One area that Deep Learning has not explored extensively is the uncertainty in estimates. Most Deep Learning frameworks currently focus on giving a best estimate as defined by a loss function. Occasionally something beyond a point estimate is required to make a decision. This is where a distribution would be useful. Bayesian statistics lends itself to this problem really well since a distribution over the dataset is inferred.

DeepSchool.io is an open-source, community based project to teach the A-Z of Deep Learning (DL). All lessons are interactive and (hopefully) funny, occasionally taking jabs at Mr. Trump (check out this notebook on Trump Tweets). This project came out of a weekly class that I did at Arbor Networks where I work as a Data Scientist. Personally I come from a background where I did a PhD in Machine Learning. However, with the development of tools such as Keras, DL has become a lot more accessible to the general community.

DeepSchool.io is an open-source, community based project to teach the A-Z of Deep Learning (DL). All lessons are interactive and (hopefully) funny, occasionally taking jabs at Mr. Trump (check out this notebook on Trump Tweets). This project came out of a weekly class that I did at Arbor Networks where I work as a Data Scientist. Personally I come from a background where I did a PhD in Machine Learning. However, with the development of tools such as Keras, DL has become a lot more accessible to the general community.