Deep Learning - Artificial Neural Networks with Tensorflow - Adam Optimization (Part 2)

The lecture covers the concept of exponential moving averages, particularly in non-stationary data like stock prices, and introduces the low pass filter. It addresses the bias issue in low pass filters and explains bias correction, a method used in deep learning to adjust initial outputs. The lecture then details how to incorporate bias correction into the Adam optimization algorithm, including initializing parameters and performing gradient updates. It concludes with a discussion on hyperparameters and the robustness of Adam, while emphasizing the importance of experimentation in machine learning.

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