# Recurrent-Neural-Networks

**Repository Path**: davidgao7/Recurrent-Neural-Networks

## Basic Information

- **Project Name**: Recurrent-Neural-Networks
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-02-11
- **Last Updated**: 2022-02-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Recurrent Neural Networks

If github in unable to render a Jupyter notebook, copy the link of the notebook and enter into the nbviewer: https://nbviewer.jupyter.org/

These notebooks provide an introduction to the **Recurrent Neural Networks (RNNs)**.


- Notebook 1: Motivation, RNN architecture, Signal propagation in RNNs, RNN architectures

- Notebook 2: RNN Training - Backpropagation through time (BPTT)

- Notebook 3: RNN Training issues - Vanishing & Exploding gradient problem, Preventing the problem: activation, layer normalization, gradient clipping

- Notebook 4: Gated RNN Cells, GRU, LSTM - Effective strategy to resolve the vanishing gradient problem in RNNs

- Notebook 5: Building effective RNNs, Increase The Representational Power of RNNs, Preventing Overfitting in RNNs

The notebooks are partially adapted from 
- Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd Edition) by Aurélien Géron
- Dive into Deep Learning by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola