Recurrent Neural Networks with TF2.0

In this session we will explore Recurrent Neural Networks (RNN) – a type of neural networks specially designed to process sequences – and their applications to time series and text processing (NLP). To make the session even more interesting, all the code will be developed using the latest version of TensorFlow 2.0, using the implementation of the models to discuss the major changes with respect to versions 1.x of the Deep Learning framework, and it will leverage MLFLlow within Azure Databricks as a development platform and model serving.


Pablo Doval

Principal Data Architect at Plain Concepts & Microsoft AI MVP

General Manager of Plain Concepts in the UK. With a background in relational databases, data warehousing, and traditional BI projects, I have spent the last years in my former role as Data Team Lead, architecting and building Big Data and Machine Learning projects for customers in different sectors, such as Healthcare, Digital Media, Retail and Industry.

Eduardo Matallanas

Data Engineer at Plain Concepts

I am a Ph.D. in the areas of Computer Science and Energy Management by Universidad Politécnica de Madrid (UPM). Member of the scientific community with numerous publications in international journals in the field of applying AI algorithms. I spent over a decade dedicated to the application and study of AI algorithms in different areas: digital signal processing, robotics, image and speech processing, etc. I have also developed different applications with virtual assistants and chatbots for the accompaniment of workers.

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