Link Examples
It is recommended to use after updating to the latest version(0.11.0).
Spiral Pattern Classification
- Classification of 2D spiral-distributed data using Pytorch framework (github)
- spiral_classification.ipynb
Iris Data EDA and Modelling
- Basic exploratory data analysis (EDA) and modelling of iris data using Scikit-Learn library (github)
- iris_data_eda_and_modeling.ipynb
Titanic Data EDA and Modelling
- Various EDA and ensemble modelling of titanic data using Scikit-Learn library (github)
- titanic_data_eda_and_modeling.ipynb
Image Generation using Variational Autoencoder
- MNIST image generation test with variational autoencoder (VAE) using Pytorch framework (github)
- image_generation_using_variational_autoencoder.ipynb
Image Restoration using Denoising Autoencoder
- Restoring corrupted MNIST images with denoising autoencoder using Pytorch framework (github)
- image_restoration_using_denoising_autoencoder.ipynb
MNIST Image Classification using CNN
- Classification of MNIST images with convolutional neural network (CNN) and fully-connected network (FCN) being compared using Pytorch framework (github)
- mnist_image_classification_using_cnn.ipynb
Using XGBoost for scikit-learn datasets
- Modelling of diabetes data for regression, classificaion, cross-validation, and hyperparameter searching using Scikit-Learn and XGBoost library (github)
- using-xgboost-for-scikit-learn-datasets.ipynb
Text Data Classification using RNN
- Text classification with RNN using Pytorch library (github)
- text_data_classification_using_rrn.ipynb
Deep Q-Network Reinforcement Learning for CartPole Environment
- βDeep Q-Network Reinforcement Learning (RL) for CartPole environment using Pytorch and OpenAI-Gym frameworks (github)
- deep q-network reinforcement learning for cartpole environment.ipynb
Updated about 1 month ago