Introduction
This repository contains the foundational concepts, discussions and models implemented using neural networks, recurrent (RNNs), Long short term memory (LSTMs) and convolutional neural networks (CNNs) with emphasis on deep learning architectures. The main objective is to explore the basics of RNNs, LSTMs and CNN with examples and identify key applications where these architectures have been applied to solve problems. The examples presented in this repository are introductory given the fact that a vast literature exist on the theory and practical applications of RNNs, LSTMs and CNNs.
Main Tech Stack
- Python and associated libraries (Numpy, Sklearn, Pandas etc)
- Tensorflow and Pytorch
- Opencv for computer vision examples
- Docker and kubernetes
Main definitions
We revisit classical neural networks and implement classification examples in python and tensorflow. The goal is to implement neural networks in text mining, machine translation, sequence-sequence language and similar examples. We discuss the RNNs, LSTMs and CNNs architectures which are most suitable to the aformentioned tasks. To this end, we transform and train examples of use cases where both RNNs and LSTMs are most applicable via supervised learning.
The training data used is downloaded from different data sources (publicly available data sets). We begin with training data which are available in the sklearn (scikit-learn) and nltk packages.
Contents of the repository
The repository consists of the following structure
- misc_files: Examples of text classification models implemented in Python and other ML frameworks.
- lstm_cnn_trainer
This folder contains LSTM and CNN examples implemented using Tensorflow, python, scikit-learn, text classification examples
To run, clone the repository using git clone <repo-name> and supply the data assuming the environment has been properly configured.
You will create two separate DB Session objects and use them knowingly in the code - whether same name or not, you will adapt the .env and the code accordingly
you can even plug the same cli on multiple Astra organization ...try astra config list
changing the code and providing different settings you can (you can create DB-specific and DB-generic tokens also)