Deep Learning

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Hello! Welcome to this guide to "The Ultimate Beginner's Guide To Artificial Intelligence And Neural Networks"
An understanding of deep learning 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 AI and machine learning. 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 LearningHas 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 modelCan solve complex problemsflexible 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 LearningHistory of Deep LearningConceptual foundationsNeural Networks: The Building Blocks of Deep Learningtraining deep networksConvolutional and Recurrent Neural NetworksLearning FunctionsThe Future of Deep LearningAnd so much more


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