There's an awful lot of text data available today, and enormous amounts of it are being created on a daily basis, ranging from structured to semi-structured to fully unstructured. What can we do with it? Well, quite a bit, actually; it depends on what your objectives are, but there are 2 intricately related yet differentiated umbrellas of tasks which can be exploited in order to leverage the availability of all of this data. NLP is a major aspect of computational linguistics, and also falls within the realms of computer science and artificial intelligence. Text mining exists in a similar realm as NLP, in that it is concerned with identifying interesting, non-trivial patterns in textual data.

I am new to the image analytics and processing. Is there any road map for learning this? Is it right to start with computer vision or should i need to know something even before starting with CV? Please suggest.

At a recent conference in 2017, Microsoft CEO Satya Nadella used the analogy of a corn maze to explain the difference in approach between a classical computer and a quantum computer. In trying to find a path through the maze, a classical computer would start down a path, hit an obstruction, backtrack; start again, hit another obstruction, backtrack again until it ran out of options. Although an answer can be found, this approach could be a very time-consuming. They take every path in the corn maze simultaneously." Thus, leading to an exponential reduction in the number of steps required to solve a problem.

In today's complicated financial landscape accurate models are a necessity for banks to remain competitive, but developing accurate models is challenging. Models are inherently complex -- and if developed poorly can do more harm than good. Minimizing Model Risk with Automated Machine Learning will demonstrate how banks can use Automated Machine Learning to gain a competitive advantage, while quickly aligning their business operation to regulatory requirements. We'll provide an overview of current trends and expectations for model risk management regulatory compliance, and how industry leading financial institutions are leveraging Automated Machine Learning to provide a much stronger framework for model development and validation than traditional manual efforts.

In this series, I will talk about training a simple neural network on image data. To give a brief overview, neural networks is a kind of supervised learning. By this I mean, the model needs to train on historical data to understand the relationship between input variables and target variables. Once trained, the model can be used to predict target variable on new input data. In the previous posts, we have written about linear, lasso and ridge regression.

Artificial Intelligence and Automation are being used in multiple industries to make the lives of people better and to make things more efficient for organizations. AI is being used to diagnose medical conditions or offer legal advice. Automation is being used in factories to reduce reliance on manual labor and to improve the quality of products. As AI and Automation are becoming more universally applicable concepts and can be implemented irrespective of the industry. These innovative technologies depend on an organization's critical data that today is threatened by various factors.

In recent years, we have become increasingly good at training deep neural networks to learn a very accurate mapping from inputs to outputs, whether they are images, sentences, label predictions, etc. from large amounts of labeled data. What our models still frightfully lack is the ability to generalize to conditions that are different from the ones encountered during training. Every time you apply your model not to a carefully constructed dataset but to the real world. The real world is messy and contains an infinite number of novel scenarios, many of which your model has not encountered during training and for which it is in turn ill-prepared to make predictions. The ability to transfer knowledge to new conditions is generally known as transfer learning and is what we will discuss in the rest of this post. Over the course of this blog post, I will first contrast transfer learning with machine learning's most pervasive and successful paradigm, supervised learning. I will then outline reasons why transfer learning warrants our attention. Subsequently, I will give a more technical definition and detail different transfer learning scenarios. I will then provide examples of applications of transfer learning before delving into practical methods that can be used to transfer knowledge.

The artificial intelligence market is fueled by the potential to use data to change the world. While many organizations have already successfully adapted to this paradigm, applying machine learning to new problems is still challenging. The single biggest technical hurdle that machine learning algorithms must overcome is their need for processed data in order to work -- they can only make predictions from numeric data. This data is composed of relevant variables, known as "features." If the calculated features don't clearly expose the predictive signals, no amount of tuning can take a model to the next level.

While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient computational methods for condensing and simplifying data are thus becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind, and the challenges of, graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.

Variational approaches to approximate Bayesian inference provide very efficient means of performing parameter estimation and model selection. Among these, so-called variational-Laplace or VL schemes rely on Gaussian approximations to posterior densities on model parameters. In this note, we review the main variants of VL approaches, that follow from considering nonlinear models of continuous and/or categorical data. En passant, we also derive a few novel theoretical results that complete the portfolio of existing analyses of variational Bayesian approaches, including investigations of their asymptotic convergence. We also suggest practical ways of extending existing VL approaches to hierarchical generative models that include (e.g., precision) hyperparameters.