This repository does the same job as Deep Neural Network From Scratch i.e it will walk you through the process of creating a Deep Recurrent Neural Network without using any of the deep learning libraries like Tensorflow, Keras, Caffe etc.
The above repository shows how to implement the Recurrent Neural Network's forward propagation, back propagation from stratch.
The dinos.txt file contains the names of dinosaurs. This is the training data that the model is trained upon.
The model learns how to generate dinosaur's name given first few characters.
Backpropagation through time is implemented by adding the gradients across all the time steps.
The best practices involved in training a good model is not followed as it is not the purpose of this repository.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
The libraries required for the above project are : numpy(*Yes only Numpy :) *).
pip install numpy
The project above follows the following pattern while training the model:
- Load the data
- Initialize parameters
- Forward Propagation
- Compute Cost
- Backward Propagation
- Update Parameters
The file main.py is to be run to start the training.
- Inspired by Andrew Ng!
- Got the code support from deeplearning.ai fifth course.
In case of any doubts/confusions do shoot a mail at : ashish.agrawal2123@gmail.com
For more details visit : https://ashish230897.github.io/2018/08/10/Deep-NeuralNetworks-From-Scratch.html