Amazon SageMaker è un servizio gestito per sviluppatori e data scientist che consente di progettare, addestrare e distribuire modelli di Machine Learning su larga scala. In questo webinar esploreremo le funzionalità di questo servizio, dalle istanze notebook Jupyter ai servizi di training e hosting, per poi discutere di aspetti come il labeling di dataset e l’ottimizzazione dei modelli. Successivamente, vedremo in modo pratico come utilizzare il servizio per implementare, addestrare e distribuire un modello di esempio.
Unleash the Power of ML with AWS | AWS Summit Tel Aviv 2019AWS Summits
How can we use Machine Learning to drive innovation?In this session, we present how to democratize ML and give every team the ability to use ML for innovation.We’ll demonstrate how we can use Sagemaker’s built in algorithms and distributed training to experiment more often and iterate faster. We’ll build a prediction of flights delay and integrate it to the product to increase the efficiency of the ground processes. In addition, we present the use of Amazon Forecast for predicting the number of flights that might be delayed in the next few days.
Unleash the Power of ML with AWS | AWS Summit Tel Aviv 2019Amazon Web Services
How can we use Machine Learning to drive innovation?In this session, we present how to democratize ML and give every team the ability to use ML for innovation.We’ll demonstrate how we can use Sagemaker’s built in algorithms and distributed training to experiment more often and iterate faster. We’ll build a prediction of flights delay and integrate it to the product to increase the efficiency of the ground processes. In addition, we present the use of Amazon Forecast for predicting the number of flights that might be delayed in the next few days.
Learn to identify use cases for machine learning (ML), acquire best practices to frame problems in a way that key stakeholders can understand and support, and help create the right conditions for delivering successful ML-based solutions to your citizens. Understand AWS ML and AI services while relating to your specific requirements.
Speakers:
Manav Sehgal, Head of Solutions Architecture, AISPL
Atanu Roy, Specialist Solutions Architect, AISPL
Artifical Intelligence and Machine Learning 201, AWS Federal Pop-Up LoftAmazon Web Services
Come join us for a one-day session where you will learn about the science of computer vision (CV) and train custom CV models utilizing Amazon SageMaker. In this course, you'll learn about Amazon's managed machine learning platform and utilize publicly available real-world ground truth data sets to train models leveraging the built-in ML algorithms of Amazon SageMaker to detect objects and buildings. This is a hands-on workshop, attendees should bring your own laptops.
Applying Maching Learning to Build Smarter Video WorkflowsAmazon Web Services
Christopher Kuthan, Worldwide Business Development Lead, Media - Solutions, AWS
This session provided a deep-dive into how you can harness the capabilities of Machine Learning to build smarter video workflows, create additional content value, and transform the viewing experience. This session incorporated live demonstrations of video use cases.
Amazon SageMaker è un servizio gestito per sviluppatori e data scientist che consente di progettare, addestrare e distribuire modelli di Machine Learning su larga scala. In questo webinar esploreremo le funzionalità di questo servizio, dalle istanze notebook Jupyter ai servizi di training e hosting, per poi discutere di aspetti come il labeling di dataset e l’ottimizzazione dei modelli. Successivamente, vedremo in modo pratico come utilizzare il servizio per implementare, addestrare e distribuire un modello di esempio.
Unleash the Power of ML with AWS | AWS Summit Tel Aviv 2019AWS Summits
How can we use Machine Learning to drive innovation?In this session, we present how to democratize ML and give every team the ability to use ML for innovation.We’ll demonstrate how we can use Sagemaker’s built in algorithms and distributed training to experiment more often and iterate faster. We’ll build a prediction of flights delay and integrate it to the product to increase the efficiency of the ground processes. In addition, we present the use of Amazon Forecast for predicting the number of flights that might be delayed in the next few days.
Unleash the Power of ML with AWS | AWS Summit Tel Aviv 2019Amazon Web Services
How can we use Machine Learning to drive innovation?In this session, we present how to democratize ML and give every team the ability to use ML for innovation.We’ll demonstrate how we can use Sagemaker’s built in algorithms and distributed training to experiment more often and iterate faster. We’ll build a prediction of flights delay and integrate it to the product to increase the efficiency of the ground processes. In addition, we present the use of Amazon Forecast for predicting the number of flights that might be delayed in the next few days.
Learn to identify use cases for machine learning (ML), acquire best practices to frame problems in a way that key stakeholders can understand and support, and help create the right conditions for delivering successful ML-based solutions to your citizens. Understand AWS ML and AI services while relating to your specific requirements.
Speakers:
Manav Sehgal, Head of Solutions Architecture, AISPL
Atanu Roy, Specialist Solutions Architect, AISPL
Artifical Intelligence and Machine Learning 201, AWS Federal Pop-Up LoftAmazon Web Services
Come join us for a one-day session where you will learn about the science of computer vision (CV) and train custom CV models utilizing Amazon SageMaker. In this course, you'll learn about Amazon's managed machine learning platform and utilize publicly available real-world ground truth data sets to train models leveraging the built-in ML algorithms of Amazon SageMaker to detect objects and buildings. This is a hands-on workshop, attendees should bring your own laptops.
Applying Maching Learning to Build Smarter Video WorkflowsAmazon Web Services
Christopher Kuthan, Worldwide Business Development Lead, Media - Solutions, AWS
This session provided a deep-dive into how you can harness the capabilities of Machine Learning to build smarter video workflows, create additional content value, and transform the viewing experience. This session incorporated live demonstrations of video use cases.
by Pratap Ramamurthy, Partner Solutions Architect
Organizations are increasingly turning to machine learning to build intelligent applications and get more insights out of their data in real-time. In this session, you’ll learn about AWS Machine Learning APIs for computer vision and language, and how to get started with these pre-trained services: Amazon Rekognition, Amazon Comprehend, Amazon Transcribe, Amazon Translate, Amazon Polly, and Amazon Lex. We’ll also show how these services connect to AWS’s comprehensive data platform and services to drive the success of your machine learning projects.
Build Text Analytics Solutions with Amazon Comprehend and Amazon TranslateAmazon Web Services
by Pratap Ramamurthy, Partner Solutions Architect, AWS
Natural language holds a wealth of information like user sentiment and conversational intent. In this session, we'll demonstrate the capabilities of Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. We'll show you how to build a VOC (Voice of the Customer) application and integrate it with other AWS services including AWS Lambda, Amazon S3, Amazon Athena, Amazon QuickSight, and Amazon Translate. We’ll also show you additional methods for NLP available through Amazon Sagemaker.
AWS Summit Singapore 2019 | Build, Train and Deploy Deep Learning Models on A...AWS Summits
Speaker: Pedro Paez, Specialist Solutions Architect, AWS
Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform.
Amazon SageMaker - ML for every developer & data scientist ft. Workday - AIM2...Amazon Web Services
Machine learning (ML) provides innovation for every business. Until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker—a fully managed service that enables developers to build, train, and deploy ML models at scale—overcomes these barriers. We review its capabilities across data labeling, model building, model training, tuning, and production hosting. Additionally, Workday—leading provider of enterprise cloud applications for financial management, human capital management, and analytics—shares how it accelerated ML throughout its organization, the benefits gained, and why it standardized on Amazon SageMaker.
Machine learning for developers & data scientists with Amazon SageMaker - AIM...Amazon Web Services
Machine learning (ML) offers innovation for every business. But until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy ML models at scale, overcomes these barriers. We review its capabilities, including data labeling, model building, model training, tuning, and production hosting.
Learn how to quickly build, train, and deploy machine learning models using Amazon SageMaker, an end-to-end machine learning platform. Amazon SageMaker simplifies machine learning with pre-built algorithms, support for popular deep learning frameworks, such as PyTorch, TensorFlow, and Apache MXNet, as well as one-click model training and deployment.
Business are continuously looking for ways to leverage artifical intelligence to help scale their customer service and support departments. In this session we will step through the process of building a Virtual Concierge experience, powered by Amazon Sumerian, that is able to recognise a visitor at the edge with the AWS DeepLens. You will gain an understanding of the machine learning algorithms that underpin this solution.
Accelerate Machine Learning with Ease Using Amazon SageMaker - BDA301 - Chica...Amazon Web Services
Organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took much time and effort, and it required expertise. In this session, we discuss and dive deep into Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models quickly and easily. We walk through the features and benefits of Amazon SageMaker and discuss the uniquely designed ML algorithms that allow for optimized model training, getting you to production fast.
Turbocharge your business with AI and Machine Learning | AWS Summit Tel Aviv ...Amazon Web Services
We will also present Amazon DocumentDB (with MongoDB compatibility), a fast, scalable, highly available, and fully managed document database service that supports MongoDB workloads.
SKL208 - Turbocharge your Business with AI and Machine Learning - Tel Aviv Su...Boaz Ziniman
This session will focus on the basic building blocks of Artificial Intelligence (AI) and Machine Learning (ML) using AWS services. It will help you to identify use cases for ML with real-world examples, and help you create the right conditions for delivering successful ML-based solutions to your business.
by Pratap Ramamurthy, Partner Solutions Architect
Organizations are increasingly turning to machine learning to build intelligent applications and get more insights out of their data in real-time. In this session, you’ll learn about AWS Machine Learning APIs for computer vision and language, and how to get started with these pre-trained services: Amazon Rekognition, Amazon Comprehend, Amazon Transcribe, Amazon Translate, Amazon Polly, and Amazon Lex. We’ll also show how these services connect to AWS’s comprehensive data platform and services to drive the success of your machine learning projects.
Build Text Analytics Solutions with Amazon Comprehend and Amazon TranslateAmazon Web Services
by Pratap Ramamurthy, Partner Solutions Architect, AWS
Natural language holds a wealth of information like user sentiment and conversational intent. In this session, we'll demonstrate the capabilities of Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. We'll show you how to build a VOC (Voice of the Customer) application and integrate it with other AWS services including AWS Lambda, Amazon S3, Amazon Athena, Amazon QuickSight, and Amazon Translate. We’ll also show you additional methods for NLP available through Amazon Sagemaker.
AWS Summit Singapore 2019 | Build, Train and Deploy Deep Learning Models on A...AWS Summits
Speaker: Pedro Paez, Specialist Solutions Architect, AWS
Amazon SageMaker is a fully managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform.
Amazon SageMaker - ML for every developer & data scientist ft. Workday - AIM2...Amazon Web Services
Machine learning (ML) provides innovation for every business. Until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker—a fully managed service that enables developers to build, train, and deploy ML models at scale—overcomes these barriers. We review its capabilities across data labeling, model building, model training, tuning, and production hosting. Additionally, Workday—leading provider of enterprise cloud applications for financial management, human capital management, and analytics—shares how it accelerated ML throughout its organization, the benefits gained, and why it standardized on Amazon SageMaker.
Machine learning for developers & data scientists with Amazon SageMaker - AIM...Amazon Web Services
Machine learning (ML) offers innovation for every business. But until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy ML models at scale, overcomes these barriers. We review its capabilities, including data labeling, model building, model training, tuning, and production hosting.
Learn how to quickly build, train, and deploy machine learning models using Amazon SageMaker, an end-to-end machine learning platform. Amazon SageMaker simplifies machine learning with pre-built algorithms, support for popular deep learning frameworks, such as PyTorch, TensorFlow, and Apache MXNet, as well as one-click model training and deployment.
Business are continuously looking for ways to leverage artifical intelligence to help scale their customer service and support departments. In this session we will step through the process of building a Virtual Concierge experience, powered by Amazon Sumerian, that is able to recognise a visitor at the edge with the AWS DeepLens. You will gain an understanding of the machine learning algorithms that underpin this solution.
Accelerate Machine Learning with Ease Using Amazon SageMaker - BDA301 - Chica...Amazon Web Services
Organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took much time and effort, and it required expertise. In this session, we discuss and dive deep into Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models quickly and easily. We walk through the features and benefits of Amazon SageMaker and discuss the uniquely designed ML algorithms that allow for optimized model training, getting you to production fast.
Turbocharge your business with AI and Machine Learning | AWS Summit Tel Aviv ...Amazon Web Services
We will also present Amazon DocumentDB (with MongoDB compatibility), a fast, scalable, highly available, and fully managed document database service that supports MongoDB workloads.
SKL208 - Turbocharge your Business with AI and Machine Learning - Tel Aviv Su...Boaz Ziniman
This session will focus on the basic building blocks of Artificial Intelligence (AI) and Machine Learning (ML) using AWS services. It will help you to identify use cases for ML with real-world examples, and help you create the right conditions for delivering successful ML-based solutions to your business.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.