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Title: EP 11 | CS21SI: Deep Learning for social good
We’re moving from basic ML into the technical engine of modern AI: Deep Learning. In this week of Stanford’s CS21SI, we explore how neural networks can model complex, non-linear realities—specifically applying them to the escalating global wildfire crisis.
Key Topics:
The Neural Analogy: Understanding neurons, forward/backward passes, and backpropagation through the lens of social impact.
Wildfire Case Study: Using DL for spread prediction, forecasting, and the ethical dilemma of optimizing resource allocation for first responders.
The "Hidden Labor": A critical look at the human cost of AI—from data labeling "sweatshops" to the exploitation of incarcerated firefighters.
Environmental Footprint: Discussing the water and carbon cost of training massive deep learning models.
Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS21SI materials and personal study notes.
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By Jack LakkapragadaWe’re moving from basic ML into the technical engine of modern AI: Deep Learning. In this week of Stanford’s CS21SI, we explore how neural networks can model complex, non-linear realities—specifically applying them to the escalating global wildfire crisis.
Key Topics:
The Neural Analogy: Understanding neurons, forward/backward passes, and backpropagation through the lens of social impact.
Wildfire Case Study: Using DL for spread prediction, forecasting, and the ethical dilemma of optimizing resource allocation for first responders.
The "Hidden Labor": A critical look at the human cost of AI—from data labeling "sweatshops" to the exploitation of incarcerated firefighters.
Environmental Footprint: Discussing the water and carbon cost of training massive deep learning models.
Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS21SI materials and personal study notes.