- Image Recognition using Convolutional Neural Network
- Dataset MNIST and CIFAR10
- Showing correctly classified and miss classified Images
- Displaying 9 feature maps per convolutional layer
- Effect of L2 Regularization on overfittng and accuracy
- Building a recurrent neural network for character-level language model
- Recurrent neural network (RNN) for word generation using character-level language model
- Long short term memory (LSTM) for word generation using charater-level language model
- Shakespeare dataset
- Regression using Neural Network with 2 Hidden Layers from scratch
- Classification using Neural Network with one Hidden layer from scratch
- Regularized linear regression (polynomial basis) using LSE and Newton's method and visualization
- MNIST Digit classification using Naive Bayes that support discrete and continous feature of MNIST data
- Online learning to learn the beta distribution of the parameter p (chance to see 1) of the coin tossing trails in batch
- Random Data Generator
- Sequential Estimator
- Baysian Linear regression
- Support Vector Machine to tackle classification of MNIST data using LIBSVM library
- kernel functions used (linear, polynomial, and RBF kernels)
- Find out support vectors using 2D Dataset
- Implementation of k-means clustering, kernel k-means, spectral clustering, DBSCAN using Numpy from scratch
- Dataset: 2 datasets with points on 2d space, circle.txt and moon.txt
- Different ways to do dimension reduction on MNIST data using PCA, LDA, S-SNE and T-SNE
- Use PCA to show the first 25 eigenfaces using att_faces dataset
- Logistic Regression from scratch
- Random data generator
- EM Algorithm from scratch
- Dataset: MNIST Data