each. It accompanies the following lecture on Deep Learning Basics as part of MIT course 6.S094.

http://bit.ly/2Mnf1vs

Personalized Recommendations for Experiences Using Deep Learning

In this blog post, you will learn how TripAdvisor’s newly-developed ‘Recommended For You’ (RFY) model generates personalized recommendations on their website using users’ browsing history and deep learning.

http://bit.ly/2MgiDPU

Introducing Dagster

Dagster is an open-source Python library for building systems like ETL processes and ML pipelines.

GitHub: http://bit.ly/2MkyYTP

http://bit.ly/2Mkw4yh

Python Machine Learning Tutorial: Predicting Airbnb Prices

This tutorial will introduce you to the fundamental concepts of machine learning. As you follow along, you’ll build your very first model from scratch to make predictions while developing a solid understanding of how exactly how your model works.

http://bit.ly/2Mn39JZ

Using DVC to create an efficient version control system for data projects

In this article, you will learn what DVC is and how to use it to track the project’s data. DVC simplifies Data projects by using stages and pipelines, which increases productivity and improves collaboration.

http://bit.ly/2MvgLmN

ICML 2019 Videos

Video and slides from the International Conference on Machine Learning

http://bit.ly/2ys9uvG

How to Develop an Information Maximizing GAN (InfoGAN) in Keras

In this tutorial, you will learn how to develop and train an InfoGAN model from scratch and use the control variables to control which digit is generated by the model.

http://bit.ly/2MxWpJB

#ML #AI #DataScience

Neural Architecture Search at CVPR 2019

In this article, you will learn about neural architecture search (NAS) and how it was presented at CVPR 2019 in Long Beach.

http://bit.ly/333nSbH

PyTorch Transformers for state-of-the-art NLP

If you're doing anything with NLP, this is a great new open-source tool to know about. This library from Hugging Face contains 27 pre-trained models to conduct state-of-the-art NLP/NLU tasks, including BERT, GPT-2, XLNet, etc. It's a unified API too, which makes it easy to use and experiment with the latest techniques.

http://bit.ly/337Mgch

Cyclical Learning Rates with Keras and Deep Learning

In this tutorial, you will learn how to use Cyclical Learning Rates (CLR) and Keras to train your own neural networks. Using Cyclical Learning Rates you can dramatically reduce the number of experiments required to tune and find an optimal learning rate for your model.

http://bit.ly/2MEAAbc

A Gentle Introduction to CycleGAN for Image Translation

In this article, you will discover the CycleGAN technique for unpaired image-to-image translation.

http://bit.ly/2MEGI33

How I built a spreadsheet app with Python to make data science easier

Grid studio is a new open-source application that integrates Python with something that looks a lot like a typical spreadsheet. The spreadsheet can be edited directly and grid cells can contain numbers, text or arbitrary functions. It's said to have full Python integration, including libraries such as scikit-learn, numpy and pandas.

GitHub: http://bit.ly/2MJH5JK

http://bit.ly/2MJaP9y

Awesome production machine learning

This repository contains a curated list of excellent open-source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning.

http://bit.ly/2MUty2n

​​Data Fest Odessa - September 7

A global series of free conferences hosted by ods.ai where researchers, engineers, and developers in Data Science come together to explore CV, NLP, SysML, and related business use cases and opportunities.

Become a speaker: http://bit.ly/2YyVBLq

Registration and more information: http://bit.ly/2YyVAqQ

Browse the State-of-the-Art in Machine Learning

1146 leaderboards, 1223 tasks, 1105 datasets and 14779 papers with code will help you track the state-of-the-art in ML.

http://bit.ly/2Yy3NvE

U-GAT-IT

Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

http://bit.ly/2N0Zlyr

​​Data Science UA — 7th Conference on Machine Learning, Artificial Intelligence and Data Science in Kyiv. Productive networking and engineering insights. Over 500 participants and 20 speakers, 3 streams.

Buy tickets: http://bit.ly/2YKlfNp

10% promo code: DSUA_Digest

Advances in Conversational AI

New open-source data sets, algorithms, and models that improve five common weaknesses of open-domain chatbots today: consistency, specificity, empathy, knowledgeability, and multimodal understanding.

http://bit.ly/2MYTfyx

Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks

These "simple rules" for Jupyter Notebooks amount to a set of best practices for ensuring that your work is maintainable, reproducible and easy to follow.

http://bit.ly/2YOasSG

​​Last Chance to save 40% on tickets to the Deep Learning Summit, Europe

The world's leading Deep Learning Summit will be in London in September! Secure your summit pass with 40% off using code DSDIGEST.

https://bit.ly/2JN5Kv6