Although deep neural networks have shown high accuracy in fields like computer vision, natural language processing, and medicine, they often behave like black boxes. Avesh Singh and Kevin Wu explain the intuitions that went into building DeepHeart, a DNN that detects cardiovascular disease from heart rate data. Next, they explain techniques to debug DNNs, including visualizing activations and training on synthetic data. Avesh and Kevin also analyze gradients for recurrent neural networks, looking for ways to quantify the amount of information loss a network may be experiencing over time. You’ll leave this talk with a greater understanding of how to design and debug a deep neural network for your machine learning task.
Avesh Singh is an engineer at Cardiogram, a startup that applies deep learning to wearable data. Previously, Avesh worked at Nest Labs and Google. He holds a a BS and MS in computer science from Carnegie Mellon University.
Kevin Wu is an engineer at Cardiogram, a startup that applies deep learning to wearable data. Previously, Kevin worked at Two Sigma and Credit Suisse. He holds a BS and MS in computer science from Stanford University.
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