Machine Learning allows computers to learn patterns from data rather than relying on fixed rules or models. Deep learning neural networks achieve this by breaking knowledge into simple neurons and steps, layer by layer. According to the Universal Approximation Theorem, a neural network can approximate almost any function—meaning nearly any knowledge or model can be learned from data. With this simple idea, Machine Learning becomes easy to grasp—truly ML for everyone.Event: TEDxCSTU
Date: May 18th, 2025
Location: California Science & Technology University (CSTU)
Website: https://www.cstu.edu?utm_source=youtube&utm_medium=social&utm_campaign=tedx2025&utm_term=glen_qinSpeaker: Glen Qin
Learn more about the speaker: https://www.linkedin.com/in/glen-qin-9275a12/Organizer: Leni Jin, Organizer of TEDxCSTU
Behind the scenes of TEDxCSTU:
Stories & notes: https://lenijin.com/tedx
More about the organizer: https://www.linkedin.com/in/lenijin/
Organizer's channel: https://www.youtube.com/@Leni_JinPlease note: This talk was recorded at TEDxCSTU, an independently organized TEDx event hosted at California Science and Technology University under a license from TED. The views and opinions expressed are solely those of the speaker and do not necessarily represent those of TEDxCSTU, the university, or TED.#TEDx #TEDxTalk #MachineLearning #DeepLearning #AIForEveryone #DataScience #FutureOfAI #NeuralNetworks #TechEducation #AIRevolution #MLForEveryone #TEDxCSTU2025
Dr. Glen Qin, President of California Science and Technology University, is an esteemed educator, engineer, and technical leader in Silicon Valley. Holding a PhD from UC Berkeley, Dr. Qin is an expert in AI and Data Science. As a visionary leader, he has been guiding CSTU since its inception, driving the university's success. His leadership has not only shaped CSTU’s achievements but has also inspired many graduates to become high achievers in their own right. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
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