Data Science Deep Learning Machine-Learning Natural Language Processing
Getting started with a natural language processing and neural networks is easier nowadays thanks to the numerous talks and tutorials. The goal is to dive deeper for those who already know the basics, or want to expand their knowledge in a machine learning field.
The talk will start with the common use cases that can be generalized to the specific problems in a NLP world. Then I will present an overview of possible features that we can use as input to our network, and show that even simple feature engineering can change our results.
Furthermore, I will compare different network architectures - starting with the fully connected networks, through convolution neural networks to recursive neural networks. I will not only considering the good parts, but also - what is usually overlooked - pitfalls of every solution.
All of these will be done considering number of parameters, which transfers into training and prediction costs and time. I will also share a number of “tricks” that enables getting the best results even out of the simple architectures, as these are usually the fastest and quite often hard to beat, at the same time being the easiest to interpret.
Type: Talk (30 mins); Python level: Beginner; Domain level: Intermediate
Hubert is a backend engineer at Brainly, world's largest social learning platform for students that handle over 100M users monthly. Hubert has started his career as PHP developer, but after joining current company has been contributing to design and implementation of µservice architecture in both Go and Python. A typical service, for which Huber's team is responsible, handles thousands of request per second.