Deep Learning Crash Course for Beginners
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Date: 2022-03-14
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Comments and reviews: 6
Laurence
I found the fake news detection statement amusing. This was clearly before we realised that every side of an issue has fake news (and some truths are just not available on any network because the data regarding them is deleted). If created today I would suggest the software would be used to determine the truth according to the clients parameters and specifications. Additionally the software would need to be directed to what are 'reliable sources' or otherwise risk verifying undesirable information. Sorry for overly discussing this issue. This video was very interesting, thank you.
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I found the fake news detection statement amusing. This was clearly before we realised that every side of an issue has fake news (and some truths are just not available on any network because the data regarding them is deleted). If created today I would suggest the software would be used to determine the truth according to the clients parameters and specifications. Additionally the software would need to be directed to what are 'reliable sources' or otherwise risk verifying undesirable information. Sorry for overly discussing this issue. This video was very interesting, thank you.
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Academiclibrary
Learn the fundamental concepts and terminology of Deep Learning, a sub-branch of Machine Learning. This course is designed for absolute beginners with no experience in programming. You will learn the key ideas behind deep learning without any code.
You'll learn about Neural Networks, Machine Learning constructs like Supervised, Unsupervised and Reinforcement Learning, the various types of Neural Network architectures, and more.
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Learn the fundamental concepts and terminology of Deep Learning, a sub-branch of Machine Learning. This course is designed for absolute beginners with no experience in programming. You will learn the key ideas behind deep learning without any code.
You'll learn about Neural Networks, Machine Learning constructs like Supervised, Unsupervised and Reinforcement Learning, the various types of Neural Network architectures, and more.
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Murrowboy
The output shouldn't be right or wrong, it should be a variable no? Because not all input we receive is binary. If anything it should be on a scale of 1-10, 1 being very negative (lots of adjustments to neurons) and 10 being perfect and should increase the weight in the future.
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The output shouldn't be right or wrong, it should be a variable no? Because not all input we receive is binary. If anything it should be on a scale of 1-10, 1 being very negative (lots of adjustments to neurons) and 10 being perfect and should increase the weight in the future.
reply
Monroe
LOL, it's funny that Jessica also started commenting on random videos as wellm and now she's raking $650 per week, lol kinda makes me mad since I was the one who told her to go'ogle the latest by Tanashie McStack and she's making more than me!
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LOL, it's funny that Jessica also started commenting on random videos as wellm and now she's raking $650 per week, lol kinda makes me mad since I was the one who told her to go'ogle the latest by Tanashie McStack and she's making more than me!
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B-lint
I watched a lots of deep learning tuturials before this, some of them were even twice as long as this and yet this explained the best and the most. Thank you for the awesome tutorial without ads for free. You are a hero.
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I watched a lots of deep learning tuturials before this, some of them were even twice as long as this and yet this explained the best and the most. Thank you for the awesome tutorial without ads for free. You are a hero.
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DayumAli
-This function is therefore perfect to use for this context! Or so you would think.- -proceeds to explain trade-off and subsequently destroy my dreams-
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-This function is therefore perfect to use for this context! Or so you would think.- -proceeds to explain trade-off and subsequently destroy my dreams-
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