Description
Neural Networks and Deep Learning: The Foundational Course by Andrew Ng
This is **Course 1 of the legendary Deep Learning Specialization**—the course that launched thousands of AI careers. Taught by **Andrew Ng**, you’ll build **neural networks from scratch** in Python, understand **backpropagation intuitively**, and apply deep learning to real problems like image recognition.
What You’ll Build
- A logistic regression classifier for cat vs non-cat images
- A 2-layer neural network with vectorized NumPy code
- A deep neural network using Python and basic libraries
Core Concepts You’ll Master
- Neural network architecture—layers, units, activations
- Forward and backward propagation—the engine of deep learning
- Vectorization—speed up code 100x with NumPy
- Activation functions—sigmoid, tanh, ReLU, and when to use each
- Hyperparameter tuning—learning rate, iterations, initialization
Why This Course?
- Created by Andrew Ng—co-founder of Coursera and former head of Google Brain
- No frameworks—you’ll understand the math and code beneath TensorFlow/PyTorch
- Beginner-friendly—only basic Python and high school math required
Who Is This For?
- Complete beginners to deep learning
- Students starting the Deep Learning Specialization
- Developers who want to understand NNs beyond Keras
- Career switchers entering AI
Your Deep Learning Journey Starts Here
This course is the **gold standard**—rigorous, intuitive, and transformative. Millions have started here. Now it’s your turn.
Enroll now—and build your first neural network today.
