Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks. We provide a simple yet rigorous explanation for this behaviour by introducing the concept of an optimal representation space, in which semantically close symbols are mapped to representations that are close under a similarity measure induced by the model's objective function. In addition, we present a straightforward procedure that, without any retraining or architectural modifications, allows deep recurrent models to perform equally well (and sometimes better) when compared to shallow models. To validate our analysis, we conduct a set of consistent empirical evaluations and introduce several new sentence embedding models in the process. Even though this work is presented within the context of natural language processing, the insights are readily applicable to other domains that rely on distributed representations for transfer tasks.

I started my way in the Data Science world a few years back. I was a Software Engineer back then and I started to learn online first (before starting my Master's degree). I remember that as I searched for online resources I saw only names of learning algorithms -- Linear Regression, Support Vector Machine, Decision Tree, Random Forest, Neural Networks and so on. It was very hard to understand where I should start. Today I know that the most important thing to learn to become a Data Scientist is the pipeline, i.e, the process of getting and processing data, understanding the data, building the model, evaluating the results (both of the model and the data processing phase) and deployment.

At their core, data scientists have a math and statistics background. Out of this math background, they're creating advanced analytics. Just like their software engineering counterparts, data scientists will have to interact with the business side. This includes understanding the domain enough to make insights. Data scientists are often tasked with analyzing data to help the business, and this requires a level of business acumen. Finally, their results need to be given to the business in an understandable fashion. This requires the ability to verbally and visually communicate complex results and observations in a way that the business can understand and act on them. Thus, it'll be extremely valuable for any aspiring data scientists to learn data mining -- the process where one structures the raw data and formulate or recognize the various patterns in the data through the mathematical and computational algorithms. This helps generate new information and unlock various insights. Here is a simple list of reasons on why you should study Data Mining?

This course is for students who wish to expend their knowledge about linear regression, beyond the technical details. This course is not for students without any background. Linear regression is not sexy. But you should know, that linear regression is the most important machine learning model. In practice, nearly every data science team in almost every company is using some form of linear regression to solve their problems.

In this post, we will use the LinearRegressor estimator from Tensorflow. It is the Behavior Risk Factor Surveillance System (BRFSS). There is a Jupyter Notebook hosted on github with the code and data needed to reproduce this work. The data is extracted into training, evaluation, and prediction sets from the 2016 BRFSS. There are 39,034 training records, 9,633 evaluation records, and 12,211 records held out for prediction.

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced investor. Learning machine trading analysis is indispensable for finance careers in areas such as computational finance research, computational finance development, and computational finance trading mainly within investment banks and hedge funds. It is also essential for academic careers in computational finance. And it is necessary for experienced investors computational finance trading research and development. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500 Index ETF prices historical data for back-testing to achieve greater effectiveness.

The Softmax function is ubiquitous in machine learning, multiple previous works suggested faster alternatives for it. In this paper we propose a way to compute classical Softmax with fewer memory accesses and hypothesize that this reduction in memory accesses should improve Softmax performance on actual hardware. The benchmarks confirm this hypothesis: Softmax accelerates by up to 1.3x and Softmax+TopK combined by up to 5x.

Motivation: Post-database searching is a key procedure in peptide dentification with tandem mass spectrometry (MS/MS) strategies for refining peptide-spectrum matches (PSMs) generated by database search engines. Although many statistical and machine learning-based methods have been developed to improve the accuracy of peptide identification, the challenge remains on large-scale datasets and datasets with an extremely large proportion of false positives (hard datasets). A more efficient learning strategy is required for improving the performance of peptide identification on challenging datasets. Results: In this work, we present an online learning method to conquer the challenges remained for exiting peptide identification algorithms. We propose a cost-sensitive learning model by using different loss functions for decoy and target PSMs respectively. A larger penalty for wrongly selecting decoy PSMs than that for target PSMs, and thus the new model can reduce its false discovery rate on hard datasets. Also, we design an online learning algorithm, OLCS-Ranker, to solve the proposed learning model. Rather than taking all training data samples all at once, OLCS-Ranker iteratively feeds in only one training sample into the learning model at each round. As a result, the memory requirement is significantly reduced for large-scale problems. Experimental studies show that OLCS-Ranker outperforms benchmark methods, such as CRanker and Batch-CS-Ranker, in terms of accuracy and stability. Furthermore, OLCS-Ranker is 15--85 times faster than CRanker method on large datasets. Availability and implementation: OLCS-Ranker software is available at no charge for non-commercial use at https://github.com/Isaac-QiXing/CRanker.

Accounting fraud is a global concern representing a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Several tricks can be used to commit accounting fraud, hence the need for non-static regulatory interventions that take into account different fraudulent patterns. Accordingly, this study aims to improve the detection of accounting fraud via the implementation of several machine learning methods to better differentiate between fraud and non-fraud companies, and to further assist the task of examination within the riskier firms by evaluating relevant financial indicators. Out-of-sample results suggest there is a great potential in detecting falsified financial statements through statistical modelling and analysis of publicly available accounting information. The proposed methodology can be of assistance to public auditors and regulatory agencies as it facilitates auditing processes, and supports more targeted and effective examinations of accounting reports.

The Statistical Learning Theory (SLT) provides the theoretical guarantees for supervised machine learning based on the Empirical Risk Minimization Principle (ERMP). Such principle defines an upper bound to ensure the uniform convergence of the empirical risk Remp(f), i.e., the error measured on a given data sample, to the expected value of risk R(f) (a.k.a. actual risk), which depends on the Joint Probability Distribution P(X x Y) mapping input examples x in X to class labels y in Y. The uniform convergence is only ensured when the Shattering coefficient N(F,2n) has a polynomial growing behavior. This paper proves the Shattering coefficient for any Hilbert space H containing the input space X and discusses its effects in terms of learning guarantees for supervised machine algorithms.