I am running these deep learning models on Google Colab because my local machine doesn't have the GPU power to train them efficiently. This repository documents my work in handling resource-heavy tasks using cloud environments.
Key Highlight: Every notebook here includes an Interactive Loop at the end. You don't just read the code—you can chat with the models or test them with your own inputs.
Note: Users are requested to download and use the datasets given or own, if needed for the model to be trained in your runtime environment.
| Task | Description | Model Used | Link |
|---|---|---|---|
| Task 01 | Salary & Health Predictor Predicting salaries based on experience and diagnosing heart disease risk. |
Linear & Logistic Regression | Open Notebook |
| Task 02 | Customer Segmentation Grouping mall customers into 5 distinct categories based on spending habits. |
K-Means Clustering | Open Notebook |
| Task 03 | Visual & Text Analysis Classifying fashion items (Images) and categorizing news headlines (Text). |
CNN, RNN, & GRU | Open Notebook |
| Task 04 | Sequence Memory Analyzing movie sentiment using long-term memory networks. Image Compression Compressing images to their core features and reconstructing them. |
LSTM, Autoencoder, & ViT | Open Notebook |
| Task 05 | Generative Art Creating new handwritten digits from random noise. |
GAN (Generative Adversarial Network) | Open Notebook |
| Task 06 | Sentiment Analysis AI Simple analysis of emotion using transformers pipeline |
Pipelining using transformers | Open Notebook |
- Open any notebook link above.
- Click the "Open in Colab" badge.
- Go to Runtime > Run All.
- Scroll to the bottom to find the Interactive Input Box (that's the fun part!).
Created by Hanish D