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

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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

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Using Dropout Regularization in PyTorch Models

Dropout is a simple and powerful regularization technique for neural networks and deep learning models. In this post, you will discover the Dropout regularization technique and how to apply it to your models in PyTorch models. After reading this post, you will know: How the Dropout regularization technique works How…

February 20, 2023

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