AIQC simplifies data preparation and parameter tuning for batches of deep learning models without an expensive cloud backend.
It empowers researchers by reducing the programming and data science know-how required to integrate machine learning into their research. It makes machine learning less of a black box by reproducibly recording experiments in a file-based database that requires no configuration.
GraphQL is a query language that is rapidly gaining wide adoption across the community.
It combines type validation with a query and filtering syntax that makes it easy to get up-and-running with a powerful web API in almost no time.
Features like running parallel queries or update-all become much easier because they are first-class citizens of GraphQL. Add to that a vibrant community that keeps creating excellent tooling and documentation, it’s clear why GraphQL has become so popular with developers
Every abstraction has a cost, and GraphQL is no exception. The added complexity and a new schema format to parse and execute mean new performance bottlenecks. In addition to performance issues, the wrong use of GraphQL can lead to architectural bottlenecks.
Instead of viewing this as a problem, NearForm took this as a challenge.
Malte Pietsch delivers this keynote on “Transfer Learning – Entering a new era in NLP” at PyData Warsaw 2019
Transfer learning has been changing the NLP landscape tremendously since the release of BERT one year ago. Transformers of all kinds have emerged, dominate most research leaderboards and have made their way into industrial applications. In this talk we will dissect the paradigm of transfer learning and its effects on pipelines, modelling and the engineers mindset.