The assignment and project implementations for EEE443 course, Bilkent University.
- Finding the probability distribution of a network with a number of neurons.
- Building a neural network to perform a simple logic formula.
- Building a single layer perceptron to learn the representations of letters in the alphabet.
- Implemented in Python (Jupyter Notebook)
- Proving the solution of mean squared error loss.
- Design and implement a neural network from scratch, and train it for a binary classification (car vs cat) problem.
- Implement a neural network that predicts the fourth word given the initial three. A simple NLP application.
- Implemented in Python (Jupyter Notebook)
- Unsupervised feature extraction using a sparse auto-encoders. MSE loss function with the addition of Tikhonov (L2) regularization and KL-Divergence to ensure sparsity.
- Observation and completion of networks about CNNs, PyTorch and TensorFlow.
- Implemented in Python (Jupyter Notebook)
- @emredonmez98
- Built an image captioning networks that generates automated captions from an image.
- A custom Flickr dataset is used.
- A pretrained Inception V3 is used to extract features from the images.
- Then, a an attention based RNN decoder (with LSTMs) is trained (initialized with GloVe embeddings) is used to generate captions for the images.