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Just watched the 30-minute DeepLearning.AI course by Andrew Ng — and I am really impressed.What stood out most was the way prompt engineering was explained into the
Just watched the 30-minute DeepLearning.AI course by Andrew Ng — and I am really impressed.What stood out most was the way prompt engineering was explained into the simple building blocks here.- Goal.- Inputs.- Layout.- Features.- Output.Simple building blocks. Powerful outcomes.The biggest takeaway for me?Specificity drives predictability ...
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4 months ago
Andrew Ng Machine Learning Course
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Introduction to Machine Learning | ML-005 Lecture 1 | Stanford University | Andrew Ng
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Stanford CS229: Machine Learning Course, Lecture 1 - Andrew Ng (Autumn 2018)
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Andrew Ng s Machine Learning Course Fundamentals | Avichal Sharma posted on the topic | LinkedIn
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4 months ago
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Master Deep Learning Fundamentals with Andrew Ng on Coursera
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Andrew Ng: Deep Learning, Education, and Real-World AI | Lex Fridman Podcast #73 | SARKER SUNZID MAHMUD
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Andrew Ng on why openness matters, especially now, as more people build software through vibe coding and AI-assisted development. At AI Dev, we bring together people building, learning, and contributing to real AI systems, regardless of background. We’ll continue these conversations at AI Dev 26 × San Francisco, April 28–29. Get your tickets now: https://ow.ly/URYM50Y5rn0 | DeepLearning.AI
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Is EU losing the AI race? Andrew Ng explains why you can't lead the AI revolution by trying to slow it down. A sharp critique of why heavy-handed regulation like the EU AI Act stifles the very innovation it aims to control. At AI Dev, we explore the real-world impact of AI policy, building, and deployment. We’ll continue these conversations at AI Dev 26 × San Francisco, April 28–29. Tickets available: https://hubs.la/Q04344C60 | DeepLearning.AI
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Building a machine learning model isn’t just about finding the right algorithm—it’s about the right process. In Machine Learning in Production, Andrew Ng breaks down the iterative loop of model development: training, error analysis, refining hyperparameters, and improving data. Getting to a high test set accuracy is one thing, but aligning your model with real-world business metrics? That’s where the real challenge begins. Learn how to bridge the gap between models and impact: https://hubs.la/Q0
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“A lot of what looks like intelligence today was painfully engineered into the data.” Andrew Ng on why training recipes alone won’t get us to general intelligence. At AI Dev, we spend time on what comes after the demos: how systems are designed, evaluated, and improved in practice. We’ll continue these conversations at AI Dev 26 × San Francisco, April 28–29. Get your tickets today: https://hubs.la/Q040336n0 | DeepLearning.AI
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