CatBoost is based on gradient boosting. A new machine learning technique developed by Yandex that outperforms many existing boosting algorithms like XGBoost, Light GBM. While deep learning algorithms requires lots of data and computational power, boosting algorithms are still in need for most of the business problems. However boosting algorithms like XGBoost takes hours to train and sometimes you'll get frustrated while tuning hyper-parameters. On the other hand, CatBoost is easy to implement and very powerful.

So I wanted to create a seamless tutorial for taking OpenStreetMap (OSM) vector data and converting it for use with machine learning (ML) models. In particular, I am really interested in creating a tight, clean pipeline for disaster relief applications, where we can use something like crowd sourced building polygons from OSM to train a supervised object detector to discover buildings in an unmapped location. The recipe for building a basic deep learning object detector is to have two components: (1) training data (raster image vector label pairs) and (2) model framework. The deep learning model itself will be a Single Shot Detector (SSD) object detector. We will use OSM polygons as the basis of our label data and Digital Globe imagery for the raster data. We won't go into the details of an SSD here, as there are plenty sources available.

In the last decade, artificial intelligence ("AI") applications have exploded across various research sectors, including computer vision, communications and medicine. Now, the rapidly developing technology is making its mark in weather prediction. The fields of atmospheric science and satellite meteorology are ideally suited for the task, offering a rich training ground capable of feeding an AI system's endless appetite for data. Anthony Wimmers is a scientist with the University of Wisconsin–Madison Cooperative Institute for Meteorological Satellite Studies (CIMSS) who has been working with AI systems for the last three years. His latest research investigates how an AI model can help improve short-term forecasting (or "nowcasting") of hurricanes.

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The development of "intelligent" systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to "intelligent" machines. Forsensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored.

In the hands of enthusiastic marketing departments, the terms "artificial intelligence," "machine learning," and "deep learning" have become fuzzy in definition, sacrificing clarity to the need for increasing sales. It's entirely possible that you'll run into a product or service that carries one (or several) of these labels while carrying few of its attributes. Talk of machine intelligence can often lead to its own special rabbit-hole of jargon and specialized concepts. Which of these will form an important part of your future security infrastructure -- and does the difference really matter? Three Branches Broadly speaking, machine "intelligence" is a system that takes in data, produces results, and gets better -- faster, more accurate, or both -- as more data is encountered.

Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. Traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the- art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation. Optimized storage has a unique opportunity to become much more than a siloed repository for the deluge of data constantly generated in today's hyper-connected world, but rather a platform that shares and delivers data to create competitive business value. Optimized storage is designed for the needs of a broad range of problem domains including FinTech, life sciences, design, HPC, government, smart cities, media, energy, and many more.

Robots are usually associated with automation, science, and engineering. But can robots have other talents? If you play a melody to a robot, would it be able to comprehend it and come up with a musical response? Can it learn music, compose its own musical, compete with humans, and even surpass them? Could robots be the next Jonas Brothers, Imagine Dragons, or Mozart?

Multilingual end-to-end (E2E) models have shown great promise in expansion of automatic speech recognition (ASR) coverage of the world's languages. They have shown improvement over monolingual systems, and have simplified training and serving by eliminating language-specific acoustic, pronunciation, and language models. This work presents an E2E multilingual system which is equipped to operate in low-latency interactive applications, as well as handle a key challenge of real world data: the imbalance in training data across languages. Using nine Indic languages, we compare a variety of techniques, and find that a combination of conditioning on a language vector and training language-specific adapter layers produces the best model. The resulting E2E multilingual model achieves a lower word error rate (WER) than both monolingual E2E models (eight of nine languages) and monolingual conventional systems (all nine languages). Index T erms: speech recognition, multilingual, RNN-T, residual adapter 1. Introduction Automatic speech recognition (ASR) systems that can transcribe speech in multiple languages, known as multilingual models, have gained popularity as an effective way to expand ASR coverage of the world's languages. Through shared learning of model elements across languages, they have been shown to outperform monolingual systems, particularly for those languages with less data.

Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structure of proteins and other macromolecular complexes at near-atomic resolution. In single particle cryo-EM, the central problem is to reconstruct the three-dimensional structure of a macromolecule from $10^{4-7}$ noisy and randomly oriented two-dimensional projections. However, the imaged protein complexes may exhibit structural variability, which complicates reconstruction and is typically addressed using discrete clustering approaches that fail to capture the full range of protein dynamics. Here, we introduce a novel method for cryo-EM reconstruction that extends naturally to modeling continuous generative factors of structural heterogeneity. This method encodes structures in Fourier space using coordinate-based deep neural networks, and trains these networks from unlabeled 2D cryo-EM images by combining exact inference over image orientation with variational inference for structural heterogeneity. We demonstrate that the proposed method, termed cryoDRGN, can perform ab initio reconstruction of 3D protein complexes from simulated and real 2D cryo-EM image data. To our knowledge, cryoDRGN is the first neural network-based approach for cryo-EM reconstruction and the first end-to-end method for directly reconstructing continuous ensembles of protein structures from cryo-EM images.