Categories: AI/ML Research

Loss Functions in PyTorch Models

The loss metric is very important for neural networks. As all machine learning models are one optimization problem or another, the loss is the objective function to minimize. In neural networks, the optimization is done with gradient descent and backpropagation. But what are loss functions, and how are they affecting your neural networks? In this […]

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Activation Functions in PyTorch

Last Updated on May 3, 2023 As neural networks become increasingly popular in the field of machine learning, it is important to understand the role that activation functions play in their implementation. In this article, you’ll explore the concept of activation functions that are applied to the output of each…

May 3, 2023

In "AI/ML Research"

How to Grid Search Hyperparameters for PyTorch Models

The “weights” of a neural network is referred as “parameters” in PyTorch code and it is fine-tuned by optimizer during training. On the contrary, hyperparameters are the parameters of a neural network that is fixed by design and not tuned by training. Examples are the number of hidden layers and…

February 10, 2023

In "AI/ML Research"

Using Learning Rate Schedule in PyTorch Training

Training a neural network or large deep learning model is a difficult optimization task. The classical algorithm to train neural networks is called stochastic gradient descent. It has been well established that you can achieve increased performance and faster training on some problems by using a learning rate that changes…

February 22, 2023

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