gradient descent

Jay McClelland: Neural Networks and the Emergence of Cognition | Lex Fridman Podcast #222

Lex Fridman

An optimization concept familiar to engineers, where connection weights are adjusted to minimize error or 'loss' in a system, introduced by Jeff Hinton as a way to approach neural network learning.

Juergen Schmidhuber: Godel Machines, Meta-Learning, and LSTMs | Lex Fridman Podcast #11

Lex Fridman

An optimization algorithm used to find the minimum of a function, commonly employed in training neural networks. While not provably optimal like methods inspired by Gödel machines, it's more practical for current AI tasks.

Andrew Ng: Deep Learning, Education, and Real-World AI | Lex Fridman Podcast #73

Lex Fridman

A fundamental optimization algorithm in machine learning, which is covered in the Machine Learning course and briefly touched upon in the Deep Learning Specialization.

Andrew Ng: Advice on Getting Started in Deep Learning | AI Podcast Clips

Lex Fridman

An optimization algorithm discussed as a concept covered in the machine learning course, which is prerequisite knowledge for the deep learning specialization.

PyTorch Crash Course - Getting Started with Deep Learning

AssemblyAI

An iterative optimization algorithm used to find the minimum of a function by taking steps proportional to the negative of the gradient of the function at the current point.

François Chollet: How We Get To AGI

Y Combinator

Theano Tutorial (Pascal Lamblin, MILA)

Lex Fridman

An optimization algorithm used in machine learning, for which Theano can compute update expressions.

Torch Tutorial (Alex Wiltschko, Twitter)

Lex Fridman

An optimization algorithm used to find the minimum of a function, fundamental to training machine learning models.

TensorFlow Tutorial (Sherry Moore, Google Brain)

Lex Fridman

An optimizer used in neural networks to minimize the loss function, utilized in the linear regression and MNIST labs.