Digests ┬╗ 36

ai

LASER natural language processing toolkit

To accelerate the transfer of natural language processing (NLP) applications to many more languages, we have significantly expanded and enhanced our LASER (Language-Agnostic SEntence Representations) toolkit. We are now open-sourcing our work, making LASER the first successful exploration of massively multilingual sentence representations to be shared publicly with the NLP community. The toolkit now works with more than 90 languages, written in 28 different alphabets. LASER achieves these results by embedding all languages jointly in a single shared space (rather than having a separate model for each). We are now making the multilingual encoder and PyTorch code freely available, along with a multilingual test set for more than 100 languages.

Fundamentals of Data Visualization

The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional. It has grown out of my experience of working with students and postdocs in my laboratory on thousands of data visualizations. Over the years, I have noticed that the same issues arise over and over. I have attempted to collect my accumulated knowledge from these interactions in the form of this book.

Deep Reinforcement Learning with TensorFlow 2.0

In this tutorial I will showcase the upcoming TensorFlow 2.0 features through the lense of deep reinforcement learning (DRL) by implementing an advantage actor-critic (A2C) agent to solve the classic CartPole-v0 environment. While the goal is to showcase TensorFlow 2.0, I will do my best to make the DRL aspect approachable as well, including a brief overview of the field.

Mastering the Data Science Interview Loop

In 2012, Harvard Business Review announced that Data Science will be the sexiest job of the 21st Century. Since then, the hype around data science has only grown. Recent reports have shown that demand for data scientists far exceeds the supply.

Generative Models Tutorial with Demo

Generative models are interesting topic in ML. Generative models are a subset of unsupervised learning that generate new sample/data by using given some training data. There are different types of ways of modelling same distribution of training data: Auto-Regressive models, Auto-Encoders and GANs. In this tutorial, we are focusing theory of generative models, demonstration of generative models, important papers, courses related generative models. It will continue to be updated over time.