How do we define the fully connected layers of a neural network in PyTorch?
Sunday, 13 August 2023
by EITCA Academy
The fully connected layers, also known as dense layers, are an essential component of a neural network in PyTorch. These layers play a crucial role in the process of learning and making predictions. In this answer, we will define the fully connected layers and explain their significance in the context of building neural networks. A
- Published in Artificial Intelligence, EITC/AI/DLPP Deep Learning with Python and PyTorch, Neural network, Building neural network, Examination review
Tagged under:
Artificial Intelligence, Dense Layers, Fully Connected Layers, Neural Network, PyTorch
What is the role of fully connected layers in a CNN and how are they implemented in TensorFlow?
Tuesday, 08 August 2023
by EITCA Academy
The role of fully connected layers in a Convolutional Neural Network (CNN) is crucial for learning complex patterns and making predictions based on the extracted features. These layers are responsible for capturing high-level representations of the input data and mapping them to the corresponding output classes or categories. In TensorFlow, fully connected layers are implemented