How to apply deep learning to real world problems


The rise of artificial intelligence in recent years is grounded in the success of deep learning. Three major drivers caused the breakthrough of (deep) neural networks: the availability of huge amounts of training data, powerful computational infrastructure, and advances in academia. Thereby deep learning systems start to outperform not only classical methods, but also human benchmarks in various tasks like image classification or face recognition. This creates the potential for many disruptive new businesses leveraging deep learning to solve real-world problems.

At Berlin-based Merantix, we work on these new business cases in various industries (currently automotive, health, financial and advertising).

It easier than ever before to train a neural network. However, it is rarely the case that you can just take code from a tutorial and directly make it work for your application. Interestingly, many of the most important tweaks are barely discussed in the academic literature but at the same time critical to make your product work.

Therefore I thought it would be helpful for other people who plan to use deep learning in their business to understand some of these tweaks and tricks. In this blog post I want to share three key learnings, which helped us at Merantix when applying deep learning to real-world problems:

  • Learning I: The Value Of Pre-Training
  • Learning II: Caveats Of Real-World Label Distributions
  • Learning III: Understanding Black Box Models


In the academic world of machine learning, there is little focus on obtaining datasets. Instead, it is even the opposite: in order to compare deep learning techniques with other approaches and ensure that one method outperforms others, the standard procedure is to measure the performance on a standard dataset with the same evaluation procedure. However, in real-world scenarios, it is less about showing that your new algorithm squeezes out an extra 1% in performance compared to another method. Instead it is about building a robust system which solves the required task with sufficient accuracy. As for all machine learning systems, this requires labeled training from which the algorithm can learn from.

  • By Rasmus Rothe, founder of AI research lab Merantix, Berlin

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