Neural Networks and Deep Learning Course Syllabus

Overview: This course provides a comprehensive introduction to neural networks and deep learning, designed for beginners with no prior experience. You'll gain a solid understanding of deep learning fundamentals, build and train neural networks, and apply key concepts through practical implementations. With approximately 19 hours of content, the course offers flexible, self-paced learning, making it accessible to both technical and non-technical audiences.

Module 1: Introduction to Deep Learning

Estimated time: 2 hours

• Analyze the major trends driving the rise of deep learning
• Understand real-world applications of deep learning
• Identify where deep learning is applied across industries

Module 2: Neural Networks Basics

Estimated time: 5 hours

• Learn the structure and functioning of neural networks
• Implement forward propagation in a neural network
• Implement backward propagation for training
• Understand the role of gradients and cost functions

Module 3: Shallow Neural Networks

Estimated time: 6 hours

• Build a shallow neural network with one hidden layer
• Apply vectorization to optimize neural network computations
• Train neural networks using gradient descent
• Understand activation functions and their importance

Module 4: Deep Neural Networks

Estimated time: 6 hours

• Construct deep neural networks with multiple hidden layers
• Understand the architecture and flow of deep networks
• Identify key parameters in neural network design
• Apply deep learning techniques to practical problems

Module 5: Final Project

Estimated time: 4 hours

• Build a fully connected deep neural network from scratch
• Train and evaluate the model using vectorized implementation
• Submit a working implementation demonstrating learned concepts

Prerequisites

• Basic knowledge of linear algebra (vectors, matrices)
• Familiarity with Python programming (helpful but not required)
• High school level mathematics

What You'll Be Able to Do After

• Understand foundational concepts of neural networks and deep learning
• Build, train, and apply fully connected deep neural networks
• Implement efficient, vectorized neural network models
• Identify and adjust key parameters in neural network architectures
• Apply deep learning techniques to real-world applications