Deep Learning Fundamentals Curriculum

Course Description:

This course provides an introduction to the fundamentals of deep learning. Students will learn the basics of neural networks, deep learning architectures, and how to apply deep learning to various domains.

Lesson 1: Introduction to Deep Learning

• Overview of neural networks
• History and evolution of deep learning
• Applications of deep learning

Lesson 2: Neural Networks

• Perceptrons and multi-layer perceptrons
• Activation functions
• Backpropagation algorithm

Lesson 3: Convolutional Neural Networks (CNNs)

• Introduction to CNNs
• Architecture of CNNs
• Training and inference with CNNs

Lesson 4: Recurrent Neural Networks (RNNs)

• Introduction to RNNs
• Long Short-Term Memory (LSTM) networks
• Applications of RNNs

Lesson 5: Autoencoders and Generative Adversarial Networks (GANs)

• Introduction to autoencoders
• Variational autoencoders (VAEs)
• Introduction to GANs

Lesson 6: Deep Learning Frameworks

• Overview of deep learning frameworks (e.g., TensorFlow, PyTorch)
• Setting up a deep learning environment
• Hands-on coding with a deep learning framework

Lesson 7: Advanced Topics

• Transfer learning
• Attention mechanisms
• Reinforcement learning basics in the context of deep learning

Lesson 8: Final Project

• Students work on a deep learning project applying the concepts learned in the course

Assessment:

• Lessonly quizzes or assignments
• Final project demonstration and submission

Prerequisites:

Basic knowledge of machine learning concepts and programming (preferably Python) is recommended. No prior knowledge of deep learning is required.