We provide to following in-company masterclasses:

  • 1 or 2-days in-company masterclass ‘Introduction Deep Learning’
  • 1 or 2-days in-company masterclass ‘Introduction Deep Learning’ + 1-day AI ideation workshop
  • 5 or 8-days in-company masterclass ‘Fundamentals Deep Learning’

Participants receive a paper handout and will carry out exercises using Jupyter notebooks. Contact us for more information or ordering a masterclass.

The ‘Introduction Deep Learning’ masterclass is currently also provided as ‘open class’ via the High Tech Institute.

Introduction to Deep Learning

Dive into deep learning with this practical masterclass

  • Get an overview of deep learning techniques in one day;
  • Get up-to-speed with deep learning.

This 1-day masterclass brings you up to speed in deep learning, one of the fastest developing fields in artificial intelligence. You will get an overview of the latest deep learning trends and techniques from both lectures and exercises.

The one day masterclass ‘Introduction to Deep Learning’ is intended for software and hardware engineers, application and process engineers, system architects and managers with technical background.

Prerequisites are basic mathematics skills and basic (python) programming skills.

Objective

After this 1-day masterclass the participants will have an understanding of the latest artificial intelligence / deep learning techniques. More specifically, they will:

  • Understand the latest artificial intelligence / deep learning trends;
  • Understand the intuition behind artificial neural networks;
  • Understand the intuition behind convolutional neural networks;
  • Understand the intuition behind recurrent neural networks;
  • Understand the intuition behind reinforcement Learning;
  • Get an overview of deep learning software tools;
  • Carry out deep learning exercises.
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Fundamentals Deep Learning

Become a deep learning expert yourself!

  • Get into the details of deep learning technology;
  • Develop and train deep learning models using real world datasets.

This 5 or 8-days masterclass teaches you all details of deep learning technology. It covers the same topics as the ‘Introduction Deep Learning’ masterclass, but in much, much more depth.

Prerequisites are mathematics skills and python programming skills.

Objective

After this masterclass the participants will be able to develop and train their own deep learning models. More specifically, they will:

  • Understand the latest artificial intelligence / deep learning trends;

  • Understand the mathematics behind convolutional neural networks;

  • Understand the mathematics behind recurrent neural networks;

  • Understand the mathematics behind reinforcement Learning;

  • Prepare datasets, develop your own deep learning models;

  • Participate in Kaggle machine learning competitions and train competing models.

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Deep Reinforcement Learning

Build self-learning algorithms that optimize processes and problems

  • Get an overview of reinforcement learning (RL) techniques in one day;
  • Get familiar the latest Deep RL techniques as used by DeepMind.

This 1-day masterclass brings you up to speed in Deep Reinforcement Learning. Deep Reinforcement Learning is a new AI techniques that learns to optimize processes and problems. You will get an overview of reinforcement learning trends and techniques from both lectures and exercises.

The one day masterclass ‘Reinforcement Learning’ is intended for software and hardware engineers, game developers, robot engineers, application and process engineers, control engineers and system architects.

Prerequisites are mathematics skills and basic (python) programming skills.

Objective

After this 1-day masterclass the participants will have an understanding of reinforcement learning techniques. More specifically, they will:

  • Understand the reinforcement learning framework;
  • Understand solving the RL by dynamic programming;
  • Understand solving the RL by Monte Carlo simulation;
  • Understand solving the RL by Temporal Difference learning;
  • Understand difference between tabular and function approximation approach;
  • Understand Deep Reinforcement Learning, such as DQN, DDQN, BDQN;
  • Understand replay memory techniques;
  • Carry out reinforcement learning exercises, including training an algorithm to play Atari games.
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