Using high-level frameworks like Keras, TensorFlow or PyTorch allows us to build very complex models quickly. However, it is worth taking the time to look inside and understand underlying concepts. Not so long ago I published an article, explaining — in a simple way — how neural nets work. However, it was highly theoretical post, dedicated primarily to math, which is the source of NN superpower. From the beginning I was planning to follow-up this topic in a more practical way. This time we will try to utilize our knowledge and build a fully operational neural network using only NumPy. Finally, we will also test our model — solve simple classification problem and compare its performance with NN built with Keras.
Here's why:
- Building a nn from scratch helps in understanding how nn works in the back-end and it is essential for building effective models.
This was build using following frameworks, libraries and softwares.
To run this project you need to follow the following steps.
To run this project you need to follow the following steps.
These are the prerequisites you need to build this bot as well as run it.
cmd:\ pip install tensorflow
cmd:\ pip install keras- Create conda environment and create project in this environment
- After installing requirements in above Modules LIST
- You need python idle such as Jupyter notebook or spyder
See the open issues for a list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
- MIT license
- Copyright 2020 © Aditya Mangla.
Aditya Mangla - @aadimangla - aadimangla@gmail.com - adityamangla.com
Project Link: https://github.com/aadimangla/Custom-Trained-Recurrent-Neural-Network