# Lukas Biewald (Weights & Biases)

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Deep Learning In The Wild
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* Machine Learning can be unpredictable and opaque.
* Deep Learning can be vulnerable to hacking.
* Machine Learning requires tons of clean training data.
* Deep Learning and GPUs break a lot of assumptions.
* Machine Learning can look at far more data than humans.
* The combination of humans and computers is powerful.
* **What's coming?**
  * Better tools and platforms.
  * More medical applications.
  * New solutions to training data.
* How to (**successfully**) ship deep learning projects:
  * Pay a lot of attention to your training data.
  * Get something working end-to-end right away, then improve one thing at a time.
  * Look for graceful ways to handle the inevitable cases where the algorithm fails.

Mentioned Resources:

* [The State of Machine Intelligence](http://www.shivonzilis.com/machineintelligence) (curated by [Shivon Zillis](https://twitter.com/shivon))
* Read Lukas's article: "[Why are Machine Learning Projects so Hard to Manage?](https://medium.com/@l2k/why-are-machine-learning-projects-so-hard-to-manage-8e9b9cf49641)"
* [Datasets over Algorithms](http://www.spacemachine.net/views/2016/3/datasets-over-algorithms) (credit to [Alex Wissner-Gross](https://www.alexwg.org/))


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