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Deep Learning: 2 Manuscripts - Deep Learning With Keras And Convolutional Neural Networks In Python

Deep Learning: 2 Manuscripts - Deep Learning With Keras And Convolutional Neural Networks In Python

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英语| 2018年3月20日| ASIN:B07BLX93F2 | 260页| AZW3 | 0.43 MB

本书将向您介绍各种有监督和无监督的深度学习算法,如多层感知器,线性回归和其他更先进的深度卷积和递归神经网络。您还将学习图像处理,手写识别,对象识别等等。

此外,当您探索时间序列,文本和音频等处理序列数据时,您将熟悉LSTM和GAN等递归神经网络。

这本书绝对是您在这个伟大的深度学习之旅中的最佳伴侣,Keras将向您介绍您需要了解的基础知识,以便采取后续步骤并学习更高级的深度神经网络。

这是您将在此学到的内容的预览...

深度学习和机器学习之间的区别

深度神经网络

卷积神经网络

用Keras建立深度学习模型

多层感知器网络模型

激活功能

使用MNIST进行手写识别

解决多级分类问题

循环神经网络和序列分类

以及更多…

Python中的卷积神经网络

本书通过简单易懂的方式向您介绍这个复杂的深度学习和人工神经网络世界,介绍了卷积神经网络背后的基础知识。它非常适合任何初学者,期待更多地了解这个机器学习领域。

本书是关于如何将卷积神经网络用于Python中的各种图像,对象和其他常见分类问题。在这里,我们还深入研究用于构建CNN的各种Keras层,我们将介绍不同的激活函数等等,最终将导致您创建高精度模型,能够在各种图像分类上执行出色的任务结果,对象分类和其他问题。

因此,在本书的最后,您将更好地了解这个世界,因此您将不仅准备好自己处理更复杂和更具挑战性的任务。

这是预览您将在本书中学到的内容......

卷积神经网络结构

卷积神经网络如何实际运作

卷积神经网络应用

卷积算子的重要性

不同的卷积神经网络层及其重要性

空间参数的安排

如何以及何时使用步幅和零填充

参数共享方法

矩阵乘法及其重要性

汇集和密集的层

引入非线性relu激活函数

如何使用反向传播训练您的卷积神经网络模型

如何以及为什么申请辍学

CNN模型培训流程

如何构建卷积神经网络

生成预测和计算损失函数

如何训练和评估您的MNIST分类器

如何构建简单的图像分类CNN

还有更多!


English | March 20, 2018 | ASIN: B07BLX93F2 | 260 pages | AZW3 | 0.43 MB

This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition and much more.

Furthermore, you will get familiar with recurrent neural networks like LSTM and GAN as you explore processing sequence data like time series, text, and audio.

The book will definitely be your best companion on this great deep learning journey with Keras introducing you to the basics you need to know in order to take next steps and learn more advanced deep neural networks.

Here Is a Preview of What You’ll Learn Here…

The difference between deep learning and machine learning

Deep neural networks

Convolutional neural networks

Building deep learning models with Keras

Multi-layer perceptron network models

Activation functions

Handwritten recognition using MNIST

Solving multi-class classification problems

Recurrent neural networks and sequence classification

And much more…

Convolutional Neural Networks in Python

This book covers the basics behind Convolutional Neural Networks by introducing you to this complex world of deep learning and artificial neural networks in a simple and easy to understand way. It is perfect for any beginner out there looking forward to learning more about this machine learning field.

This book is all about how to use convolutional neural networks for various image, object and other common classification problems in Python. Here, we also take a deeper look into various Keras layer used for building CNNs we take a look at different activation functions and much more, which will eventually lead you to creating highly accurate models able of performing great task results on various image classification, object classification and other problems.

Therefore, at the end of the book, you will have a better insight into this world, thus you will be more than prepared to deal with more complex and challenging tasks on your own.

Here Is a Preview of What You’ll Learn In This Book…

Convolutional neural networks structure

How convolutional neural networks actually work

Convolutional neural networks applications

The importance of convolution operator

Different convolutional neural networks layers and their importance

Arrangement of spatial parameters

How and when to use stride and zero-padding

Method of parameter sharing

Matrix multiplication and its importance

Pooling and dense layers

Introducing non-linearity relu activation function

How to train your convolutional neural network models using backpropagation

How and why to apply dropout

CNN model training process

How to build a convolutional neural network

Generating predictions and calculating loss functions

How to train and evaluate your MNIST classifier

How to build a simple image classification CNN

And much, much more!

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