Synthetic Biology is the design and construction of new biological parts, devices, and systems, and the re-design of existing, natural biological systems for supposedly useful purposes. It is a field dedicated to understanding and re-engineering the basic building blocks of life, and has its roots in the early 1970s, when key discoveries were made about how to cut and paste short DNA sequences from one organism (everything from bacteria to humans) into another.

In this part of the series, we will introduce Recurrent Neural Networks aka RNNs that made a major breakthrough in predictive analytics for sequential data. This article covers RNNs on both conceptual and practical levels. We will start with the definition of RNNs, why and when they are used, then we will build an RNN ourselves for sentiment analysis. So far we have been working with regular tabular data. This data has no real notion of a sequence.

In my last blog, I discussed k-Nearest Neighbor machine learning algorithms with an example that was hopefully easy to understand for beginners. During the summer of 2017 I began a five-part series on types of machine learning. That series included more details about K-means clustering, Singular Value Decomposition, Principal Component Analysis, Apriori and Frequent Pattern-Growth. Today I want to expand on the ideas presented in my Naive Bayes "Data Science in 90 Seconds" You Tube video and continue the discussion in plain language. If you recall from earlier discussions, unsupervised machine learning is the'task of inferring a function to describe hidden structure from unlabeled data'.

Sramana Mitra: There's a clear trend of these more complex plumbing of the AI systems becoming available as platforms and then abstraction layers being created such that AI can become a lot more ubiquitous. Where would you start a new company today if you wanted to do something in AI? Josh Sutton: Where I would look at and where I get excited about the investment opportunities is not in a technology play directly, but in a business transformation play. If you think about it simply, the suite of AI tools right now enables you to make better decisions faster, engage with customers conversationally without having the human capital cost associated with that model, and the ability to perform certain tasks and functions that require limited human interactions. Andrew Ng came out with a simplified but still great litmus test of if you can do it, then it's probably a good use case. We take those three things as levers of change and then look at industry problems today and how they're being solved.

In machine learning and computational learning theory, Logit Boost is a boosting algorithm formulated by Jerome Friedman, Trevor Hastie, and Robert Tibshirani. The original paper casts the AdaBoost algorithm into a statistical framework. Specifically, if one considers AdaBoost as a generalized additive model and then applies the cost functional of logistic regression, one can derive the LogitBoost algorithm. LogitBoost can be seen as a convex optimization. Bootstrap Aggregation (or Bagging for short), is a simple and very powerful ensemble method.