Recurrent Neural Network with LSTM
This project implements an RNN with LSTM layers to model and predict sequential data. LSTM networks are designed to capture long-term dependencies and are especially powerful for time series forecasting, natural language processing, and other tasks involving ordered data. The model learns temporal patterns and retains information across time steps using memory cells and gating mechanisms.
AutoEncoder
The second project features an Autoencoder, a type of unsupervised neural network used for dimensionality reduction and feature learning. It consists of an encoder that compresses the input into a latent representation, and a decoder that reconstructs the input from this compressed code. Autoencoders are useful for anomaly detection, denoising, and pretraining tasks in deep learning.
Generative Adversarial Network
The third project showcases a GAN, a generative model composed of two networks: a Generator and a Discriminator. The Generator aims to create realistic data samples (in this case pistachio), while the Discriminator tries to distinguish between real and fake samples. Through adversarial training, the Generator learns to produce increasingly realistic outputs. This project demonstrates the generative power of GANs in synthesizing new data from noise.