Anyone that plays video games knows the immense frustration that arises from repeatedly losing. It requires extreme determination and focus to continue playing the same level over and over, attaining in-game levels or equipment to increase the...
Anyone that plays video games knows the immense frustration that arises from repeatedly losing. It requires extreme determination and focus to continue playing the same level over and over, attaining in-game levels or equipment to increase the chance of victory. Some people are able to muster up this necessary determination, patiently re-playing a level until they finally win. But often, hours of playing the same level and making the same mistakes creates pent-up anger that explodes into a fiery rage-quit. Patience often expires after five or six consecutive losses, leading players to lose all motivation to continue progressing in a game.
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.
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
A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start?
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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.