This repository contains various deep learning projects, demonstrating the application of different algorithms and techniques on real-world datasets.

This project uses the EfficientNetV2 model to classify images into two categories: dogs and cats. The model is trained on a dataset of dog and cat images and aims to accurately predict the animal present in a given image.

This project extends the previous one by incorporating Principal Component Analysis (PCA) to reduce the dimensionality of the data. This can potentially improve the model’s performance by focusing on the most important features of the images.

This project uses a Deep Neural Network (DNN) to perform sentiment analysis on text data. The goal is to determine whether the sentiment of a given text is positive, negative, or neutral.

This project uses a Convolutional Neural Network (CNN) to classify images of butterflies. The model is trained on a dataset of butterfly images and aims to accurately classify the species of a butterfly from a given image.

pandas: 1.5.3 numpy: 1.24.3 matplotlib: 3.7.1 seaborn: 0.12.2 plotly: 5.9.0 scipy: 1.10.1 scikit-learn: 1.3.0 tensorflow

1. Clone this repository.
2. Open with google colab or jupyter notebook

Each project directory contains a README file with specific instructions on how to run the project.

Feel free to explore these projects and provide your valuable feedback or queries if any.