Quantum Machine Learning: learning on neural networks. Analytical gradient computation, the Hadamard test, and more. This time, we’re going a little deeper into the rabbit hole and looking at how to build a neural network on a quantum computer.
My last articles tackled Bayes nets on quantum computers (read it here!), and k-means clustering, our first steps into the weird and wonderful world of quantum machine learning.
This time, we’re going a little deeper into the rabbit hole and looking at how to build a neural network on a quantum computer.
In case you aren’t up to speed on neural nets, don’t worry — we’re starting with neural nets 101.
Almost everyone has heard of neural networks — they’re used to run some of the coolest tech we have today — self driving cars, voice assistants, and even the software that generates super realistic pictures of famous people doing questionable things.
What makes them different from regular algorithms is that instead of having to write down a set of rules, we need to provide networks with examples of the problem we want it to solve.
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What is Artificial Intelligence (AI)? AI is the ability of a machine to think like human, learn and perform tasks like a human. Know the future of AI, Examples of AI and who provides the course of Artificial Intelligence?
Artificial Intelligence (AI) will and is currently taking over an important role in our lives — not necessarily through intelligent robots.
You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.
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