Last modified: Jan 31 2026 at 10:09 PM • 1 min read
Neural Networks and Deep Learning
Course 1 of the Deep Learning Specialization by Andrew Ng.
Course Overview
This course provides a comprehensive introduction to deep learning, covering:
- Foundations: Understanding what neural networks are and how they work
- Implementation: Building neural networks from scratch using Python and NumPy
- Practical Skills: Vectorization, efficient computation, and best practices
- Applications: Image recognition, classification, and other real-world problems
Course Structure
Week 1: Introduction to Deep Learning
- What is a Neural Network?
- Supervised Learning with Neural Networks
- Why is Deep Learning Taking Off?
Week 2: Neural Networks Basics
- Binary Classification
- Logistic Regression
- Gradient Descent
- Vectorization
- Python and NumPy fundamentals
Week 3: Shallow Neural Networks
- Neural Network Representation
- Forward and Backward Propagation
- Activation Functions
- Random Initialization
Week 4: Deep Neural Networks
- Deep L-layer Neural Networks
- Forward and Backward Propagation in Deep Networks
- Hyperparameters and Tuning
- Building Your Deep Neural Network
Learning Resources
Prerequisites
- Basic Python programming
- Linear algebra fundamentals
- Basic calculus (derivatives)
What You’ll Build
Throughout this course, you’ll implement:
- Logistic regression from scratch
- Shallow neural networks (one hidden layer)
- Deep neural networks (multiple hidden layers)
- Image classification systems