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AWS

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AWS | Databricks ML Dev Day - Every enterprise today wants to accelerate innovation by building Data and ML into their business. However, most companies struggle with preparing large datasets for analytics, managing the proliferation of Data and ML frameworks, and moving models in development to production. In this virtual workshop, we’ll cover best practices for enterprises to use powerful open source technologies to simplify and scale your Data and ML efforts. We’ll discuss how to leverage Apache Spark™, the de-facto data processing and analytics engine in enterprises today, for data preparation as it unifies data at massive scale across various sources. You’ll also learn how to use Data and ML frameworks (i.e. TensorFlow, XGBoost, Scikit-Learn, etc.) to train models based on different requirements. And finally, you can learn how to use MLflow to track experiment runs between multiple users within a reproducible environment, and manage the deployment of models to production on Amazon SageMaker. Join this virtual workshop to learn how Unified Data Analytics can bring Data Science, Business Analytics and engineering together to accelerate your Data and ML efforts. This virtual workshop will give you the opportunity to:Learn how to build highly scalable and reliable pipelines for analyticsDeeper insight into Apache Spark and Databricks, including the latest updates with Delta LakeTrain a model against data and learn best practices for working with ML frameworks (i.e. - TensorFlow, XGBoost, Scikit-Learn, etc.)Learn about MLflow to track experiments, share projects and deploy models in the cloud with Amazon SageMakerWe will use Zoom for a virtual meeting environment. Your Zoom link will be sent to you upon registration.  We look forward to seeing you on July 29th.  Slalom Privacy Policy - Wednesday, July 29, 2020


Radiology: Artificial Intelligence

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Those of us who see the great potential of artificial intelligence in radiology are eager to assure that AI systems work to the benefit of all of our patients. To do so, we must be aware of possibilities for error. In quality management, a latent error is a failure that is "waiting to happen," often due to an oversight in design or execution. Modern AI systems are complex: they can entail hundreds of layers with thousands of connections. It's well known that deep learning systems can associate extraneous features with their intended goals.


Applications of machine learning to diagnosis and treatment of neurodegenerative diseases

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Globally, there is a huge unmet need for effective treatments for neurodegenerative diseases. The complexity of the molecular mechanisms underlying neuronal degeneration and the heterogeneity of the patient population present massive challenges to the development of early diagnostic tools and effective treatments for these diseases. Machine learning, a subfield of artificial intelligence, is enabling scientists, clinicians and patients to address some of these challenges. In this Review, we discuss how machine learning can aid early diagnosis and interpretation of medical images as well as the discovery and development of new therapies. A unifying theme of the different applications of machine learning is the integration of multiple high-dimensional sources of data, which all provide a different view on disease, and the automated derivation of actionable insights. In this Review, the authors describe the latest developments in the use of machine learning to interrogate neurodegenerative disease-related datasets. They discuss applications of machine learning to diagnosis, prognosis and therapeutic development, and the challenges involved in analysing health-care data.


Python For Beginners Part-1

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Udemy Coupon - Python For Beginners Part-1, Beginner to Expert Python.Start from the basics and go all the way to creating your own applications and games! New Created by Suraj Nimbalkar English [Auto]00 Students also bought Advanced AI: Deep Reinforcement Learning in Python ayesian Machine Learning in Python: A/B Testing 2020 Complete Python Bootcamp: From Zero to Hero in Python Python and Django Full Stack Web Developer Bootcamp ython A-Z: Python For Data Science With Real Exercises! Learn Python & Ethical Hacking From Scratch Preview this Course GET COUPON CODE Description Learn Python From Scratch I've created thorough, extensive, but easy to follow content which you'll easily understand and absorb. The course starts with the basics, including Python fundamentals, programming, and user interaction. The curriculum is going to be very hands-on as we walk you from start to finish becoming a professional Python developer.


Solving fruits classification problem in Python – Sushrut Tendulkar

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In this blog post we'll try to understand how to do a simple classification on fruits data. Dataset contains fruit names as target variables and mass, width, height and color score as features. It is a simple data set with less than 100 training examples. To understand the distribution of fruit names let's plot count of each category using seaborn library. Looks like all the fruits are equally distributed except mandarin.


Intelligent algorithms for genome research

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Although the importance of machine learning methods in genome research has grown steadily in recent years, researchers have often had to resort to using obsolete software. Scientists in clinical research often did not have access to the most recent models. This will change with the new free open access repository: Kipoi enables an easy exchange of machine learning models in the field of genome research. The repository was created by Julien Gagneur, Assistant Professor of Computational Biology at the TUM, in collaboration with researchers from the University of Cambridge, Stanford University, the European Bioinformatics Institute (EMBL-EBI) and the European Molecular Biology Laboratory (EMBL). "What makes Kipoi special is that it provides free access to machine learning models that have already been trained," says Julien Gagneur.


Deep Dive Into Big Pharma AI Productivity: One Study Shaking The Pharmaceutical Industry

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The pharmaceutical business is perhaps the only industry on the planet, where to get the product from idea to market the company needs to spend about a decade, several billion dollars, and there is about 90% chance of failure. It is very different from the IT business, where only the paranoid survive but a business where executives need to plan decades ahead and execute. So when the revolution in artificial intelligence fueled by credible advances in deep learning hit in 2013-2014, the pharmaceutical industry executives got interested but did not immediately jump on the bandwagon. Many pharmaceutical companies started investing heavily in internal data science R&D but without a coordinated strategy it looked more like re-branding exercise with the many heads of data science, digital, and AI in one organization and often in one department. And while some of the pharmaceutical companies invested in AI startups no sizable acquisitions were made to date.


What AlexNet Brought To The World Of Deep Learning

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The AlexNet convolutional neural network(CNN) was introduced in the year 2012. Since then, the utilization of deep convolutional neural network has skyrocketed to the point where several machine learning solutions leverage deep CNNs. This article will present the essential findings, and talking points of the research paper, in which the AlexNet architecture was introduced. Machine learning and Deep learning practitioner of all levels can follow along with the content presented in this article.


Fundamentals of Machine Learning [Hindi][Python]

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Online Courses Udemy - Machine Learning, Fundamentals of Machine Learning [Hindi][Python] Complete hands-on Machine Learning Course with Data Science, NLP, Deep Learning and Artificial Intelligence Created by Rishi Bansal English Students also bought Machine Learning and AI: Support Vector Machines in Python Data Science: Supervised Machine Learning in Python Machine Learning A-Z: Hands-On Python & R In Data Science Machine Learning, Data Science and Deep Learning with Python Data Science and Machine Learning Bootcamp with R Machine Learning Practical: 6 Real-World Applications Preview this course GET COUPON CODE Description This course is designed to understand basic Concept of Machine Learning. Anyone can opt for this course. No prior understanding of Machine Learning is required. NOTE: Course is still under Development. You will see new topics will get added regularly. Now question is why this course?


Automated Machine Learning is the Future of Data Science

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As the fuel that powers their progressing digital transformation endeavors, organizations wherever are searching for approaches to determine as much insight as could reasonably be expected from their data. The accompanying increased demand for advanced predictive and prescriptive analytics has, thus, prompted a call for more data scientists capable with the most recent artificial intelligence (AI) and machine learning (ML) tools. However, such highly-skilled data scientists are costly and hard to find. Truth be told, they're such a valuable asset, that the phenomenon of the "citizen data scientist" has of late emerged to help close the skills gap. A corresponding role, as opposed to an immediate substitution, citizen data scientists need explicit advanced data science expertise. However, they are fit for producing models utilizing best in class diagnostic and predictive analytics.