Data science and machine learning are not just buzz words of the moment but have rather transformed how business is conducted in today's world. Incorporation of technology and digitization of almost every feasible aspect of business provides access to abundance of data. This data can provide insights which offer a scientific basis for decision making. To keep up with constantly changing trends and to have ability to self-correct based of most recent information necessitates the machines with analytical capabilities. Machine learning is a data analysis method used to automate analytical model building algorithms.

In this work, we consider the problem of combining link, content and temporal analysis for community detection and prediction in evolving networks. Such temporal and content-rich networks occur in many real-life settings, such as bibliographic networks and question answering forums. Most of the work in the literature (that uses both content and structure) deals with static snapshots of networks, and they do not reflect the dynamic changes occurring over multiple snapshots. Incorporating dynamic changes in the communities into the analysis can also provide useful insights about the changes in the network such as the migration of authors across communities. In this work, we propose Chimera, a shared factorization model that can simultaneously account for graph links, content, and temporal analysis. This approach works by extracting the latent semantic structure of the network in multidimensional form, but in a way that takes into account the temporal continuity of these embeddings. Such an approach simplifies temporal analysis of the underlying network by using the embedding as a surrogate. A consequence of this simplification is that it is also possible to use this temporal sequence of embeddings to predict future communities. We present experimental results illustrating the effectiveness of the approach.

This paper introduces Jensen, an easily extensible and scalable toolkit for production-level machine learning and convex optimization. Jensen implements a framework of convex (or loss) functions, convex optimization algorithms (including Gradient Descent, L-BFGS, Stochastic Gradient Descent, Conjugate Gradient, etc.), and a family of machine learning classifiers and regressors (Logistic Regression, SVMs, Least Square Regression, etc.). This framework makes it possible to deploy and train models with a few lines of code, and also extend and build upon this by integrating new loss functions and optimization algorithms.

Accurately predicting the future health of batteries is necessaryElectrochemical batteries, such as lithium-ion and leadacid to ensure reliable operation, minimise maintenance cells, experience degradation over time and during costs, and calculate the value of energy storage investments.usage, leading to decreased energy storage capacity and The complex nature of degradation renders datadrivenincreased internal resistance. Being able to predict the approaches a promising alternative to mechanistic rate of degradation and the remaining useful life (RUL) modelling. This study predicts the changes in batteryof a battery is important for performance and economic capacity over time using a Bayesian nonparametric reasons. For example, in an electric vehicle, the driveable approach based on Gaussian process regression. These range is directly related to the battery capacity. For energy changes can be integrated against an arbitrary input sequence storage asset valuation, depreciation, warranty, insurance to predict capacity fade in a variety of usage scenarios, and preventative maintenance purposes, predicting forming a generalised health model. The approach RUL at design stage and during operation is crucial, and naturally incorporates varying current, voltage and temperaturethe investment case is strongly dependent on the degradation inputs, crucial for enabling real world application.

The user uses the control panel (A) to specify the detail information to visualize (e.g., level of detail, rule filters). The rule-based explanatory representation is visualized as a matrix (B), where each row represents a rule, and each column is a feature used in the rules. The user can also filter the data or use a customized input in the data filter (C) and navigate the filtered dataset in the data table (D). Abstract--With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have been developed to help model developers understand, diagnose, and refine machine learning models. However, a large number of potential but neglected users are the domain experts with little knowledge of machine learning but are expected to work with machine learning systems. In this paper, we present an interactive visualization technique to help users with little ...

Predictive geometric models deliver excellent results for many Machine Learning use cases. Despite their undoubted performance, neural predictive algorithms can show unexpected degrees of instability and variance, particularly when applied to large datasets. We present an approach to measure changes in geometric models with respect to both output consistency and topological stability. Considering the example of a recommender system using word2vec, we analyze the influence of single data points, approximation methods and parameter settings. Our findings can help to stabilize models where needed and to detect differences in informational value of data points on a large scale.

When approaching any type of Machine Learning (ML) problem there are many different algorithms to choose from. In machine learning, there's something called the "No Free Lunch" theorem which basically states that no one ML algorithm is best for all problems. The performance of different ML algorithms strongly depends on the size and structure of your data. Thus, the correct choice of algorithm often remains unclear unless we test out our algorithms directly through plain old trial and error. But, there are some pros and cons to each ML algorithm that we can use as guidance.

Pulled from the web, here is a our collection of the best, free books on Data Science, Big Data, Data Mining, Machine Learning, Python, R, SQL, NoSQL and more. Let's use the Reddit API to grab news headlines and perform Sentiment Analysis Expanding on the previous article, we'll be looking at how to incorporate recent price behaviors into our strategy In this post, we'll walk through building linear regression models to predict housing prices resulting from economic activity.

Your computer is continuously executing programs, but does it really understand them? Not in any meaningful sense. That burden falls upon human knowledge workers, who are increasingly asked to write and understand code. They would benefit greatly from intelligent tools that reveal the connections between their code and its subject matter. Towards this prospect, we develop an AI system that forms semantic representations of computer programs, using techniques from knowledge representation and program analysis. We focus on code written for data science, although our method is more generally applicable. The semantic representations are created through a novel algorithm for the semantic enrichment of dataflow graphs. This algorithm is undergirded by a new ontology language for modeling computer programs and a new ontology about data science, written in this language.

In the last several years, Twitter is being adopted by the companies as an alternative platform to interact with the customers to address their concerns. With the abundance of such unconventional conversation resources, push for developing effective virtual agents is more than ever. To address this challenge, a better understanding of such customer service conversations is required. Lately, there have been several works proposing a novel taxonomy for fine-grained dialogue acts as well as develop algorithms for automatic detection of these acts. The outcomes of these works are providing stepping stones for the ultimate goal of building efficient and effective virtual agents. But none of these works consider handling the notion of negation into the proposed algorithms. In this work, we developed an SVM-based dialogue acts prediction algorithm for Twitter customer service conversations where negation handling is an integral part of the end-to-end solution. For negation handling, we propose several efficient heuristics as well as adopt recent state-of- art third party machine learning based solutions. Empirically we show model's performance gain while handling negation compared to when we don't. Our experiments show that for the informal text such as tweets, the heuristic-based approach is more effective.