Deep learning has made AI accessible to everyone. Complex algorithms have been replaced by neural networks that are trained end-to-end. Open sourced code allows easy reproduction of experiments.  In practice, starting a career in AI is much easier than a decade ago, and requires less background knowledge and resources.

On the other hand, to work effectively, it is not enough to understand deep learning theory. What's required is practical knowledge of everything around deep learning. To put a model into production, you will need a deep understanding of the data and annotation pipeline, where biases could come into play, how to think about error rates of individual systems and how they stack together into a final error rate for the whole system.

Learning all of those details takes practice, a lot of trial and error, and most of all: time. With the resources shared here, you can cut corners and gain the required knowledge in a fraction of the time!

Working in deep learning is absolutely amazing! I want to share what has helped me so you can step up your AI game and have as much fun as I do in this industry, and as a side effect, also step up in your career!

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