DEEP-LEARNING-2023

A series of notebooks that constitute the coding assignments for Deep learning and Neural Networks subject in Artificial Intelligence degree (UAB).

Assignments

1. Multilayer Perceptron

Generate several Multilayer Perceptron (MLP) neural networks using PyTorch to perform classification and regression tasks, create visualizations, and gain insights into network behavior. Run it on Colab to visualize the results effectively.

MLP Intro Notebooks
Unsolved Notebook
Solved Notebook

2. MLP for Images

Learn how to use a Multi-Layer Perceptron (MLP) (Fully-Connected, Feed-Forward Network) for classifying images. An MLP can be used with any kind of input data if it can be represented as a vector of real numbers. In the case of images (2D arrays of pixel values), flatten the images into a 1D array to classify them effectively.

MLP for Images Notebooks
Unsolved Notebook
Solved Notebook

3. Introduction to Convolutional Neural Networks (CNN)

Explore Convolutional Neural Networks (CNNs) in this assignment.

Intro to CNN Notebooks
Unsolved Notebook
Solved Notebook

4. Convolutional Neural Networks with Pretrained Models

Learn how to use pretrained models with Convolutional Neural Networks (CNNs).

Pretrained CNN Notebooks
Unsolved Notebook
Solved Notebook

5. Autoencoders

Explore Autoencoders, a type of neural network used in unsupervised learning.

Autoencoders Notebooks
Unsolved Notebook
Solved Notebook

6. Recurrent Neural Networks for Language Modeling

Dive into Recurrent Neural Networks (RNNs) and their applications in language modeling.

RNNs Notebooks
Unsolved Notebook
Solved Notebook

7. Attention

Explore attention mechanisms in deep learning.

Attention Notebooks
Unsolved Notebook
Solved Notebook

Professor and provider of notebooks:

Dimosthenis Karatzas.