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It is recommended to use after updating to the latest version(1.1.0).

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