Deep Learning Labs Series Overview

Overview

This series of labs offers a comprehensive journey through various aspects of deep learning, covering Convolutional Neural Networks (CNNs), loss functions, visualization techniques, Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Generative Adversarial Networks (GANs). Each lab combines theoretical insights with practical exercises.

Labs Overview

1. CNN Theoretical Exercise Guide: Explore CNN components through manual calculations and practical exercises in a Colab Notebook.
2. Loss Functions and Evaluation Metrics in CNNs: Understand and calculate various loss functions and evaluation metrics.
3. Visualizing CNNs with t-SNE and PCA: Delve into CNN internals using visualization techniques like t-SNE and PCA.
4. RNNs and LSTMs: Focus on sequence learning and the structure of LSTMs.
5. Exploring GANs: Analyze GANs' architecture, functionality, and applications.

Prerequisites

• Basic knowledge of neural networks and specific architectures.
• Proficiency in Python and TensorFlow.

Objectives

• Develop a deep understanding of different deep learning architectures.
• Apply theoretical concepts in practical scenarios using Python and TensorFlow.
• Analyze and compare the performance of various deep learning models.