Machine Learning Real Life Applications By Examples - Mario CartiaData Driven Innovation
Durante il talk verranno illustrati 3 casi d'uso reali di utilizzo del machine learning da parte delle maggiori piattaforme web (Google, Facebook, Amazon, Twitter, PayPal) per l'implementazione di particolari features. Per ciascun esempio verrà spiegato l'algoritmo utilizzato mostrando come realizzare le medesime funzionalità attraverso l'utilizzo di Apache Spark MLlib e del linguaggio Scala.
Machine Learning Real Life Applications By Examples - Mario CartiaData Driven Innovation
Durante il talk verranno illustrati 3 casi d'uso reali di utilizzo del machine learning da parte delle maggiori piattaforme web (Google, Facebook, Amazon, Twitter, PayPal) per l'implementazione di particolari features. Per ciascun esempio verrà spiegato l'algoritmo utilizzato mostrando come realizzare le medesime funzionalità attraverso l'utilizzo di Apache Spark MLlib e del linguaggio Scala.
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
Suggestions:
1) For best quality, download the PDF before viewing.
2) Open at least two windows: One for the Youtube video, one for the screencast (link below), and optionally one for the slides themselves.
3) The Youtube video is shown on the first page of the slide deck, for slides, just skip to page 2.
Screencast: http://youtu.be/VoL7JKJmr2I
Video recording: http://youtu.be/CJRvb8zxRdE (Thanks to Al Friedrich!)
In this talk, we take Deep Learning to task with real world data puzzles to solve.
Data:
- Higgs binary classification dataset (10M rows, 29 cols)
- MNIST 10-class dataset
- Weather categorical dataset
- eBay text classification dataset (8500 cols, 500k rows, 467 classes)
- ECG heartbeat anomaly detection
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Perché scegliere un ITS? Perché scegliere la nostra Fondazione ITS Kennedy? Ce lo spiega il Prof. Marco Parenzan nell'ambito dell'open day organizzato il 13 settembre 2014 dalla Fondazione ITS Kennedy di Pordenone, evento in cui sono stati presentati i temi per i corsi in partenza a novembre 2014.
Perchè a Pordenone è nato l'ITS Kennedy?
Per rispondere all'esigenza di preparare figure professionali sui grandi valori, sulle moderne ed innovative tecnologie e tecniche che già oggi, ma soprattutto domani, saranno richieste ai nuovi professionisti.
2000 ore di formazione, tra aula/laboratorio e stage in azienda, per parlare di Mobile, di Cloud Computing e Big Data. Nel 2014 sono in avviamento i corsi di Cloud Computing e Intelligence Data Analisys.
Perchè venire all'ITS Kennedy di Pordenone? Perchè è un corso su Tecnologie e Tecnica, dove i tecnici sono docente e insegnano per formare dei tecnici. L'unico posto dove per 2000 ore si parla e si formano tecnici sul Cloud, sul Mobile e sui Big Data
Workshop di co-progettazione: open data, wi-fi, inclusione digitaleRegione Emilia-Romagna
Tematiche del workshop:
1 > banca regionale del dato per favorire individuazione e riutilizzo dati pubblici
2 > wifi per un accesso ubiquo, libero e gratuito alla rete
3 > inclusione sociale (formazione digitale, facilitazione digitale e cultura digitale)
Azure for DreamSpark: student's benefits and how to create a blog hosted by W...Angelo Gino Varrati
"Azure for DreamSpark: student's benefits and how to create a blog hosted by WordPress" was exposed for DotNet Abruzzo by a MSP (Microsoft Student Partners) in L'Aquila on April 28 - 2016.
"Sviluppo Windows 10 e mobile a 360 gradi"
Real Solutions Day - Progetto e gestione del lavoro: ALM in breve con Visual ...Davide Benvegnù
Slide della sessione sulla gestione del lavoro e della gestione del ciclo di vita dell'applicazione con Visual Studio Online dell'evento "Real Solutions Day"
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
Suggestions:
1) For best quality, download the PDF before viewing.
2) Open at least two windows: One for the Youtube video, one for the screencast (link below), and optionally one for the slides themselves.
3) The Youtube video is shown on the first page of the slide deck, for slides, just skip to page 2.
Screencast: http://youtu.be/VoL7JKJmr2I
Video recording: http://youtu.be/CJRvb8zxRdE (Thanks to Al Friedrich!)
In this talk, we take Deep Learning to task with real world data puzzles to solve.
Data:
- Higgs binary classification dataset (10M rows, 29 cols)
- MNIST 10-class dataset
- Weather categorical dataset
- eBay text classification dataset (8500 cols, 500k rows, 467 classes)
- ECG heartbeat anomaly detection
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Perché scegliere un ITS? Perché scegliere la nostra Fondazione ITS Kennedy? Ce lo spiega il Prof. Marco Parenzan nell'ambito dell'open day organizzato il 13 settembre 2014 dalla Fondazione ITS Kennedy di Pordenone, evento in cui sono stati presentati i temi per i corsi in partenza a novembre 2014.
Perchè a Pordenone è nato l'ITS Kennedy?
Per rispondere all'esigenza di preparare figure professionali sui grandi valori, sulle moderne ed innovative tecnologie e tecniche che già oggi, ma soprattutto domani, saranno richieste ai nuovi professionisti.
2000 ore di formazione, tra aula/laboratorio e stage in azienda, per parlare di Mobile, di Cloud Computing e Big Data. Nel 2014 sono in avviamento i corsi di Cloud Computing e Intelligence Data Analisys.
Perchè venire all'ITS Kennedy di Pordenone? Perchè è un corso su Tecnologie e Tecnica, dove i tecnici sono docente e insegnano per formare dei tecnici. L'unico posto dove per 2000 ore si parla e si formano tecnici sul Cloud, sul Mobile e sui Big Data
Workshop di co-progettazione: open data, wi-fi, inclusione digitaleRegione Emilia-Romagna
Tematiche del workshop:
1 > banca regionale del dato per favorire individuazione e riutilizzo dati pubblici
2 > wifi per un accesso ubiquo, libero e gratuito alla rete
3 > inclusione sociale (formazione digitale, facilitazione digitale e cultura digitale)
Azure for DreamSpark: student's benefits and how to create a blog hosted by W...Angelo Gino Varrati
"Azure for DreamSpark: student's benefits and how to create a blog hosted by WordPress" was exposed for DotNet Abruzzo by a MSP (Microsoft Student Partners) in L'Aquila on April 28 - 2016.
"Sviluppo Windows 10 e mobile a 360 gradi"
Real Solutions Day - Progetto e gestione del lavoro: ALM in breve con Visual ...Davide Benvegnù
Slide della sessione sulla gestione del lavoro e della gestione del ciclo di vita dell'applicazione con Visual Studio Online dell'evento "Real Solutions Day"
"Piattaforme online per la partecipazione: ioPartecipo+" nell'ambito di: Settimana dell'Amministrazione Aperta, Come condurre una consultazione pubblica
Webinar Formez 10 Marzo 2017
Presentato al sesto WebMeetup del Machine Learning / Data Science Meetup Roma: https://www.meetup.com/it-IT/Machine-Learning-Data-Science-Meetup/events/273089965/
Presentazione per il sesto WebMeetup del Machine Learning / Data Science Meetup Roma: https://www.meetup.com/it-IT/Machine-Learning-Data-Science-Meetup/events/273089965/
Paolo Galeone - Dissecting tf.function to discover auto graph strengths and s...MeetupDataScienceRoma
Original presentation available on GitHub: https://pgaleone.eu/tf-function-talk/
Meetup: https://www.meetup.com/it-IT/Machine-Learning-Data-Science-Meetup/events/264338606/
Multimodal AI Approach to Provide Assistive Services (Francesco Puja)MeetupDataScienceRoma
Presentazione dal Meetup del Machine Learning / Data Science Meetup di Roma - Giugno 2019:
https://www.meetup.com/it-IT/Machine-Learning-Data-Science-Meetup/events/262120815/
Presentazione dal Meetup del Machine Learning / Data Science Meetup di Roma - Giugno 2019:
https://www.meetup.com/it-IT/Machine-Learning-Data-Science-Meetup/events/262120815/
Zero, One, Many - Machine Learning in Produzione (Luca Palmieri)MeetupDataScienceRoma
Talk dal Meetup del Machine Learning / Data Science Meetup di Roma - Giugno 2019:
https://www.meetup.com/it-IT/Machine-Learning-Data-Science-Meetup/events/262120815/
2. Agenda
19:00 Ingresso e Registrazione
19:00-19:10 Presentazione del Meetup
19:10-19:40 Intelligenza artificiale per il tuo business
[Gianluca Mauro (AI-Academy)]
19:40-20:10 Serverless Data Architecture at scale on GCP
[Lorenzo Ridi (Noovle)]
20:10-21:00 Networking
3. Le tre «domande» del Meetup
1. « Sono appassionato di ML, dove trovo altri esperti
del settore? »
2. « Cerchiamo qualcuno esperto di ML, ne conosci? »
3. « Mi piacerebbe avvicinarmi al ML, come faccio? »
4. TensorFlow Dev Summit Ext Roma 2017
Insieme a Google Developer Group Roma Lazio Abruzzo:
Quando: Giovedì 16 Febbraio 2017
Dove: Talent Garden Cinecittà – TAG (Via Quinto Publicio, 90)
Agenda:
18:30 - 19:00: TensorFlow and Google, one year of exciting breakthroughs
[Simone Scardapane]
19:00 - 21:00: Extended Streaming / Networking
Sponsored by: