Deep Learning 101 Syllabus

Welcome to the syllabus for our Deep Learning 101 course! This course is designed for beginners who want to dive into the world of deep learning. Here's what you can expect from this course:

Course Overview

• Duration: 8 weeks
• Language: English
• Prerequisites: Basic knowledge of Python and Machine Learning

Course Content

Week 1: Introduction to Deep Learning

• What is Deep Learning?
• History of Deep Learning
• Applications of Deep Learning

Week 2: Neural Networks

• Basic Neural Network Architecture
• Activation Functions
• Training and Backpropagation

Week 3: Convolutional Neural Networks (CNNs)

• Introduction to CNNs
• Convolutional Layers
• Pooling Layers
• Applications of CNNs

Week 4: Recurrent Neural Networks (RNNs)

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

Week 5: Generative Adversarial Networks (GANs)

• Introduction to GANs
• Architecture of GANs
• Applications of GANs

Week 6: Transfer Learning

• What is Transfer Learning?
• Pre-trained Models
• Fine-tuning
• Applications of Transfer Learning

Week 7: Practical Deep Learning Projects

• Project 1: Image Classification
• Project 2: Natural Language Processing

Week 8: Final Project Presentation and Review

Learning Resources

• Python for Data Science
• Machine Learning Basics

Prerequisites

Before starting this course, we recommend that you have a basic understanding of Python and machine learning concepts.

Conclusion

This syllabus provides an overview of the Deep Learning 101 course. We hope this course will help you gain a solid foundation in deep learning and prepare you for more advanced topics in the future. Happy learning!

Deep Learning