ArtificialIntelligenceArticles
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2. Machine Learning
3. Deep Learning
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some of the smartest people I know are leaving AI research for engineering/neuroscience. Their reasons?

1. We need to understand how humans learn to teach machines to learn.
2. Research should be hypothesis -> experiments, but AI research rn is experiments -> justifying results.
To learn how to design machine learning systems, I find it really helpful to read case studies to see how great teams deal with different deployment requirements and constraints. Here are some of my favorite case studies.

Topics covered: lifetime value, ML project workflow, feature engineering, model selection, prototyping, moving prototypes to production. It's completed with lessons learned and looking ahead!

https://medium.com/airbnb-engineering/using-machine-learning-to-predict-value-of-homes-on-airbnb-9272d3d4739d
Netflix streams to over 117M members worldwide, half of those living outside the US. The company uses machine learning to predict the network quality, detect device anomaly, handle predictive caching. https://netflixtechblog.com/using-machine-learning-to-improve-streaming-quality-at-netflix-9651263ef09f
Most courses only teach you how to train your models. This is only one I've seen that shows you how to design, train, & deploy models. All videos are available. Great resource for those struggling with the ML system design Qs in interviews too. https://fullstackdeeplearning.com/march2019
Monkeys Wake From Anaesthetic When Brain Region Linked to Consciousness Is Stimulated
https://www.cell.com/neuron/fulltext/S0896-6273(20)30005-2
Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling
Grathwohl et al.: https://arxiv.org/abs/2002.05616
#ArtificialIntelligence #DeepLearning #MachineLearning