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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.
When people watch videos online, their video quality is mostly dependent on their internet connection. Faster internet means better video. This is great for people with consistent high-speed connections, but those folks are still in the minority. Instead, most people simply end up suffering through low quality videos; we accept it as a sad fact of life—like morning traffic or melting ice caps. It sucks, but it's going to happen. However, at least for video quality, there might be a better way. Enter Per-Title Encoding.
This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learned in calculus 1, and provide links to help you refresh the necessary math where needed. Note that you do not need to understand this material before you start learning to train and use deep learning in practice; rather, this material is for those who are already familiar with the basics of neural networks, and wish to deepen their understanding of the underlying math.