книга Data Science for Beginners: 2 Books in 1: Deep Learning for Beginners + Machine Learning with Python- A Crash Course to Go Through the Artificial Intelligence Revolution, Python and Neural Networks

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Data Science for Beginners: 2 Books in 1: Deep Learning for Beginners + Machine Learning with Python- A Crash Course to Go Through the Artificial Intelligence Revolution, Python and Neural Networks

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Название: Data Science for Beginners: 2 Books in 1: Deep Learning for Beginners + Machine Learning with Python- A Crash Course to Go Through the Artificial Intelligence Revolution, Python and Neural Networks
Автор: Russel R. Russo
Страниц: 295
Формат: PDF, EPUB
Размер: 10.1 MB
Качество: Отличное
Язык: Английский
Год издания: 2020


 Are you fascinated by Data Science but it seems too complicated? Do you want to learn everything about Artificial Intelligence but it looks like it is an exclusive club? If this is you, please keep reading: you are in the right place, looking at the right book. SInce you are reading these lines you have probably already noticed this: Artificial Intelligence is all around you. Your smartphone that suggests you the next word you want to type, your Netflix account that recommends you the series you may like or Spotify’s personalised playlists. This is how machines are learning from you in everyday life. And these examples are only the surface of this technological revolution.

Deep learning is a very complex subject and it may be difficult to understand. If that’s you, don’t feel discouraged. Keep this book close by and read it in small bites. Eventually, you will grasp its meaning and if you’re seriously interested, don’t be afraid to reach out for more knowledge. Thank you for walking with us into the world of machine learning, artificial intelligence, and deep learning.
Google and Facebook are, however, trying to identify words spoken in order to categorize them. They are also trying to help different machines identify the relationship between alternate objects in the training data set and assess that relationship between differing variables or data points.

For example, if you want a computer to interpret “this is an elephant” exactly that way instead of “this is a collection of pixels”, you must determine a way to map some features of the elephant to other complex features. For instance, you can convert a line, curve, pixels, sounds of alphabets, and much more if you know how to transform the features of that entity into ones that can be recognized by the machine. It can then use indexing or inference to predict the output. This type of learning is called “deep learning”.


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