Deep Neural Networks @ MIMUW

This repository contains my personal solutions for the "Deep Neural Networks" course at MIMUW. The course covers practical aspects of deep neural networks, including various techniques, algorithms, and tools used in fields like image recognition and natural language processing.

Course Topics

1. Introduction to neural networks
2. Hardware and software in deep learning
3. Convolutional Neural Networks (CNNs)
4. Recurrent Neural Networks (RNNs)
5. Generative Adversarial Networks (GANs)
6. Reinforcement learning
7. Updates in the field of neural networks
8. Applications (e.g., AlphaGo, robots)

Learning Outcomes

The course aims to provide a structured understanding of machine learning and neural networks, equip students with skills to use modern machine learning libraries, and implement image classification and text processing algorithms. It also promotes critical thinking, entrepreneurial action, and the importance of expert opinions.

Repository Contents

The repository consists of Jupyter notebooks, scripts, and resources used to solve the course exercises and projects.

Getting Started

1. Clone the repository
2. Navigate into the repository
3. Install required Python packages
4. Launch Jupyter Notebook

Disclaimer

This repository is for personal academic use. Plagiarism is not encouraged. If you're a student in the "Deep Neural Networks" course, refrain from copying or using this material for graded assignments. Use this for learning and understanding.