Go to books ↓
Deep learning is tricky in several respects. Not only can the math and theory quickly lead to hairballs of gradient formulas and update equations, but deep learning models are also complex and often finicky pieces of software.
"How can you hear my heartrate? My heart doesn't beat that loudly! And you can detect my heart rate from video? witchcraft!?". In fact you would be almost right. Several studies have shown that it is completely possible.
Facebook’s algorithms are able to recognize your friends’ faces after they have been tagged only a few times. It’s pretty amazing technology — Facebook can recognize faces with 98% accuracy which is pretty much as good as humans can do!
This is the first of a multi-part series explaining the fundamentals of deep learning by long-time tech journalist Michael Copeland.
It’s not hyperbole to say that use cases for machine learning and deep learning are only limited by our imaginations. About one year ago, a former embedded systems designer from the Japanese automobile industry named Makoto Koike started helping out at his parents’ cucumber farm, and was amazed by the amount of work it takes to sort cucumbers by size, shape, color and other attributes.
This series of posts was initially created as a way to explain Neural Networks and Deep Learning to my younger brother. Therefore, we are not going to assume any prior knowledge of calculus or linear algebra, among other things.
Systems powered by deep learning algorithms should be safe from human interference, right? How is a hacker going to get past a neural network trained on terabytes of data?
The pattern is that there’s an existing software project doing data processing using explicit programming logic, and the team charged with maintaining it find they can replace it with a deep-learning-based solution.
Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software.