Independently Published
Deep Learning: The Ultimate Beginner's Guide to Artificial Intelligence and Neural Networks. Intermediate, Advanced and Expert Concepts and Techniques.
Key Metrics
- Robert Hack
- Independently Published
- Paperback
- 9798630949479
- 9.02 X 5.98 X 0.43 inches
- 0.61 pounds
- Computers > Artificial Intelligence - Computer Vision & Pattern Recognition
- English
Secure TransactionBook Description
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 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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