Machine learning and artificial intelligence have had a high impact on the evolving future of technology as well as human lives. Numerous innovations in the past few years were witnessed in the field of technology. The most important part of machine learning is to deal with real-world problems. In this article, we list 10 most useful machine learning projects for 2019 that will help you in tasks related to real-world problems. These projects equip ML enthusiasts with codes and datasets to learn and apply to real-life issues.

Let's go together down the loss landscape of Deep Learning. These are exciting times for those passionate about the mysteries and possibilities of deep learning. They include people like Jeremy Howard at fast.ai, One of the key things that many of them recommend is to get hands on as soon as possible, coding the key principles of deep learning on your own. Nowadays, we have amazing libraries at our disposal.

Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You'll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project.

Yoshua Bengio, a computer scientist who's widely considered one of the three "godfathers" of deep learning, opened up in a new interview with Bloomberg about his anxiety about how China is using the technology he helped develop to surveil and manipulate large populations. "This is the 1984 Big Brother scenario," he told the business magazine, referring to George Orwell's classic dystopic novel about universal government surveillance. "I think it's becoming more and more scary." Bengio, who's a professor at the University of Montreal and the co-founder of deep learning company Element AI, is particularly troubled by China's use of facial recognition technology. Though American tech companies including Amazon also offer facial recognition software, Benio argued that the tech should be "highly regulated" because of its capacity for abuse, according to Bloomberg.

If you are like me, there is a good chance that you are confused as well about the most recent terminology to use in the field of data science … pardon, artificial intelligence … no, I mean data science. No, I mean artificial intelligence. Please, somebody tell me what I should call it and what the difference is! Isn't artificial intelligence just a new cool name to label the old traditional data science? Don't both concepts cover the same algorithms? This is what I used to think until I took a pause to write this post.

DeepMind specializes in "deep learning," a type of artificial intelligence that is rapidly changing drug discovery science. A growing number of companies are applying similar methods to other parts of the long, enormously complex process that produces new medicines. These A.I. techniques can speed up many aspects of drug discovery and, in some cases, perform tasks typically handled by scientists. "It is not that machines are going to replace chemists," said Derek Lowe, a longtime drug discovery researcher and the author of In the Pipeline, a widely read blog dedicated to drug discovery. "It's that the chemists who use machines will replace those that don't."

New work from MIT researchers peers under the hood of an automated fake-news detection system, revealing how machine-learning models catch subtle but consistent differences in the language of factual and false stories. The research also underscores how fake-news detectors should undergo more rigorous testing to be effective for real-world applications. Popularized as a concept in the United States during the 2016 presidential election, fake news is a form of propaganda created to mislead readers, in order to generate views on websites or steer public opinion. Almost as quickly as the issue became mainstream, researchers began developing automated fake news detectors -- so-called neural networks that "learn" from scores of data to recognize linguistic cues indicative of false articles. Given new articles to assess, these networks can, with fairly high accuracy, separate fact from fiction, in controlled settings.

Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To augment the compound design process we introduce Mol-CycleGAN - a CycleGAN-based model that generates optimized compounds with high structural similarity to the original ones. Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property. We evaluate the performance of the model on selected optimization objectives related to structural properties (presence of halogen groups, number of aromatic rings) and to a physicochemical property (penalized logP). In the task of optimization of penalized logP of drug-like molecules our model significantly outperforms previous results.

Ancillaries have become a major source of revenue and profitability in the travel industry. Yet, conventional pricing strategies are based on business rules that are poorly optimized and do not respond to changing market conditions. This paper describes the dynamic pricing model developed by Deepair solutions, an AI technology provider for travel suppliers. We present a pricing model that provides dynamic pricing recommendations specific to each customer interaction and optimizes expected revenue per customer. The unique nature of personalized pricing provides the opportunity to search over the market space to find the optimal price-point of each ancillary for each customer, without violating customer privacy. In this paper, we present and compare three approaches for dynamic pricing of ancillaries, with increasing levels of sophistication: (1) a two-stage forecasting and optimization model using a logistic mapping function; (2) a two-stage model that uses a deep neural network for forecasting, coupled with a revenue maximization technique using discrete exhaustive search; (3) a single-stage end-to-end deep neural network that recommends the optimal price. We describe the performance of these models based on both offline and online evaluations. We also measure the real-world business impact of these approaches by deploying them in an A/B test on an airline's internet booking website. We show that traditional machine learning techniques outperform human rule-based approaches in an online setting by improving conversion by 36% and revenue per offer by 10%. We also provide results for our offline experiments which show that deep learning algorithms outperform traditional machine learning techniques for this problem. Our end-to-end deep learning model is currently being deployed by the airline in their booking system.

We first, introduce a deep learning based framework named as DeepIrisNet2 for visible spectrum and NIR Iris representation. The framework can work without classical iris normalization step or very accurate iris segmentation; allowing to work under non-ideal situation. The framework contains spatial transformer layers to handle deformation and supervision branches after certain intermediate layers to mitigate overfitting. In addition, we present a dual CNN iris segmentation pipeline comprising of a iris/pupil bounding boxes detection network and a semantic pixel-wise segmentation network. Furthermore, to get compact templates, we present a strategy to generate binary iris codes using DeepIrisNet2. Since, no ground truth dataset are available for CNN training for iris segmentation, We build large scale hand labeled datasets and make them public; i) iris, pupil bounding boxes, ii) labeled iris texture. The networks are evaluated on challenging ND-IRIS-0405, UBIRIS.v2, MICHE-I, and CASIA v4 Interval datasets. Proposed approach significantly improves the state-of-the-art and achieve outstanding performance surpassing all previous methods.