How do you speed up deep neural network training and improve its performance simultaneously? Batch Normalization is the answer. By addressing internal covariate shift, it allows models to train faster, requiring fewer steps and lower learning rates. In this episode, we break down how this technique was applied to a state-of-the-art image classification model, cutting training time by 14 times and surpassing human-level accuracy on ImageNet. Tune in to learn how Batch Normalization is transforming deep learning and setting new benchmarks in AI research.

Link to research paper- 

https://arxiv.org/abs/1502.03167

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