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

• ​Deep Q-Network Reinforcement Learning (RL) for CartPole environment using Pytorch and OpenAI-Gym frameworks (github)
  
• deep q-network reinforcement learning for cartpole environment.ipynb