agascong / practicas_deeplearning_2024

Deep Learning Lab Sessions

Welcome to the Deep Learning Lab Sessions! In these sessions, we will explore various concepts and applications of deep learning. This readme file will provide you with an overview of each session, its objectives, and what you can expect to learn.

By the end of these sessions, you will have a solid understanding of convolutional filters, transfer learning, and how to deploy a deep learning model in a real-world application.

Happy learning!

Session 1: Understanding Convolutional Filters

Objective: Gain a comprehensive understanding of convolutional filters and their role in Convolutional Neural Networks (CNNs).

In this session, we will delve into the workings of convolutional filters, which are the building blocks of Convolutional Neural Networks (CNNs). We will discuss how these filters help in extracting features from images, and how they contribute to the success of CNNs in computer vision tasks and how can we try to make CNNs more interpretable.

Sessions 2-4: Transfer Learning and Deployment

Objective: Learn how to apply transfer learning to a simulated business case and deploy the model in a publicly available Streamlit app.

In these sessions, we will focus on applying transfer learning to a real-world simulated business case. We will make use of several pre-trained model and fine-tune it to suit our specific needs. Once our model is ready, we will deploy it in a publicly available Streamlit app, allowing us to test the results in real life. We will discuss on best practices for Deep Learning experimentation, which can be costly and time consuming and we will make use of Weights & Biases web app.