Stanford CS229: Machine Learning Course, Lecture 1 - Andrew Ng (Autumn 2018)
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Stanford CS229: Machine Learning Course, Lecture 1 - Andrew Ng (Autumn 2018)
If you could only watch 1 lectures to learn how AI really works, make it this one. And it’s FREE. Stanford’s CS229 by Andrew Ng.
2 months ago
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Andrew Ng X-Rays the AI Hype
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If you’ve never written code before, this is for you. I’ve just launched a course that shows you, in less than 30 minutes, how to describe an idea for an app and build it with AI. In this course, you'll build a working web application - a funny interactive birthday message generator that runs in your browser and can be shared with friends. You'll customize it by telling AI how you want it changed, and tweak it until it works the way you want. By the end, you'll have a repeatable process you can
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If you’ve never written code before, this is for you. I’ve just launched a course that shows you, in less than 30 minutes, how to describe an idea for an app and build it with AI. In this course, you'll build a working web application - a funny interactive birthday message generator that runs in your browser and can be shared with friends. You'll customize it by telling AI how you want it changed, and tweak it until it works the way you want. By the end, you'll have a repeatable process you can
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ieee.org
As machine learning models grow in size, so too does their carbon footprint. As AI scales, it's important that we quantify and mitigate these emissions. In our new short course "Carbon Aware Computing for GenAI Developers" you'll learn from Google Cloud Developer Advocate Nikita Namjoshi how to: - Query the ElectricityMaps API for real-time data on regional electricity grid carbon intensity - Route your model training jobs to data centers in regions primarily powered by low-carbon sources like w
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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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