| Content | Description |
|---|---|
| 📈 Scientific Data Visualization | Data Visualization notebooks for my Scientific data visualization Uni course |
| 💻 Statistics | Git gud at statistics - in R |
| 📔 Projects from Books | Projects that I made following the book materials from well known ML related content, mostly from O'Reilly |
| 📊 Kaggle challenges | Notebooks and scripts related to competitions, training, data analysis on Kaggle |
| ⚙️ NN from Scratch | Neural Networks implemented from scratch, using only libraries to facilitate mathematical operations on matrices and data handling |
| 🔥 Pytorch projects | Deep learning project using Pytorch as the main library |
| 💻 Tensorflow projects | Deep learning project using Tensorflow as the main library |
This repository contains a collection of AI projects using PyTorch, Kaggle challenges, projects from machine learning books, and other projects. The projects cover a wide range of topics, from natural language processing and computer vision to time series analysis and deep reinforcement learning.
The repository is organized into different folders, each containing a different project. The PyTorch projects include implementations of popular deep learning models, such as convolutional neural networks and recurrent neural networks, as well as practical applications of these models in various domains.
The Kaggle challenges folder contains notebooks and code for participating in Kaggle competitions, including solutions to some of the most popular challenges on the platform.
The projects from machine learning books include implementations of the algorithms and techniques presented in popular machine learning books, such as "Hands-On Machine Learning with Scikit-Learn and TensorFlow" or "Designing Machine Learning Systems".
In addition to these projects, the repository also contains a variety of other AI projects, including projects using different deep learning frameworks and libraries, as well as projects exploring different application areas, such as natural language understanding and generative modeling.