Deep Learning Essentials ★★★ Expert Level
Challenge yourself with our two-day Deep Learning Essentials course and work alongside our top scientists to learn and apply the most effective and sought after machine learning techniques.
Topics supervised Machine Learning
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2 days
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Deep Learning Essentials

About the course

Deep learning is the fastest-growing method in AI, with methodological innovations constantly being developed. In this course, we will deep dive into neural networks and teach you how to build your own using Keras. The second half of the training will equip you with the knowledge to increase model performance and confidently interpret and explain network behavior.  

Why this is for you

Deep learning and neural networks are among the fastest growing and hottest machine learning algorithms. And for a good reason, they can do most anything that classical methods can do, and much more. Their applications range from classification modeling to image recognition and controlling autonomous systems. In this module, we will unravel the mysteries surrounding deep learning, show you how to build your own neural networks and how to utilize them to their full potential.  

For whom

We welcome Data Scientists and anyone who is eager to learn about the fastest-growing and most advanced machine learning techniques. Deep learning is challenging to understand with many technical concepts, therefore, you must have completed our Machine Learning Process (3201) and Classification Using Tree Models (3202) badges to benefit from the content of this course.  

What you’ll learn

  1. General principles and setup of a deep learning algorithm
  2. Convolutional and recurrent neural networks
  3. To build a neural network in Tensorflow and Keras
  4. Interpreting the results of your neural network
Learning Goals
  • Theory behind deep learning – Understand an explain how a neural network works
  • Use cases of deep learning – Know when to apply which neural networks in use cases
  • Build a simple neural network – Skilled at building a neural network using Keras
  • Tuning a neural network – Increase model performance through accuracy and model performance
  • Interpretability of neural networks – Confident at interpreting and explaining the neural networks behavior
Theory and practical use All trainings in the GAIn portfolio combine high-quality standardized training material with theory sessions from experts and hands-on experience where you directly apply the material to real-life cases. Each training is developed by top of the field practitioners which means they are full of industry examples along with practical challenges and know-how, fueling the interactive discussions during training. We believe this multi-level approach creates the ideal learning environment for participants to thrive.