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Artificial Intelligence
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Description
The TV series Artificial Intelligence, created by an unlisted creator and produced by an unlisted studio, first premiered on May 12, 2026 in Royaume-Uni. The series spans 1 season with 21 episodes, featuring the voices of an unlisted voice cast. It follows the story of In 20 episodes, Jabril will teach you about Artificial Intelligence and Machine Learning! This course is based on a university-level curriculum. By the end of the course, you will be able to: * Define, differentiate, and provide examples of Artificial Intelligence and three types of Machine Learning: supervised, unsupervised, and reinforcement * Understand how different AI and ML approaches can be combined to create compelling applications such as natural language processing, robotics, recommender systems, and web search * Implement several types of AI to classify images, generate text from examples, play video games, and recommend content based on past preferences * Understand the causes of algorithmic bias and audit datasets for several of these causes * Reason about how specific advances in AI may impact our world and your life, for better or for worse. This is an Ai series and has received a rating of 0/10 from 0 viewers.
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Production
Castle Rock Entertainment
Récompense
21 victoires et 43 nominations au total
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Avis des utilisateurs
Follow along: https://colab.research.google.com/drive/1-v9cw18wTDjaCUlECKHsQnHeisLKyG8U We need to save Jabril and John Green Bot’s movie nights. Jabril generally likes action movies and John Green Bot likes romantic movies, but they need to find something that they can both watch and enjoy together. Today, we’re going to build a movie recommender systems to find that perfect movie. With the help of the LensKit library, our AI will use existing movie ratings from the MovieLens dataset and personalized ratings from Jabril and John Green Bot to perform user-user collaborative filtering. We’ll then create a Jabril Green Bot hybrid that will average these ratings to try and find something that they both want to watch. Lenskit documentation: https://java.lenskit.org/documentation/ Our GitHub for this lab: https://github.com/crash-course-ai/lab4-recommender-systems Crash Course is produced in association with PBS Digital Studios: https://www.youtube.com/pbsdigitalstudios Crash Course
Follow along: https://colab.research.google.com/drive/1-v9cw18wTDjaCUlECKHsQnHeisLKyG8U We need to save Jabril and John Green Bot’s movie nights. Jabril generally likes action movies and John Green Bot likes romantic movies, but they need to find something that they can both watch and enjoy together. Today, we’re going to build a movie recommender systems to find that perfect movie. With the help of the LensKit library, our AI will use existing movie ratings from the MovieLens dataset and personalized ratings from Jabril and John Green Bot to perform user-user collaborative filtering. We’ll then create a Jabril Green Bot hybrid that will average these ratings to try and find something that they both want to watch. Lenskit documentation: https://java.lenskit.org/documentation/ Our GitHub for this lab: https://github.com/crash-course-ai/lab4-recommender-systems Crash Course is produced in association with PBS Digital Studios: https://www.youtube.com/pbsdigitalstudios Crash Course
Follow along: https://colab.research.google.com/drive/1-v9cw18wTDjaCUlECKHsQnHeisLKyG8U We need to save Jabril and John Green Bot’s movie nights. Jabril generally likes action movies and John Green Bot likes romantic movies, but they need to find something that they can both watch and enjoy together. Today, we’re going to build a movie recommender systems to find that perfect movie. With the help of the LensKit library, our AI will use existing movie ratings from the MovieLens dataset and personalized ratings from Jabril and John Green Bot to perform user-user collaborative filtering. We’ll then create a Jabril Green Bot hybrid that will average these ratings to try and find something that they both want to watch. Lenskit documentation: https://java.lenskit.org/documentation/ Our GitHub for this lab: https://github.com/crash-course-ai/lab4-recommender-systems Crash Course is produced in association with PBS Digital Studios: https://www.youtube.com/pbsdigitalstudios Crash Course
Today, in our final episode of Crash Course AI, we're going to look towards the future. We've spent much of this series explaining how and why we don't have the Artificial General Intelligence (or AGI) that we see in the movies like Bladerunner, Her, or Ex Machina. Siri frequently doesn't understand us, we probably shouldn't sleep in our self-driving cars, and those recommended videos on YouTube and Netflix often aren't what we really want to watch next. So let's talk about what we do know, how we got here, and where we think it's all headed. Thanks so much everyone for watching! Don't forget to subscribe to Jabril’s channel here! http://youtube.com/c/jabrils And you can find some more free recourses to learn about AI below! https://course.fast.ai/ https://www.coursera.org/learn/ai-for-everyone https://www.coursera.org/learn/machine-learning https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html https://www.kaggle.com/learn/overview https://www.kaggle.com/competitions?
Today, in our final episode of Crash Course AI, we're going to look towards the future. We've spent much of this series explaining how and why we don't have the Artificial General Intelligence (or AGI) that we see in the movies like Bladerunner, Her, or Ex Machina. Siri frequently doesn't understand us, we probably shouldn't sleep in our self-driving cars, and those recommended videos on YouTube and Netflix often aren't what we really want to watch next. So let's talk about what we do know, how we got here, and where we think it's all headed. Thanks so much everyone for watching! Don't forget to subscribe to Jabril’s channel here! http://youtube.com/c/jabrils And you can find some more free recourses to learn about AI below! https://course.fast.ai/ https://www.coursera.org/learn/ai-for-everyone https://www.coursera.org/learn/machine-learning https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html https://www.kaggle.com/learn/overview https://www.kaggle.com/competitions?
Today, in our final episode of Crash Course AI, we're going to look towards the future. We've spent much of this series explaining how and why we don't have the Artificial General Intelligence (or AGI) that we see in the movies like Bladerunner, Her, or Ex Machina. Siri frequently doesn't understand us, we probably shouldn't sleep in our self-driving cars, and those recommended videos on YouTube and Netflix often aren't what we really want to watch next. So let's talk about what we do know, how we got here, and where we think it's all headed. Thanks so much everyone for watching! Don't forget to subscribe to Jabril’s channel here! http://youtube.com/c/jabrils And you can find some more free recourses to learn about AI below! https://course.fast.ai/ https://www.coursera.org/learn/ai-for-everyone https://www.coursera.org/learn/machine-learning https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html https://www.kaggle.com/learn/overview https://www.kaggle.com/competitions?
Today, in our final episode of Crash Course AI, we're going to look towards the future. We've spent much of this series explaining how and why we don't have the Artificial General Intelligence (or AGI) that we see in the movies like Bladerunner, Her, or Ex Machina. Siri frequently doesn't understand us, we probably shouldn't sleep in our self-driving cars, and those recommended videos on YouTube and Netflix often aren't what we really want to watch next. So let's talk about what we do know, how we got here, and where we think it's all headed. Thanks so much everyone for watching! Don't forget to subscribe to Jabril’s channel here! http://youtube.com/c/jabrils And you can find some more free recourses to learn about AI below! https://course.fast.ai/ https://www.coursera.org/learn/ai-for-everyone https://www.coursera.org/learn/machine-learning https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html https://www.kaggle.com/learn/overview https://www.kaggle.com/competitions?
Follow along: https://colab.research.google.com/drive/1N5IdMTmiNbwEOD8dqammN8GAfpk41arw Today, in our final lab, Jabril tries to make an AI settle the question once and for all, "Will a cat or a dog make us happier?" But in building this AI, Jabril will accidentally incorporate the very bias he was trying to avoid. So today we'll talk about how bias creeps into our algorithms and what we can do to try to account for these problems. Crash Course is produced in association with PBS Digital Studios: https://www.youtube.com/pbsdigitalstudios Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Eric Prestemon, Sam Buck, Mark Brouwer, Efrain R. Pedroza, Matthew Curls, Indika Siriwardena, Avi Yashchin, Timothy J Kwist, Brian Thomas Gossett, Haixiang N/A Liu, Jonathan Zbikowski, Siobhan Sabino, Jennifer Kill
Follow along: https://colab.research.google.com/drive/1N5IdMTmiNbwEOD8dqammN8GAfpk41arw Today, in our final lab, Jabril tries to make an AI settle the question once and for all, "Will a cat or a dog make us happier?" But in building this AI, Jabril will accidentally incorporate the very bias he was trying to avoid. So today we'll talk about how bias creeps into our algorithms and what we can do to try to account for these problems. Crash Course is produced in association with PBS Digital Studios: https://www.youtube.com/pbsdigitalstudios Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Eric Prestemon, Sam Buck, Mark Brouwer, Efrain R. Pedroza, Matthew Curls, Indika Siriwardena, Avi Yashchin, Timothy J Kwist, Brian Thomas Gossett, Haixiang N/A Liu, Jonathan Zbikowski, Siobhan Sabino, Jennifer Kill
Follow along: https://colab.research.google.com/drive/1N5IdMTmiNbwEOD8dqammN8GAfpk41arw Today, in our final lab, Jabril tries to make an AI settle the question once and for all, "Will a cat or a dog make us happier?" But in building this AI, Jabril will accidentally incorporate the very bias he was trying to avoid. So today we'll talk about how bias creeps into our algorithms and what we can do to try to account for these problems. Crash Course is produced in association with PBS Digital Studios: https://www.youtube.com/pbsdigitalstudios Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Eric Prestemon, Sam Buck, Mark Brouwer, Efrain R. Pedroza, Matthew Curls, Indika Siriwardena, Avi Yashchin, Timothy J Kwist, Brian Thomas Gossett, Haixiang N/A Liu, Jonathan Zbikowski, Siobhan Sabino, Jennifer Kill
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