For Adam Jasinski, a technology director for a school district outside of St Louis, Missouri, monitoring student emails used to be a time-consuming job. Jasinski used to do keyword searches of the official school email accounts for the district's 2,600 students, looking for words like "suicide" or "marijuana". Then he would have to read through every message that included one of the words. The process would occasionally catch some concerning behavior, but "it was cumbersome", Jasinski recalled. Last year Jasinski heard about a new option: following the school shooting in Parkland, Florida, the technology company Bark was offering schools free, automated, 24-hour-a-day surveillance of what students were writing in their school emails, shared documents and chat messages, and sending alerts to school officials any time the monitoring technology flagged concerning phrases.

If you think your data is clean, perhaps you have not looked into it yet; if you think your data is messy, it's even messier. Nobody cares how you did it; just do it correctly. People do not care how much you know until they know how much you care (about them and their business). In 2-3 years, nobody will talk about Big Data anymore. It always pays off to be damn good at numbers, Excel, and PowerPoint (and yes, presentation skills); Tableau is a big plus. Downloading some code and data and running them does not make you a data scientist. The same is true for doing data science courses. Participating in Kaggle competitions does not make you a data scientist, although it can help you learn from others. Winning Kaggle competitions does not necessarily make you a good data scientist. ETL is always needed - be good at it and learn a good tool for it (Talend is a good one). Also, learn scripting languages for ETL. Deep learning is cool, but it's still cool if you don't use it when you don't need it, and in 99% of cases you don't need it. Algorithms are commodities, your data is not. Ideas are commodities, execution is not. Deep learning expertise will soon become a commodity; problem-solving skills won't.

Google search is advancing a reading grade. Google says it has enhanced its search-ranking system with software called BERT, or Bidirectional Encoder Representations from Transformers to its friends. It was developed in the company's artificial intelligence labs and announced last fall, breaking records on reading comprehension questions that researchers use to test AI software. Pandu Nayak, Google's vice president of search, said at a briefing Thursday that the muppet-monickered software has made Google's search algorithm much better at handling long queries, or ones where the relationships between words are crucial. You're now less likely to get frustrating responses to queries dependent on prepositions like for" and "to," or negations such as "not" or "no." "This is the single biggest positive change we've had in the last five years," Nayak said--at least according to Google's measures of how ranking changes help people find what they want.

"Words that will Inspire" is an analysis on 2,500 TED talks using text analytics and machine learning on R to find the factors that make some talks more popular than others. What was the motivation for doing this project? I am part of a meetup group called Data Scientist speakers in London that meets regularly to practice data science talks and receive feedback to improve public speaking. Every year at the club we have a competition to see who can come with the best data science story. I joined this competition and wanted to make participate with something special: I wanted to combine my data science skills to analyse famous speeches or talks and use these insights to build an entirely new one.

Unsupervised learning methods summarize data or transform it such that some desirable properties are enforced. These properties are often easily achieved analytically but are harder to enforce when working in a stochastic optimization (e.g. Before a model is created or a method is defined, some groundwork needs to be laid. What assumptions do we make about the data or the model? How do we know that the model we end up with is good and what do we exactly mean by good?

This course is designed for any graduates as well as Software Professionals who are willing to learn data science in simple and easy steps using R programming, Python Programming, WEKA tool kit and SQL. Data is the new Oil. This statement shows how every modern IT system is driven by capturing, storing and analysing data for various needs. Be it about making decision for business, forecasting weather, studying protein structures in biology or designing a marketing campaign. All of these scenarios involve a multidisciplinary approach of using mathematical models, statistics, graphs, databases and of course the business or scientific logic behind the data analysis.

We introduce a general approach, called test-time training, for improving the performance of predictive models when test and training data come from different distributions. Test-time training turns a single unlabeled test instance into a self-supervised learning problem, on which we update the model parameters before making a prediction on this instance. We show that this simple idea leads to surprising improvements on diverse image classification benchmarks aimed at evaluating robustness to distribution shifts. Theoretical investigations on a convex model reveal helpful intuitions for when we can expect our approach to help.

We present relay policy learning, a method for imitation and reinforcement learning that can solve multi-stage, long-horizon robotic tasks. This general and universally-applicable, two-phase approach consists of an imitation learning stage that produces goal-conditioned hierarchical policies, and a reinforcement learning phase that finetunes these policies for task performance. Our method, while not necessarily perfect at imitation learning, is very amenable to further improvement via environment interaction, allowing it to scale to challenging long-horizon tasks. We simplify the long-horizon policy learning problem by using a novel data-relabeling algorithm for learning goal-conditioned hierarchical policies, where the low-level only acts for a fixed number of steps, regardless of the goal achieved. While we rely on demonstration data to bootstrap policy learning, we do not assume access to demonstrations of every specific tasks that is being solved, and instead leverage unstructured and unsegmented demonstrations of semantically meaningful behaviors that are not only less burdensome to provide, but also can greatly facilitate further improvement using reinforcement learning. We demonstrate the effectiveness of our method on a number of multi-stage, long-horizon manipulation tasks in a challenging kitchen simulation environment. Videos are available at https://relay-policy-learning.github.io/

Graph Attention Networks (GA Ts) are the state-of-the-art neural architecture for representation learning with graphs. GA Ts learn attention functions that assign weights to nodes so that different nodes have different influences in the feature aggregation steps. In practice, however, induced attention functions are prone to over-fitting due to increasing number of parameters and the lack of direct supervision on attention weights. GA Ts also suffer from over-smoothing at the decision boundary of nodes. Here we propose a framework to address their weaknesses via margin-based constraints on attention during training. We first theoretically demonstrate the over-smoothing behavior of GA Ts and then develop an approach using constraint on the attention weights according to the class boundary and feature aggregation pattern. Furthermore, to alleviate the over-fitting problem, we propose additional constraints on graph structure. Extensive experiments and ablation studies on common benchmark datasets demonstrate the effectiveness of our method, which leads to significant improvements over the previous state-of-the-art graph attention methods on all datasets. Introduction Many real world applications involve graph data, like social networks (Zhang and Chen 2018), chemical molecules (Gilmer et al. 2017), and recommender systems (Berg, Kipf, and Welling 2017). The complicated structures of these graphs have inspired new machine learning methods (Cai, Zheng, and Chang 2018; Wu et al. 2019b). Recently much attention and progress has been made on graph neural networks, which have been successfully applied to social network analysis (Battaglia et al. 2016), recommendation systems (Ying et al. 2018), and machine reading comprehension (Tu et al. 2019; De Cao, Aziz, and Titov 2018). Recently, a novel architecture leveraging attention mechanism in Graph Neural Networks (GNNs) called Graph Attention Networks (GA Ts) was introduced (V eli ˇ ckovi c et al. 2017). GA T was motivated by attention mechanism in natural language processing (V aswani et al. 2017; Devlin et al. 2018).

Many machine learning problems can be expressed as the optimization of some cost functional over a parametric family of probability distributions. It is often beneficial to solve such optimization problems using natural gradient methods. These methods are invariant to the parametrization of the family, and thus can yield more effective optimization. Unfortunately, computing the natural gradient is challenging as it requires inverting a high dimensional matrix at each iteration. We propose a general framework to approximate the natural gradient for the Wasserstein metric, by leveraging a dual formulation of the metric restricted to a Reproducing Kernel Hilbert Space. Our approach leads to an estimator for gradient direction that can trade-off accuracy and computational cost, with theoretical guarantees. We verify its accuracy on simple examples, and show the advantage of using such an estimator in classification tasks on Cifar10 and Cifar100 empirically.