Gradient Descent

4 Posts

Gradient Descent: It’s All Downhill - Gradient Descent for Machine Learning

Imagine hiking in the mountains past dusk and finding that you can’t see much beyond your feet. And your phone’s battery died so you can’t use a GPS app to find your way home. You might find the quickest path down via gradient descent.

Graphs comparing SGD + Momentum, Adam and AdaBelief

Gradient Descent

Striding Toward the Minimum: A faster way to optimize the loss function for deep learning.

When you’re training a deep learning model, it can take days for an optimization algorithm to minimize the loss function. A new approach could save time.

Graphs related to a comparison and evaluation of 14 different optimizers

Gradient Descent

Optimizer Shootout: An evaluation of 14 deep learning optimizers

Everyone has a favorite optimization method, but it’s not always clear which one works best in a given situation. New research aims to establish a set of benchmarks. Researchers evaluated 14 popular optimizers using the Deep Optimization Benchmark Suite some of them introduced last year.

Series of images with graphs and data related to optimization algorithms

Gradient Descent

When Optimization is Suboptimal: How gradient descent can sometimes lead to model bias

Bias arises in machine learning when we fit an overly simple function to a more complex problem. A theoretical study shows that gradient descent itself may introduce such bias and render algorithms unable to fit data properly.

Gradient Descent

Gradient Descent: It’s All Downhill - Gradient Descent for Machine Learning

Striding Toward the Minimum: A faster way to optimize the loss function for deep learning.

Optimizer Shootout: An evaluation of 14 deep learning optimizers

When Optimization is Suboptimal: How gradient descent can sometimes lead to model bias

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