Deep Learning Specialization
by DeepLearning.AI (Andrew Ng)
Andrew Ng's five-course deep dive into neural networks, from basics to sequence models.
Overview
The Deep Learning Specialization is the natural next step after the ML Specialization. Across five courses you build neural networks from scratch, learn to tune and regularize them, dive into convolutional networks for vision, and study sequence models (RNNs, LSTMs, attention) for language. Assignments in Python and TensorFlow reinforce each idea. Audit free; certificate requires a subscription.
At a Glance
- Topic
- ML
- Level
- Intermediate
- Format
- Course
- Cost
- Freemium
- Duration
- ~3 months, ~8 hrs/week
- Provider
- DeepLearning.AI (Andrew Ng)
- Hands-on
- Yes — code/exercises
- Certificate
- Available
What You’ll Learn
- ✓Building and training deep neural networks
- ✓Regularization, optimization, and hyperparameter tuning
- ✓Convolutional networks for computer vision
- ✓Sequence models: RNNs, LSTMs, and attention
Highlights
- •Taught by Andrew Ng
- •Strong bridge toward transformers and LLMs
Who It’s For
Best For
- ✓Learners ready to go deep after intro ML
Prerequisites
- •Python
- •Intro machine learning
FAQ
What is Deep Learning Specialization?
A five-course specialization covering neural networks, hyperparameter tuning, CNNs, and sequence models with hands-on assignments.
Is Deep Learning Specialization free?
Deep Learning Specialization offers free content, with paid options for certificates or premium features.
What level is Deep Learning Specialization for?
Deep Learning Specialization is aimed at a intermediate audience. Recommended background: Python, Intro machine learning.
How long does Deep Learning Specialization take?
Expect roughly ~3 months, ~8 hrs/week. Most learners work through it at their own pace.
What will I learn from Deep Learning Specialization?
You'll learn: Building and training deep neural networks; Regularization, optimization, and hyperparameter tuning; Convolutional networks for computer vision; Sequence models: RNNs, LSTMs, and attention.