Advancements in genomic research such as high-throughput sequencing techniques have driven modern genomic studies into "big data" disciplines. This data explosion is constantly challenging conventional methods used in genomics. In parallel with the urgent demand for robust algorithms, deep learning has succeeded in a variety of fields such as vision, speech, and text processing. Yet genomics entails unique challenges to deep learning since we are expecting from deep learning a superhuman intelligence that explores beyond our knowledge to interpret the genome. A powerful deep learning model should rely on insightful utilization of task-specific knowledge. In this paper, we briefly discuss the strengths of different deep learning models from a genomic perspective so as to fit each particular task with a proper deep architecture, and remark on practical considerations of developing modern deep learning architectures for genomics. We also provide a concise review of deep learning applications in various aspects of genomic research, as well as pointing out potential opportunities and obstacles for future genomics applications.

In this post, you will discover the top 10 books available right now on Artificial Intelligence. There are quite a few available online in which you may purchase. Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence.

Editor's note: Originally posted as part of Grakn's Advent at Grakn Labs series. In this era, one can safely say that creating your own chat bot is no longer reserved only for crazy engineers but even non-technical people can create their own. Chat bots have received a lot of hype in 2016 with large news networks like CNET lauding it as the best thing since sliced bread. So did 2016 actually end up being the year of the bot? One of the pioneers of the rise of the bots is Chinese mobile messaging platform WeChat who has provided a bot platform since 2013.

In order to explain the differences between alternative approaches to estimating the parameters of a model, let's take a look at a concrete example: Ordinary Least Squares (OLS) Linear Regression. In Ordinary Least Squares (OLS) Linear Regression, our goal is to find the line (or hyperplane) that minimizes the vertical offsets. Or, in other words, we define the best-fitting line as the line that minimizes the sum of squared errors (SSE) or mean squared error (MSE) between our target variable (y) and our predicted output over all samples i in our dataset of size n. The closed-form solution may (should) be preferred for "smaller" datasets -- if computing (a "costly") matrix inverse is not a concern. For very large datasets, or datasets where the inverse of XTX may not exist (the matrix is non-invertible or singular, e.g., in case of perfect multicollinearity), the GD or SGD approaches are to be preferred.