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Python Machine Learning: Machine Learning And Deep Learning From Scratch Illustrated With Python, Scikit-Learn, Keras, Theano and TensorflowНазвание: Python Machine Learning: Machine Learning And Deep Learning From Scratch Illustrated With Python, Scikit-Learn, Keras, Theano and Tensorflow
Автор: Moubachir Madani Fadoul
Издательство: Amazon.com Services LLC
Год: 2020
Язык: английский
Формат: pdf, azw3, mobi
Размер: 10.1 MB

Deep Learning (DL) is a form of Machine Learning (ML) that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in Deep Learning.

Hierarchical learning or structured deep learning or deep learning in short is part of the family of machine learning methods which are themselves a subset of the broader field of Artificial Intelligence. Deep learning defined as a class of machine learning algorithms that use several layers of nonlinear processing units for transformation and feature extraction. The output of each successive layer from the previous layer is used as input.

Deep neural networks, recurrent neural networks and deep belief networks have been used in several fields such as speech recognition, computer vision, audio recognition, natural language processing, social network filtering, machine translation, and bioinformatics where they produced results comparable to and in some cases better than human experts have.

What you will learn:
•Master the frameworks, models, and techniques that enable machines to 'learn' from data
•Use scikit-learn for machine learning and TensorFlow for deep learning
•Apply machine learning to classification, predict predict customer churning , and more
•Build and train neural networks, GANs, CNN, and other models
•Discover best practices for evaluating and tuning models
•Predict target outcomes using optimization algorithm such as Gradient Descent algorithm analysis
•Overcome challenges in deep learning algorithms by using dropout, regulation

Who This Book Is For:
If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.

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