TensorFlow Quantum allow data scientists to build machine learning models that work on quantum architectures. An extremely helpful article. You will definitely regret skipping it.
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The intersection of quantum computing and artificial intelligence(AI) promises to be one of the most fascinating movement in the entire history of technology. The emergence of quantum computing its likely to force us to reimagine almost all the existing computing paradigms and AI is not an exception. However, the computational power of quantum computers also has the potential to accelerate many areas of AI that remain unpractical today. The first step for AI and quantum computing to work together is to reimagine machine learning models to work on quantum architectures. Recently, Google open sourced TensorFlow Quantum, a framework for building quantum machine learning models.
The core idea of TensorFlow Quantum is to interleave quantum algorithms and machine learning programs all within the TensorFlow programming model. Google refers to this approach as quantum machine learning and is able to implement it by leveraging some of its recent quantum computing frameworks such as Google Cirq.
This "Deep Learning vs Machine Learning vs AI vs Data Science" video talks about the differences and relationship between Artificial Intelligence, Machine Learning, Deep Learning, and Data Science.
What is the difference between machine learning and artificial intelligence and deep learning? Supervised learning is best for classification and regressions Machine Learning models. You can read more about them in this article.
Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
This article will simply explain the concept which will help you understand the difference between Machine Learning and Deep Learning.