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zakruti.com » Knowledge, science, education » Crash Course
Training Neural Networks: Crash Course AI #4

Training Neural Networks: Crash Course AI #4

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Rating: 4.0; Vote: 1
Today we-re going to talk about how neurons in a neural network learn by getting their math adjusted, called backpropagation, and how we can optimize networks by finding the best combinations of weights to minimize error. Then we-ll send John Green Bot into the metaphorical jungle to find where this error is the smallest, known as the global optimal solution, compared to just where it is relatively small, called local optimal solutions, and we'll discuss some strategies we can use to help neural networks find these optimized solutions more quickly. Crash Course is produced in association with PBS Digital Studios
Date: 2022-04-04

Comments and reviews: 10


say that the medications have available routes to take them IV, oral - the summaries of the assessment and implementations have parts only for those routes. Also say every route for that medicine has that medicines basic special instruction text, plus the amount of the ingredients in each med may have limits per day to compare with other meds that might contain that med so rules on amount per day. programmatically with AI cut down the text then have licensed staff review and authorizations to edit and then check for patient outcomes of reasons either stress alert fatigue of too much on had reference info basically why there were any errors and then put that back in to give a more precise summary for reference and again repeat the licensed staff reviews of that text for edits. Battle hardened and fast to generate but do this with supervised role play training to make up for the columns of data there is not information services for yet.
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Are the AI weights a sort of universal measure that are single weights for a given area of analysis? In other words, are the weights adjusted as singular weights and this then has relevance to any question that may be inputted in to the neural network? Or are there a multitude of weights for each of analysis (i. e.; an area such as weather) that make the neural network more fluid and open to been adjusted for specific questions rather than adjusted at each time for every possible question that may come its way? Or does it depend on the neural network in question?
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I had high hopes for this series, but tbh, Im very disappointed. way too diluted with John green it and poor storytelling analogies. the material is exciting enough. I will try and stick with it (no promises ) mostly because your computer science series was so fantastic
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Oh John Green Bot is Q'Bert Q is for quantum lets do it in Q# with Microsoft AI flow chart programming I do visual blocks better then raw math like signal R honestly I even better with 'Enhanced Intelligence' Ei Ei O using the Excel and peoples actual workflow but.
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As an AI scientist, I'm echoing the comments from others: this was an excellent explanation of the basics of neural network training, without digging into the complexities of gradient descent. Great job, can't wait for the lab!
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This assigning blame backpropagation thing reminds me how people work. Just add that some neurons are never to blame (they are bosses) and other neurons never change their behavior: boom, you have an average workplace.
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Hey, CrashCourse! Have you done anything yet on Propaganda and what it has done to the USA over the last several decades?
People need to be aware of what Rupert Murdoch has done to the UK, USA and Australia.

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I started my CS degree with the intention of going into game dev. But the BS in the game industry, carykh, and this are making me reconsider refocusing on AI. Thanks Jabril and Crash Course!
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reminded of that lasers shot through a pinpoint hole for precision groupings effected by thought intent and true nonrandom interference true fringe science experiment
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This isn't CrachCourse: Artificial intelligence, this isn't even CrashCourse: Machine learning, this is CrashCourse: Neural networks.
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