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Advanced Artificial Intelligence Training

Advanced A.I. Training

Due to the COVID-19 our training courses will be taught via an online classroom.

Receive in-depth knowledge from industry professionals, test your skills with hands-on assignments & demos, and get access to valuable resources and tools.

This training offers a deep dive into Artificial Intelligence from a practical point of view. The lessons presented here focus on general neural networks, deep learning, convolutional neural networks (CNN) and recurrent neural networks (RNN). After this course, you will be able to create your own CNNs to recognize hand-written digits using noise images, your own RNNs to create a Shakespeare poem generator, and predict stock prices. You will also understand and be able to apply the advanced A.I. principles and algorithms included in the training. This course is ideal for data scientists who want to make a step forward into A.I. Strong experience with python and general machine learning knowledge are required to understand the training.

Are you interested? Contact us and we will get in touch with you.

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Academy: Advanced AI
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About the training & classes

The Advanced A.I. training is split in 3 days. Click below to see a detailed description of each class: 

 
Neural Networks

In the first class of the Advanced A.I. training, we will teach you how Neural Networks work and how to apply them in your projects. We will also give an overview of common problems you could encounter when implementing a Neural Network and how to solve them.

Furthermore, we will explain how the basic elements of a Neural Network work and how you can combine them to create complex structures. Then, we explain how you can train a Neural Network, what kind of hyper-parameters you can optimize and how you can train your Neural Network even faster.

After this theoretical overview you will gain hands-on experience in a lab session, where we are going to create our own Neural Network and experiment with different ways of training and optimizing it.

This training includes theory, demos and hands-on exercises.

After this training you will have gained knowledge about:

  • The basic building blocks of a neural-network
  • How to train a Neural Network
  • Which hyper-parameters to optimize and how to optimize them
  • Practical considerations
  • Lab session to get hands-on experience in building and optimizing your own Neural Network
Deep Learning: Convolutional Neural networks (CNN)

In the second class of the training, we will teach you about Deep Learning and give you a better understanding and overview about the different Deep Learning architectures with a focus on Convolutional Neural Networks (CNN).

The lesson starts with a quick recap of Neural Networks and moves on to investigate the different problems that may occur when adding more and more hidden-layers. Then, we introduce some different types of hidden-layers, such as convolutional layers and pooling layers and talk about the benefits of these hidden-layers and when/how you should apply them. After that, we move on to the different Deep Learning architectures, like residual networks and inception modules.

We will also have a look at how we can use very large pre-trained Deep Learning models and customize them so they can be used in your projects with less training data. Lastly, we will show you how Deep Learning algorithms can be used to detect objects or recognize faces.

The training includes theory, demos and hands-on exercises.

After this training you will have gained knowledge about:

  • The problems that may occur when you apply a deep Neural Network and how you can solve them
  • Different types of hidden layers, like convolutional layers
  • Different deep learning architectures, like residual networks
  • How to use large pre-trained Deep Learning models for you own tasks with less data
  • How Deep Learning models work that are able to recognize objects and faces
  • Lab session to get hands-on experience in building your own Deep Learning models to recognize handwritten digits and reduce noise in images
Deep Learning: Recurrent Neural Networks (RNN)

In the final class of the training, we will teach you everything you need to know about Recurrent Neural Networks (RNN) and when/how you can apply them in your projects.

The class starts with a discussion about the benefits of RNNs and some use cases where they can be applied. Then, we explain the basic components of an RNN and how you can use these components to create different types of RNNs. We will also discuss a common RNN long-term problem and how LSTMs and GRUs are designed to solve it. Then, we will talk about bi-directional RNNs and for which use cases they can be used. Last, we will show you how RNNs can be stacked to create Deep Networks of RNN layers.

After this theoretical overview we gain hands-on experience in a lab session, where we are going to make our own RNNs to create a Shakespeare poem generator and to predict stock prices.

This training includes theory, demos and hands-on exercise.

After this training you will have gained knowledge about:

  • The basic building blocks of a RNN
  • How LSTMs work
  • How GRUs work
  • Bi-directional RNNs
  • How you can stack RNN layers to create deep networks
  • Lab session to get hands-on experience in building and optimizing your own RNN's