This course was written in collaboration with machine learning researchers and lecturers from MIT, Princeton, and Stanford.
This interactive course dives into the fundamentals of artificial neural networks, from the basic frameworks to more modern techniques like adversarial models.
You’ll answer questions such as how a computer can distinguish between pictures of dogs and cats, and how it can learn to play great chess.
Using inspiration from the human brain and some linear algebra, you’ll gain an intuition for why these models work – not just a collection of formulas.
This course is ideal for students and professionals seeking a fundamental understanding of neural networks, or brushing up on basics.
This interactive course dives into the fundamentals of artificial neural networks, from the basic frameworks to more modern techniques like adversarial models.
You’ll answer questions such as how a computer can distinguish between pictures of dogs and cats, and how it can learn to play great chess.
Using inspiration from the human brain and some linear algebra, you’ll gain an intuition for why these models work – not just a collection of formulas.
This course is ideal for students and professionals seeking a fundamental understanding of neural networks, or brushing up on basics.