Using simple neural network to study and predict the famous MNIST dataset. The dataset is a collection of images of handwritten numbers from 0 to 9 The result is a model that can achieve accuracy rate of 97%

In this demonstration, I used convolutional network to study the CIFAR10 dataset. Using convolutional layer to first filter and extract important information from the image, and then feed those features to a densely connected layer. The dataset is a collection of images of 10 different classes of object. The result is a model that can achieve accuracy rate of 75%

In this experiment, I used LSTM to predict return of interest of a stock price, then from there reverse calculation to predict stock price. This model manage to capture the trend and movement of stock price with very low error rate.

In this experiment, I will implement the famous autoencoder model that will learn the best fit function to compress image data from the Fashion MINST dataset. The model also has a decoder that can reconstruct to the original image. Although the reconstrcuted images are not identical, the error rate is acceptable and the important features are transfer completely.

Using OpenGL to load Mesh object files and render it in a window. User can navigate around with left, right, forward, backward, turn left, turn right