Deep Learning - Artificial Neural Networks with Tensorflow - Adam Optimization (Part 1)

The video tutorial introduces Adaptive Moment Estimation (ATOM), a popular optimization technique for neural networks, developed as a successor to RMS Prop. It explains how ATOM combines momentum and adaptive learning rates, making it robust and effective with default settings. The tutorial also covers methods to improve gradient descent, the concept of moving averages, and the significance of exponentially weighted moving averages. Finally, it discusses the use of moments in RMS Prop and how ATOM integrates these concepts.

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