Copyright and License

© 2026, Daniel Bazo Correa

This course material is licensed under the MIT License.

Disclaimer

• This course material is provided "as is", without warranty of any kind, express or implied.
• The author assumes no responsibility or liability for any errors, omissions, or outcomes resulting from the use of this content.
• All analyses and interpretations are for educational and research purposes only and do not constitute professional advice.

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Deep Learning Course

This repository contains a comprehensive deep learning course with hands-on examples implemented in PyTorch. It provides a progressive introduction to core concepts, mathematical foundations, and practical applications of deep learning, while remaining accessible to non-specialist readers.

Course Overview

The main topics covered in the course are:

• Topic 1, Initial Concepts: Introduction to neural networks, differences between traditional machine learning and deep learning, and main components of a deep learning workflow: Datasets, models, loss functions, optimization algorithms, and evaluation metrics.

• Topic 2, Mathematics: Mathematical foundations for deep learning, including linear algebra, calculus, and basic probability and statistics, with emphasis on their application to neural network training and tensor computation in PyTorch.

• Topic 3, Applications: End-to-end examples of deep learning applied to tasks such as classification, regression, and recommendation, including model definition, choice of loss functions and optimizers, and implementation of training and evaluation loops in PyTorch.

• Topic 4, Computer Vision: Deep learning methods for image processing and computer vision, including convolutional neural networks, image classification, object detection, segmentation, and transfer learning using pre-trained models.

• Topic 5, Sequential Models: Models for sequential data, such as text and time-ordered signals, covering recurrent architectures (RNNs, LSTMs, GRUs) and, where appropriate, attention mechanisms and transformer-based models.

• Topic 6, Graph Models: Introduction to graph neural networks and related architectures for node classification, link prediction, and graph classification, using graph-structured data.

Quick Start

Prerequisites

The following tools are required to install and run the course materials:

• uv package manager: Tool used for fast and reproducible dependency and environment management.

Installation

To obtain the materials and configure the environment, execute the following commands in a terminal:

# Clone the repository
git clone https://github.com/danibcorr/unie-deep-learning.git
cd unie-deep-learning

# Install dependencies and set up the environment
make setup

The make setup command automatically installs the required Python dependencies and prepares a suitable execution environment for the notebooks and scripts in the repository.

Author

This course has been developed by Daniel Bazo Correa. Additional information and professional contact can be found through LinkedIn or GitHub.