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Deep Machine Learning: Complete Tips and Tricks to Deep Machine Learning
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Название: Deep Machine Learning: Complete Tips and Tricks to Deep Machine Learning
Автор: Joe Grant
Издательство: Independently published
Год: 2019
Страниц: 154
Язык: английский
Формат: pdf, rtf
Размер: 10.1 MB

We live in a digital world, and AI is an important aspect of our lives. Many applications and machines help us to complete our daily tasks. Do you ever wonder how these machines are getting their intelligence? This happens due to the concept of machine learning and deep learning. In this book, you will learn how important deep learning is for our future machines, and we will provide tips and tricks to understand and learn the architecture presented in the book.

The book contains all the beginner and advanced knowledge related to deep learning. You will find the basics of deep learning and algorithms and concepts that are vital in this department. We have also provided information about the neural networks and complexities of the machine learning and AI world in this book.

Deep learning is a branch of machine learning. To understand the concept of deep learning, one has to understand the concept and basics of machine learning completely. Machine learning is comprised of learning algorithms that are used by the computers in performing a task without having any explicit instructions. Learning algorithms can be explained through an example of a linear regression algorithm. After that, we will move towards the concept of how computers fit existing data and find patterns that convert it into new generalize data. Most machine learning algorithms are based on settings called hyperparameters, which are adjusted separately by using additional data. Hyperparameters must be determined external and separate from learning algorithms. Machine learning interprets and processes data on the basis of statistics. The only difference between machine learning and the conventional way of using statistical data is that, in machine learning, there is increased emphasis on computers to statistically solve complicated functions and decreased emphasis on manual usage of formulas to interpret statistical data. Two major statistic approaches are Frequentist estimators and Bayesian inference.

Some key takeaway from the book is:

Feed-forward networks
Neural networks
Deep learning regulations and algorithms

This book is designed for both beginner and advanced readers and will provide you with the best knowledge related to deep learning and machine learning.

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