Presentazione sul Machine Learning e lo studio. Esempi di Machine Learning non intuitivi, tenuta in occasione di Young! Orienta il tuo Futuro 2017. Google TensorFlow, IBM Watson, Autodesk ed altri esempi di ML.
I had the opportunity to teach a lab of computer vision for the amazing women studying one of the AllWomen courses.
Here I share the slides used for teaching these 2h labs where I tried to cover in a very high level some of the basic concepts of computer vision.
Young 2018 se non sto attento in classe - esempi inaspettati di machine lea...Andrea Vaccarella
This document discusses machine learning and provides examples of its applications. It begins with a brief history of machine learning and an explanation of how machine learning algorithms are able to learn without being explicitly programmed. It then provides examples of machine learning such as image and speech recognition, recommendation systems, sentiment analysis and more. Both well-known examples, like Google Translate and less known applications in domains like energy consumption and malware detection are covered.
Artificial Intelligence: Classification, Applications, Opportunities, and Cha...Abdullah al Mamun
1. The document discusses various topics related to artificial intelligence including its definition, applications in different fields like agriculture, education, information technology and entertainment.
2. Key concepts discussed include machine learning, deep learning, neural networks, supervised and unsupervised learning, computer vision and natural language processing.
3. Applications of AI mentioned include image and speech recognition, predictive analysis, personalized learning, chatbots, targeted advertising and automated tasks to aid professionals.
Webinar: Will the Real AI Please Stand Up?Interset
In this webinar, Interset CTO Stephan Jou and VP of Products Mario Daigle discussed what to look for when cybersecurity vendors claim to leverage AI for UEBA. View a recording of this webinar at https://zoom.us/webinar/register/WN_0Etv6kilRN-0QuqoNn26bg.
The slide helps to get an insight on the concepts of Artificial Intelligence.
The topics covered are as follows,
* Concept of AI
* Meaning of AI
* History of AI
* Levels of AI
* Types of AI
* Applications of AI - Agriculture, Health, Business (Emerging market), Education
* AI Tools and Platforms
Fossasia ai-ml technologies and application for product development-chetan kh...Chetan Khatri
Train at GPU and Inference at Mobile, Artificial Intelligence / Machine learning Technologies and Applications for AI Driven Product Development. Talk at FOSSASIA 2018, Singapore
The document provides an introduction to knowledge graphs. It discusses how knowledge graphs are being used by large enterprises and intelligent agents to capture concepts, entities, and relationships within domains to drive business, generate insights, and enhance relationships. The presentation will cover an overview of what knowledge graphs are, who uses them, why they are used, and how to use them. It then provides some examples of how knowledge graphs are applied, including in intelligent agents, semantic web, search engines, social networks, biology, enterprise knowledge management, and more.
I had the opportunity to teach a lab of computer vision for the amazing women studying one of the AllWomen courses.
Here I share the slides used for teaching these 2h labs where I tried to cover in a very high level some of the basic concepts of computer vision.
Young 2018 se non sto attento in classe - esempi inaspettati di machine lea...Andrea Vaccarella
This document discusses machine learning and provides examples of its applications. It begins with a brief history of machine learning and an explanation of how machine learning algorithms are able to learn without being explicitly programmed. It then provides examples of machine learning such as image and speech recognition, recommendation systems, sentiment analysis and more. Both well-known examples, like Google Translate and less known applications in domains like energy consumption and malware detection are covered.
Artificial Intelligence: Classification, Applications, Opportunities, and Cha...Abdullah al Mamun
1. The document discusses various topics related to artificial intelligence including its definition, applications in different fields like agriculture, education, information technology and entertainment.
2. Key concepts discussed include machine learning, deep learning, neural networks, supervised and unsupervised learning, computer vision and natural language processing.
3. Applications of AI mentioned include image and speech recognition, predictive analysis, personalized learning, chatbots, targeted advertising and automated tasks to aid professionals.
Webinar: Will the Real AI Please Stand Up?Interset
In this webinar, Interset CTO Stephan Jou and VP of Products Mario Daigle discussed what to look for when cybersecurity vendors claim to leverage AI for UEBA. View a recording of this webinar at https://zoom.us/webinar/register/WN_0Etv6kilRN-0QuqoNn26bg.
The slide helps to get an insight on the concepts of Artificial Intelligence.
The topics covered are as follows,
* Concept of AI
* Meaning of AI
* History of AI
* Levels of AI
* Types of AI
* Applications of AI - Agriculture, Health, Business (Emerging market), Education
* AI Tools and Platforms
Fossasia ai-ml technologies and application for product development-chetan kh...Chetan Khatri
Train at GPU and Inference at Mobile, Artificial Intelligence / Machine learning Technologies and Applications for AI Driven Product Development. Talk at FOSSASIA 2018, Singapore
The document provides an introduction to knowledge graphs. It discusses how knowledge graphs are being used by large enterprises and intelligent agents to capture concepts, entities, and relationships within domains to drive business, generate insights, and enhance relationships. The presentation will cover an overview of what knowledge graphs are, who uses them, why they are used, and how to use them. It then provides some examples of how knowledge graphs are applied, including in intelligent agents, semantic web, search engines, social networks, biology, enterprise knowledge management, and more.
Security in the age of Artificial IntelligenceFaction XYZ
The document discusses how artificial intelligence will impact security and introduces both opportunities and challenges. It describes current AI techniques like deep learning and how they are being applied to security domains such as malware detection, network anomaly detection, and insider threat detection. While AI has the potential to make systems more scalable and adaptive, it also introduces new vulnerabilities if misused to generate sophisticated attacks. The document argues for developing morality systems to ensure autonomous systems continue making moral decisions even if compromised.
HKOSCon18 - Chetan Khatri - Open Source AI / ML Technologies and Application ...Chetan Khatri
This document summarizes a presentation about open source AI and machine learning technologies for product development. The presentation discusses key concepts like artificial intelligence, machine learning, deep learning and neural networks. It also provides examples of using computer vision, natural language processing and other AI techniques for applications like self-driving cars, visual search, sentiment analysis and more. Challenges in scaling models and frameworks are discussed along with solutions like ONNX for model interoperability across platforms.
The document discusses various topics related to machine learning including machine learning applications like spam filtering and recommendation systems. It provides definitions and examples of different machine learning categories like supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves providing input and desired output for classification while unsupervised learning allows machines to classify without prior information. Reinforcement learning uses rewards and penalties to direct unsupervised learning through experiences.
App;ying Different Classification Technologies and for Different types of datasets such as Text and image dataset. Here I have used Machine learning and Deep Learning respectively for text and image datasets.
Evolve Machine Learners - Businesses Built on Artificial Intelligence (AI)Akber Khan
Overview of how businesses are using Artificial Intelligence throughout their business. Drones, Smart Speakers, Machine Learning, Sales, Chatbots - all of it in their stack.
This presentation explores the transformative impact of machine learning on the realm of cybersecurity and highlights its potential to revolutionize threat detection, prevention, and response.
Deep Learning - Hype, Reality and Applications in ManufacturingAdam Cook
This is the slide deck for the introductory webinar for our "Artificial Intelligence in Manufacturing" webinar and workshop series within the SME Virtual Network.
The video for this slide deck is located here: https://www.youtube.com/watch?v=orrVqOnFqds
To learn more about the SME Virtual Network and our events, please visit the following links:
https://www.facebook.com/smevirtual/
https://www.linkedin.com/company/smevirtual/
Cyber Security.
Watch my videos on snack here: --> --> http://sck.io/x-B1f0Iy
@ Kindly Follow my Instagram Page to discuss about your mental health problems-
-----> https://instagram.com/mentality_streak?utm_medium=copy_link
@ Appreciate my work:
-----> behance.net/burhanahmed1
Thank-you !
Machine Learning API'S By Mushahid AliMushahid Ali
The document discusses machine learning APIs and libraries like TensorFlow, MLPack, and Shogun that can be used to build machine learning models and automatically learn from experience without being explicitly programmed. It also discusses how machine learning can help make sense of large, unstructured data sets through techniques like classification and converting unstructured data into a structured format. The document provides an overview of machine learning capabilities and services for algorithm design, data modeling, and analyzing different types of data including images, audio, and text.
Presenter: Alvaro Soto
AI can be a hype but it can also transform the way we live and interact with each other. For the past 5 years, we have seen enormous advancements in machine learning techniques and computation that have given rise to one of the most significant advancements in technology. Naturally, like most emergent technologies, its incubation period has been dominated by early adopters who had the resources to build machine learning products that can work in the real world. But in the next 5 years, the barriers to entry will exponentially decrease making AI accessible to millions in every industry. As AI becomes more accessible, the technology will move from the Innovation team of a few companies to the technology stack of every product team.
In this talk, I share my experience designing AI products at scale. I would discuss what it takes to teach AI domain-specific knowledge and will provide a framework for designers and product managers to leverage in their Design Sprints.
The document provides an introduction to generative AI and discusses its capabilities. It outlines the agenda which includes an introduction to AI, the current state of AI, types of AI, popular AI tools, an overview of the Azure OpenAI service, responsible AI, uses and capabilities of generative AI, and a demo. It defines generative AI as AI that can generate new content like text, images, audio or video based on a given input or prompt. The document discusses how generative AI works by learning patterns from large datasets to produce new content that fits within those patterns.
The document provides an introduction to artificial intelligence (AI), including a brief history and the four phases of its development. It discusses what AI is, how it works by collecting and processing data through machine learning algorithms to make inferences. The key domains of AI are described as natural language processing, computer vision, speech recognition, and data. The types of AI are defined based on capabilities as artificial narrow intelligence, artificial general intelligence, and potential future artificial super intelligence. Related fields like machine learning, neural networks, data science, expert systems, and robotics are also outlined. Advantages, disadvantages, relevance to daily life, future possibilities, ethical concerns are presented at a high level.
When dealing with over 300 hundred thousand of malware samples every day, we had to deploy the state-of-the-art techniques to combat cyberthreats. And among them - machine learning algorithms.
In this whitepaper, we start from describing the basic approaches and proceed to explaining the key applications of machine learning algorithms to automated malware detection. Learn more about how Kaspersky Lab protects businesses like yours => https://kas.pr/8dxv
masterclass de introducción a Inteligencia Artificial utilizando las APIs de Google elaborada a partir de la de Mario Ezquerro en GDG La Rioja.
Impartida dentro de las actividades de la Agenda Digital de La Rioja por AERTIC
Artificial intelligence for cctv cameras, video surveillanceHIGHMARK SECURITY
The document discusses how artificial intelligence is being applied to CCTV cameras and video surveillance. It explains that AI uses machine vision and deep learning algorithms to analyze video feeds and detect anomalous behaviors, such as people entering restricted areas. This behavioral analytics approach learns normal behaviors over time and can detect more subtle threats compared to traditional rule-based AI. However, behavioral analytics also produces more false alarms that require human operators to review. The document outlines both the capabilities and limitations of using AI for video surveillance.
Combating Cyber Security Using Artificial IntelligenceInderjeet Singh
Cyber Security & Data Protection India Summit 2018 aims to convene the best minds in Cybersecurity under one roof to create an interactive milieu for exchange of knowledge and ideas. The event will endeavour to address the emerging and continuing threats to Cybersecurity and its changing landscape, as well as respond to increasing risk of security breaches and security governance, application security, cloud based security, Network, Mobile and endpoint security and other cyber risks in the India and abroad.
Agenda:
What is Artificial Intelligence ?
What is Machine Learning?
What is Deep Learning?
What is Data Science?
AI in software testing.
AI in software automation testing.
Demo using testim.io
Why You Shouldn't Worry About Artificial Intelligence...Until You Have ToAWH
We've heard a lot lately about how the machines may be taking over our jobs. AWH founder and principal, Chris Slee, recently discussed artificial intelligence and machine learning - and how it will affect your business in the future.
NEW EMERGING TECHNOLOGIES_AARUSHIJATIAN.pptxAarushiJatian
Machine learning is a form of artificial intelligence that uses algorithms and data to enable computers to learn without being explicitly programmed. The key difference between normal software and machine learning is that machine learning systems are trained by large datasets rather than having their behavior defined by human coding. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Machine learning has many applications including traffic prediction, virtual assistants, and spam filtering.
THIS IS AN INTRODUCTORY PPT OF EMERGING TECHNOLOGIES AND NEED IN REAL LIFE. THIS WIL EXPLAIN BSICS ABOUT ALL EMERGING TECHNOLOGY AND THEIR APPLICATION IN VARIOUS SECTOR
Slide usate nella presentazione di YOUNG! 2018, rassegna di strumenti e consigli utili per tutti i ragazzi per migliorare il proprio studio, operatività e gestione del tempo e del lavoro. Software, metodi, consigli, di tutto! Grazie a chi mi è venuto ad ascoltare dal vivo, spero sia stata utile!
This document provides an overview of Andrea Vaccarella's background and experiences with startups. It discusses his education at Politecnico di Milano and work at Google. It also summarizes his founding of Fluxedo in 2014, an app to manage projects and tasks. The document outlines Fluxedo's features and growth from 5 founders in 2014 to a larger team in 2015. It discusses common challenges for startups like problems, competitors, and everyday issues. Overall, the summary emphasizes Vaccarella's experience in founding startups and developing products through iterations to solve problems.
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Security in the age of Artificial IntelligenceFaction XYZ
The document discusses how artificial intelligence will impact security and introduces both opportunities and challenges. It describes current AI techniques like deep learning and how they are being applied to security domains such as malware detection, network anomaly detection, and insider threat detection. While AI has the potential to make systems more scalable and adaptive, it also introduces new vulnerabilities if misused to generate sophisticated attacks. The document argues for developing morality systems to ensure autonomous systems continue making moral decisions even if compromised.
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This document summarizes a presentation about open source AI and machine learning technologies for product development. The presentation discusses key concepts like artificial intelligence, machine learning, deep learning and neural networks. It also provides examples of using computer vision, natural language processing and other AI techniques for applications like self-driving cars, visual search, sentiment analysis and more. Challenges in scaling models and frameworks are discussed along with solutions like ONNX for model interoperability across platforms.
The document discusses various topics related to machine learning including machine learning applications like spam filtering and recommendation systems. It provides definitions and examples of different machine learning categories like supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves providing input and desired output for classification while unsupervised learning allows machines to classify without prior information. Reinforcement learning uses rewards and penalties to direct unsupervised learning through experiences.
App;ying Different Classification Technologies and for Different types of datasets such as Text and image dataset. Here I have used Machine learning and Deep Learning respectively for text and image datasets.
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Overview of how businesses are using Artificial Intelligence throughout their business. Drones, Smart Speakers, Machine Learning, Sales, Chatbots - all of it in their stack.
This presentation explores the transformative impact of machine learning on the realm of cybersecurity and highlights its potential to revolutionize threat detection, prevention, and response.
Deep Learning - Hype, Reality and Applications in ManufacturingAdam Cook
This is the slide deck for the introductory webinar for our "Artificial Intelligence in Manufacturing" webinar and workshop series within the SME Virtual Network.
The video for this slide deck is located here: https://www.youtube.com/watch?v=orrVqOnFqds
To learn more about the SME Virtual Network and our events, please visit the following links:
https://www.facebook.com/smevirtual/
https://www.linkedin.com/company/smevirtual/
Cyber Security.
Watch my videos on snack here: --> --> http://sck.io/x-B1f0Iy
@ Kindly Follow my Instagram Page to discuss about your mental health problems-
-----> https://instagram.com/mentality_streak?utm_medium=copy_link
@ Appreciate my work:
-----> behance.net/burhanahmed1
Thank-you !
Machine Learning API'S By Mushahid AliMushahid Ali
The document discusses machine learning APIs and libraries like TensorFlow, MLPack, and Shogun that can be used to build machine learning models and automatically learn from experience without being explicitly programmed. It also discusses how machine learning can help make sense of large, unstructured data sets through techniques like classification and converting unstructured data into a structured format. The document provides an overview of machine learning capabilities and services for algorithm design, data modeling, and analyzing different types of data including images, audio, and text.
Presenter: Alvaro Soto
AI can be a hype but it can also transform the way we live and interact with each other. For the past 5 years, we have seen enormous advancements in machine learning techniques and computation that have given rise to one of the most significant advancements in technology. Naturally, like most emergent technologies, its incubation period has been dominated by early adopters who had the resources to build machine learning products that can work in the real world. But in the next 5 years, the barriers to entry will exponentially decrease making AI accessible to millions in every industry. As AI becomes more accessible, the technology will move from the Innovation team of a few companies to the technology stack of every product team.
In this talk, I share my experience designing AI products at scale. I would discuss what it takes to teach AI domain-specific knowledge and will provide a framework for designers and product managers to leverage in their Design Sprints.
The document provides an introduction to generative AI and discusses its capabilities. It outlines the agenda which includes an introduction to AI, the current state of AI, types of AI, popular AI tools, an overview of the Azure OpenAI service, responsible AI, uses and capabilities of generative AI, and a demo. It defines generative AI as AI that can generate new content like text, images, audio or video based on a given input or prompt. The document discusses how generative AI works by learning patterns from large datasets to produce new content that fits within those patterns.
The document provides an introduction to artificial intelligence (AI), including a brief history and the four phases of its development. It discusses what AI is, how it works by collecting and processing data through machine learning algorithms to make inferences. The key domains of AI are described as natural language processing, computer vision, speech recognition, and data. The types of AI are defined based on capabilities as artificial narrow intelligence, artificial general intelligence, and potential future artificial super intelligence. Related fields like machine learning, neural networks, data science, expert systems, and robotics are also outlined. Advantages, disadvantages, relevance to daily life, future possibilities, ethical concerns are presented at a high level.
When dealing with over 300 hundred thousand of malware samples every day, we had to deploy the state-of-the-art techniques to combat cyberthreats. And among them - machine learning algorithms.
In this whitepaper, we start from describing the basic approaches and proceed to explaining the key applications of machine learning algorithms to automated malware detection. Learn more about how Kaspersky Lab protects businesses like yours => https://kas.pr/8dxv
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What is Artificial Intelligence ?
What is Machine Learning?
What is Deep Learning?
What is Data Science?
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AI in software automation testing.
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Slide usate nella presentazione di YOUNG! 2018, rassegna di strumenti e consigli utili per tutti i ragazzi per migliorare il proprio studio, operatività e gestione del tempo e del lavoro. Software, metodi, consigli, di tutto! Grazie a chi mi è venuto ad ascoltare dal vivo, spero sia stata utile!
This document provides an overview of Andrea Vaccarella's background and experiences with startups. It discusses his education at Politecnico di Milano and work at Google. It also summarizes his founding of Fluxedo in 2014, an app to manage projects and tasks. The document outlines Fluxedo's features and growth from 5 founders in 2014 to a larger team in 2015. It discusses common challenges for startups like problems, competitors, and everyday issues. Overall, the summary emphasizes Vaccarella's experience in founding startups and developing products through iterations to solve problems.
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Se non sto attento in classe una macchina mi ruberà il lavoro: esempi inaspettati di machine learning
1. SE NON STO ATTENTO IN CLASSE UNA
MACCHINA MI RUBERÀ IL LAVORO
ANDREA VACCARELLA
YOUNG 2017
19/10/2017
Esempi inaspettati di Machine Learning
2. Giochi e Intelligenze Artificiali
Cos’è il Machine Learning
Cosa “vedono“ le Deep Convolutional Networks
Esempi (si spera inaspettati) di Machine Learning
9. MACHINE LEARNING
Computer Algorithms that have the ability to learn without being explicitly
programmed
Email
Filtering
Program
Real Emails
Spam Emails
10. MACHINE LEARNING
Computer Algorithms that have the ability to learn without being explicitly
programmed
MACHINE
LEARNING
ALGORITHM
Real Emails
Spam Emails
Real Emails
Spam Emails
11. MACHINE LEARNING
“I know I should do something with it, I’m not sure what”
Detect objects or products in user’s living room photo
Recommend products based on purchase history and search requests
Understand product/brand/company sentiment on social media platforms
Capture customer sentiment during customer service calls
Extract text data from receipt images with printed content
Opzimize inventory levels across locations based off user demand, region, weather and past
purchase history
Determine what type of receipt it is (automated entry, manual entry)
…
Google Cloud NEXT / 10 March 2017
37. (1) "Nearly all new malware differs less than 2% from previous malware” Eli David, CTO Deep Instinct
In 2014, Kaspersky Lab was detecting 325,000 new malicious files every day (1)
43. MACHINE LEARNING
Computer Algorithms that have the ability to learn without being explicitly
programmed
MACHINE
LEARNING
ALGORITHM
Birds
Not Birds
Birds
Not Birds
52. Google Pictures
Show me all pictures of [Andrew] doing [Lessons]
Video
Vision API
Smart Reply in Inbox by Gmail
10% of all responses by mobile
Google Translate
Google Neural Machine Translation system (GNMT),
1952
Tic Tac Toe (Tris) (fun fact: Google tic tac toe giochi)
1997 (42 years later)
Deep Blue (IBM) beats Kasparov
2011 IBM Watson beats in Jeopardy (two Champs)
2016 Alpha Go beats Lee Sedol (Go World Champion)
2016, Fox chiede a IBM di far creare a Watson un trailer per il film Morgan, un Horror che parla di Intelligenze Artificiali
I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”
I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”
I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”
I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”
I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”
I don’t really know what these numbers mean but maybe you can figure out if there is a pattern or grouping or something — good luck!”
Supervised (we wish to infer the mapping implied by the data). We are given a set of example pairs and the aim is to find a function in the allowed class of functions that matches the examples.
Unsupervised: some data is give and the cost function to be minimized, that can be any function of the data and the networks’ output. Estimation problems, Clustering, Statistical distrib, Compression & Filtering
Reinf. Learning (Stochastic control): data x is not usually given, but generated by an agent’s interactions with the environment. At each point in time, the agent performs an action and the evnv. Generates an observation and an instantaneous cost. Accordin to some unknow dynamics. The aim is to discover a policy for selecting actions that minimizes some measure of a long-term cost.
As a human, you instantly recognize the hierarchy in this picture:
The ground is covered in grass and concrete
There is a child
The child is sitting on a bouncy horse
The bouncy horse is on top of the grass
MIT 3 algoritmi basati sul machine learning per verificare chi copia in università (chi copia codice sorgente nei progetti e compiti di informatica)
82 milioni di emendamenti (85) al DDL Boschi
sostituzioni di termini e punteggiatura che, pur mantenendo la struttura base di un emendamento, lo rendono diverso dagli altri 85 milioni.
Creano milioni di varianti, piccole o grandi, partendo da un numero contenuto di modelli base, di regole grammaticali, di sinonimi.
82 milioni di emendamenti (85) al DDL Boschi
Sostituzioni di termini e punteggiatura che, pur mantenendo la struttura base di un emendamento, lo rendono diverso dagli altri 85 milioni.
Creano milioni di varianti, piccole o grandi, partendo da un numero contenuto di modelli base, di regole grammaticali, di sinonimi.
Associated Press “scrive” dieci volte di più gli articoli. Ogni trimestre le aziende americane pubblicano I report finanziari. AP scrive articoli automaticamente grazie a software come ai
NLG = natural language generation
90% degli articoli online, entro 10 anni.
HOG: Histogram of Oriented Gradients
Smart Reply by INBOX (gmail)
https://static.googleusercontent.com/media/research.google.com/it//pubs/archive/45189.pdf
https://research.googleblog.com/2016/09/a-neural-network-for-machine.html
Smart Reply by INBOX (gmail)
https://static.googleusercontent.com/media/research.google.com/it//pubs/archive/45189.pdf
https://research.googleblog.com/2016/09/a-neural-network-for-machine.html
Eastern Bluebird = Sialia Sialis (Simbolo dello Stato di NY)
Uno dei pochi Turdidi del continente americano, simbolo diOttimismo e Felicità)
https://aiexperiments.withgoogle.com/bird-sounds
https://aiexperiments.withgoogle.com/bird-sounds
https://aiexperiments.withgoogle.com/bird-sounds
Their initial research looked at dance hits from 1985 through 2014, but I asked one of the study authors, Dorien Herremans, to run 2015 top Billboard dance singles through their prediction tool.
The algorithm predicted a 65 percent or higher probability of a hit for all of the top 10, and over 70 percent probability for 6 out of 10 songs. Not bad.
Offer real-time food personalization to your customers
Flavor Print sceglie ricette in base ai gusti culinari (basati su altre ricette predette) http://vivanda.com/
IBM Chef Watson
http://mx3d.com/
Supervised (we wish to infer the mapping implied by the data). We are given a set of example pairs and the aim is to find a function in the allowed class of functions that matches the examples.
Unsupervised: some data is give and the cost function to be minimized, that can be any function of the data and the networks’ output. Estimation problems, Clustering, Statistical distrib, Compression & Filtering
Reinf. Learning (Stochastic control): data x is not usually given, but generated by an agent’s interactions with the environment. At each point in time, the agent performs an action and the evnv. Generates an observation and an instantaneous cost. Accordin to some unknow dynamics. The aim is to discover a policy for selecting actions that minimizes some measure of a long-term cost.