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Karkidi on LinkedIn: Dream of becoming a MAANG Engineer

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Apply now at Tiffany & Co. is hiring for an Internship, Data Science Job Required: - Strong statistical knowledge - Excellent communication skills - Completed or pursuing a degree in data science, business analytics or another similar field - Self-driven/autonomous Preferred: - Experience with different Machine Learning methods (relevant coursework is acceptable) - Proficiency in Python or R (to support Machine Learning) - Data visualization experience (ex: PBI, Tableau) - Project management experience (relevant coursework is acceptable) https://lnkd.in/dvDMBeus


What is neuromorphic computing? - Dataconomy

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Neuromorphic computing is a growing computer engineering approach that models and develops computing devices inspired by the human brain. Neuromorphic engineering focuses on using biology-inspired algorithms to design semiconductor chips that will behave similarly to a brain neuron and then work in this new architecture. Neuromorphic computing adds abilities to think creatively, recognize things they've never seen, and react accordingly to machines. Unlike AIs, the human brain is fascinating at understanding cause and effect and adapts to changes swiftly. However, even the slightest change in their environment renders AI models trained with traditional machine learning methods inoperable.


Research Papers to read based on Robotic Manipulation part1(Artificial Intelligence)

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BulletArm is designed around two key principles: reproducibility and extensibility. We aim to encourage more direct comparisons between robotic learning methods by providing a set of standardized benchmark tasks in simulation alongside a collection of baseline algorithms. The framework consists of 31 different manipulation tasks of varying difficulty, ranging from simple reaching and picking tasks to more realistic tasks such as bin packing and pallet stacking. In addition to the provided tasks, BulletArm has been built to facilitate easy expansion and provides a suite of tools to assist users when adding new tasks to the framework. Moreover, we introduce a set of five benchmarks and evaluate them using a series of state-of-the-art baseline algorithms.


Machine learning methods getting approval in P&C rate filings – S&P Global

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Broadly speaking, machine learning refers to models that improve based on data fed to them. In essence, they learn and adapt with new information.


Predicting Electricity Infrastructure Induced Wildfire Risk in California

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This paper examines the use of risk models to predict the timing and location of wildfires caused by electricity infrastructure. Our data include historical ignition and wire-down points triggered by grid infrastructure collected between 2015 to 2019 in Pacific Gas Electricity territory along with various weather, vegetation, and very high resolution data on grid infrastructure including location, age, materials. With these data we explore a range of machine learning methods and strategies to manage training data imbalance. The best area under the receiver operating characteristic we obtain is 0.776 for distribution feeder ignitions and 0.824 for transmission line wire-down events, both using the histogram-based gradient boosting tree algorithm (HGB) with under-sampling. We then use these models to identify which information provides the most predictive value.


A machine-learning method hallucinates its way to better text translation

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As babies, we babble and imitate our way to learning languages. We don't start off reading raw text, which requires fundamental knowledge and understanding about the world, as well as the advanced ability to interpret and infer descriptions and relationships. Rather, humans begin our language journey slowly, by pointing and interacting with our environment, basing our words and perceiving their meaning through the context of the physical and social world. Eventually, we can craft full sentences to communicate complex ideas. Similarly, when humans begin learning and translating into another language, the incorporation of other sensory information, like multimedia, paired with the new and unfamiliar words, like flashcards with images, improves language acquisition and retention. Then, with enough practice, humans can accurately translate new, unseen sentences in context without the accompanying media; however, imagining a picture based on the original text helps.


Understanding Kernel Learning Methods(Artificial Intelligence)

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Abstract: Diffusion Magnetic Resonance Imaging (dMRI) is a promising method to analyze the subtle changes in the tissue structure. However, the lengthy acquisition time is a major limitation in the clinical application of dMRI. Different image acquisition techniques such as parallel imaging, compressed sensing, has shortened the prolonged acquisition time but creating high-resolution 3D dMRI slices still requires a significant amount of time. In this study, we have shown that high-resolution 3D dMRI can be reconstructed from the highly undersampled k-space and q-space data using a Kernel LowRank method. Abstract: In this work we introduce KERNELIZED TRANSFORMER, a generic, scalable, data driven framework for learning the kernel function in Transformers.


Papers based on Human Recognition Part1(Artificial Intelligence)

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Abstract: Analysis of human affect plays a vital role in human-computer interaction (HCI) systems. Due to the difficulty in capturing large amounts of real-life data, most of the current methods have mainly focused on controlled environments, which limit their application scenarios. To tackle this problem, we propose our solution based on the ensemble learning method. Specifically, we formulate the problem as a classification task, and then train several expression classification models with different types of backbones -- ResNet, EfficientNet and InceptionNet. After that, the outputs of several models are fused via model ensemble method to predict the final results.


Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness

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Document understanding is a key business process in the data-driven economy since documents are central to knowledge discovery and business insights. Converting documents into a machine-processable format is a particular challenge here due to their huge variability in formats and complex structure. Accordingly, many algorithms and machine-learning methods emerged to solve particular tasks such as Optical Character Recognition (OCR), layout analysis, table-structure recovery, figure understanding, etc. We observe the adoption of such methods in document understanding solutions offered by all major cloud providers. Yet, publications outlining how such services are designed and optimized to scale in the cloud are scarce.


Data Scientist - Bosch Group

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Company Description Robert Bosch is a world-class engineering and manufacturing company. Our products impact hundreds of millions of people every day, many in safety critical systems. Artificial intelligence is impacting Bosch’s products and services in many domains, including: manufacturing, autonomous driving, predictive maintenance, vehicle diagnostics and supply chain & logistics. The Bosch Center for Artificial Intelligence provides AI technologies to Bosch’s business units and plants. We are a global team of data scientists, engineers and research scientists focused on applying state-of-the-art AI models to improve all aspects of Bosch’s products and operations. Job Description This is a technical position for someone who is skilled at bringing together disparate technologies to solve business problems. Primary Responsibilities: * Design and implement internal data products that have a significant impact on Bosch global business. * Share knowledge by clearly articulating results and ideas to customers, managers, and key decision makers. * Select and apply appropriate statistical, machine learning, and computing methods to large-scale, high-dimensional data * Write clear, maintainable and tested code * Stay current with the latest research and technology and communicate your knowledge throughout the enterprise * Take responsibility for preparing data for analysis, and provide critical feedback on issues of data integrity * Contribute to Bosch's patent portfolio * Up to 10% travel may be required. Qualifications Basic Qualifications: * Ph.D. (or M.S with 2+ years relevant work experience.) in Computer Science, Statistics, or related quantitative technical fields * 2+ years experience in applying machine learning and other analytic methods to solve practical business problems * Experience in machine learning algorithms & underlying mathematical/statistical principles * Programming experience in Python and/or R, including common data science libraries (e.g. scikit-learn, pandas, numpy) * Experience with software engineering best practices and able to write code for production systems Preferred Qualifications: * Hands-on experience with neural networks and deep learning methods * Experience working with big data and associated distributed processing tools (e.g. Spark) as well as cloud services (e.g. AWS, Azure) * Experience deploying machine learning models in production systems * Excellent communication and documentation skills Additional Information BOSCH is a proud supporter of STEM (Science, Technology, Engineering & Mathematics) Initiatives * FIRST Robotics (For Inspiration and Recognition of Science and Technology) * AWIM (A World In Motion) By choice, we are committed to a diverse workforce – EOE/Protected Veteran/Disabled. For more information on our culture and benefits, please visit: Culture and Benefits | Bosch in the USA