GitHub topics: exploding-gradient

Repositories

Hasnat-Aarif-Aslam/How-to-Improve-the-Performance-of-a-Neural-Network

This pdf shows some methods to fine tune the hyperparameters of a Neural Network to increase it's performance. It also puts light on some of the common problems related to Neural Networks along with their solutions.

Language: Jupyter Notebook - Size: 25.2 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 1 - Forks: 0

EmamulHossen/Gradient-Problem-in-RNN

The vanishing gradient problem is a well-known issue in training recurrent neural networks (RNNs). It occurs when gradients (derivatives of the loss with respect to the network's parameters) become too small as they are backpropagated through the network during training.

Size: 2.18 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

hwixley/MLP-coursework2-report

Machine Learning Practical - Coursework 2 Report: Analysing problems with the VGG deep neural network architectures (with 8 and 38 hidden layers) on the CIFAR100 dataset by monitoring gradient flow during training. And exploring solutions using batch normalization and residual connections.

Language: TeX - Size: 741 KB - Last synced at: almost 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0

hwixley/MLP-coursework2

Machine Learning Practical - Coursework 2: Analysing problems with the VGG deep neural network architectures (with 8 and 38 hidden layers) on the CIFAR100 dataset by monitoring gradient flow during training. And exploring solutions using batch normalization and residual connections.

Language: Python - Size: 3.37 GB - Last synced at: almost 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0