Stanford CS229: Machine Learning Course, Lecture 1 - Andrew Ng (Autumn 2018)
Stanford CS229: Machine Learning Course, Lecture 1 - Andrew Ng (A…
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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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As machine learning models grow in size, so too does their carbon foot…
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Learn to deploy AI models to edge devices in our new short course "Introduction to On-Device AI," created with Qualcomm and taught by Senior Director of Engineering Krishna Sridhar. On-device (edge) AI is an important technology trend that's enabling new low latency, privacy-preserving applications. In this course, you'll deploy a real-time image segmentation model on-device, and through this learn key steps for on-device deployment: Neural Network graph capture, on-device compilation, hardware
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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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New Course: Reinforcement Fine-Tuning LLMs with GRPO! Learn to use reinforcement learning to improve your LLM performance in this short course, built in collaboration with Predibase, and taught by Travis Addair, its Co-Founder and CTO, and Arnav Garg, its Senior Engineer and Machine Learning Lead. Reasoning models have been one of the most important developments in LLMs. Reinforcement Fine-Tuning (RFT) uses rewards to encourage LLMs to find solutions to multi-step reasoning tasks such as solving
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