Deep Learning: The Ultimate Beginner's Guide to Artificial Intelligence and Neural Networks. Intermediate, Advanced and Expert Concepts and TechniquesКНИГИ » ПРОГРАММИНГ
Название: Deep Learning: The Ultimate Beginner's Guide to Artificial Intelligence and Neural Networks. Intermediate, Advanced and Expert Concepts and Techniques Автор: Robert Hack Издательство: Amazon.com Services LLC Год: 2020 Страниц: 122 Язык: английский Формат: pdf, azw3, epub Размер: 12.7 MB
Everything You Need to Know About Deep Learning. Do you want to know all about Deep Learning? Wondering what you need to get started with Deep Learning? You Are 1-Click Away From Knowing All About Deep Learning. Hello! Welcome to this guide to "The Ultimate Beginner's Guide To Artificial Intelligence And Neural Networks".
An understanding of Deep Learning (DL) begins with a precise definition of terms. Otherwise, you have a hard time separating the media hype from the realities of what deep learning can actually provide. Deep learning is part of both Artificial Intelligence (AI) and Machine Learning (ML). To understand Deep Learning, you must begin at the outside — that is, you start with AI, and then work your way through machine learning, and then finally define deep learning. This book would help you through this process.
Why study Deep Learning:
- Has best-in-class performance on problems that significantly outperforms other solutions in multiple domains. This includes speech, language, vision, playing games like Go etc. This isn’t by a little bit, but by a significant amount. - Reduces the need for feature engineering, one of the most time-consuming parts of machine learning practice. - Is an architecture that can be adapted to new problems relatively easily e.g. Vision, time series, language etc., are using techniques like convolutional neural networks, recurrent neural networks, long short-term memory etc. - Feature engineering can be automatically executed inside Deep Learning model - Can solve complex problems - Flexible to be adapted to new challenge in the future (or transfer learning can be easily applied) - High automation. Deep learning library (Tensorflow, Keras, or MATLAB…) can help users build a deep learning model in seconds (without the need of deep understanding)
More precisely, the book will teach you:
Introduction to Deep Learning History of Deep Learning Conceptual foundations Neural Networks: The Building Blocks of Deep Learning training deep networks Convolutional and Recurrent Neural Networks Learning Functions The Future of Deep Learning And so much more...
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