Building Nadam Optimizer from Scratch in Python

Building Nadam Optimizer from Scratch in Python

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Optimizing neural network weights is a crucial step in deep learning. Nadam, a variant of Adam optimizer, is a popular choice due to its excellent performance and convergence speed. In this video, we'll dive into the working of Nadam optimizer and build it from scratch in Python. We'll explore the underlying math and implement the algorithm using NumPy. By the end of this video, you'll have a solid understanding of the Nadam optimizer and how to use it in your own projects.

Nadam optimizer is a type of stochastic gradient descent algorithm that adapts learning rates for each parameter based on the magnitude of the gradient. This allows it to converge faster and more accurately than traditional Adam.

Here, we'll implement the Nadam optimizer using NumPy, exploring the concept of adaptive learning rates and how it improves the convergence of the algorithm.

This video is perfect for anyone looking to understand the fundamental concepts of optimization in deep learning and how to implement them from scratch.

Suggested additional reading:

* Adam optimizer paper by Kingma and Ba (2014)
* Understanding stochastic gradient descent and its variants (Coursera course)

#stem #machinelearning #artificialintelligence #python #nadamoptimizer #deeplearning #optimization

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