Biography

I am a machine learning engineer at Vinted and the dean of Vilnius School of AI. I help people learn to apply deep learning techniques and take their first steps in the deep learning field. I am passionate about artificial intelligence and its application in business.

Workshop

Introduction to Deep Learning with Tensorflow 2.0

Abstract

In this workshop you will learn the fundamentals of deep learning using Tensorlfow 2.0 machine learning library. We will build two deep learning models: an image classifier using computer vision and a movie review classifier using natural language processing. This workshops works for people, who have used a different deep learning library before and want to learn the basics of Tensorflow 2.0 as well as machine learning engineers, data scientists and Python developers, who want to learn the fundamentals of deep learning using the latest technologies.

Agenda

Part 1: Introduction to deep learning:

• Fundamentals of machine learning
• Deep learning basics
• Building blocks of neural networks

Part 2: Deep learning applications

• Deep learning for tabular data
• Deep learning for computer vision
• Deep learning for text and sequences
• Generative deep learning

Part 3: Introduction to Tensorflow 2.0

• Basic concepts and general overview of its functionalities
• keras API
• data module
• Eager execution
• Building models
• Training models
• Validating models
• Using models for prediction

Part 4: Hands-on examples

• Computer vision example using image classifier
• NLP example using fake news classifier
• Tabular data example using price regressor

The goal of this workshop is to introduce the participants with deep learning using Tensorflow 2.0 deep learning library.

This workshop is for people who want to learn more about and start using deep learning or want to learn the Tensorflow 2.0 library. No previous knowledge of deep learning is needed, but machine learning fundamentals will help the participants to take the most out of this workshop. Intermediate level of Python knowledge is required.

• Installations:
  • Anaconda environment with Python 3.7 and Tensorflow 2.0 library installed.
• Technical knowledge:
  • Intermediate level of Python
  • Machine learning fundamentals
  • Basic math knowledge
  • Basic statistics knowledge