Welcome to the AI Concepts Podcast, where host Shay unravels the intricate world of AI through relatable examples and easy-to-understand analogies. In this episode, we continue our dive into deep learning by addressing the challenges and solutions of gradient descent. Learn how traditional gradient descent, which is pivotal in neural network training, sometimes falls short due to its slow speed and susceptibility to getting stuck.
Explore enhancements like Stochastic Gradient Descent, which speeds up the process by using random data subsets, and discover the power of momentum in overcoming noisy gradients. Dive into Adagrad, the adaptive learning rate optimizer that adjusts itself based on parameter updates, ensuring efficient learning even with sparse data. However, watch out for Adagrad's tendency to become overly cautious over time.
Get ready for an insightful discussion as we lay the groundwork for future episodes focusing on advanced optimizers like RMSprop and Adam, along with the crucial art of hyperparameter tuning.
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