About This Event

ML Prague has become the biggest practical conference about Machine Learning in Europe. On March 23-25, 45 speakers from leading AI companies such as Google, Konica Minolta, Adobe or eBay among others, will hold advanced level talks and workshops on Deep Learning and ML applications. ML Prague 2018 will open its doors of the Rudolfinum Music Hall to over 1000 attendees from around the world.

Some of the must-see talks and workshops:

  • Sepp Hochreiter (Johannes University of Linz): The pioneer of Deep Learning and best known for inventing the long short term memory, the best speech and language processing performing techniques. He’ll show you all the applications of Deep Learning that are revolutionizing AI.
  • Amit Srivastava (eBay): He leads the ShopBot and eBay Assistants initiatives. His team develops and integrates algorithms in areas such as computer vision, speech recognition, NLU or conversational dialog management to develop e-commerce chatbot shopping assistants. He will teach us how they leverage Big Data and Machine Learning to enable conversational commerce at eBay.
  • Jacob Biamonte (Skolkovo Institute of Science and Technology): Quantum
    mechanics accelerate and improve certain machine learning tasks in ways that
    classical computers cannot. There are already some promising results, but some
    challenges remain. Jacob, quantum physicist, and quantum computer scientist will show us the steps quantum enhanced machine learning can take to be born
    out in practice.
  • Big Data Science – Elucidation and Practice with Spark Algorithms: Despite Hadoop and Spark being Big Data Frameworks used to carry out common tasks, Spark is reported to work up to 100 times faster in certain circumstances. Learn by getting your hands on “Apache Spark how to scale to very Big Data Science”.
  • Deep Learning for Music classification using Keras: Know how to use the Python Keras framework, on top of Tensorflow, to work on Convolutional Neural Networks plus concepts such as (batch-) normalization, training epochs, activation, loss, and others, for audio and music recognition tasks.
  • Deep Learning for Text Processing: Get hands-on experience with Neural Network models used in NLP to build word and character level models to use them for recognizing entities in texts.