深入理解神经网络与R语言实现

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"《Packt_Neural_Networks_with_R》是一本由Giuseppe Ciaburro和Balaji Venkateswaran合著的书籍,涵盖了神经网络和人工智能的基本概念,以及如何在R语言中实现这些概念。本书深入探讨了神经网络的工作原理,包括分层结构、权重和偏置、监督与无监督学习、反向传播、激活函数、多层感知器、深度学习等。此外,还介绍了训练模型的步骤、评估指标、防止过拟合的方法、递归神经网络(RNN)和卷积神经网络(CNN)的应用,以及神经网络在不同领域的案例,如手写数字识别、文本预测和肿瘤检测。书中还涉及了与TensorFlow和Keras的集成,以及使用H2O和darch库进行自动编码器的实践。" 在这本书中,作者首先解释了神经网络的灵感来源以及其工作方式,强调了分层结构的重要性,每个层都有不同的权重和偏置。接着,他们讨论了训练神经网络的不同方法,包括有监督和无监督学习,以及如何通过反向传播调整权重。书中详细解释了各种激活函数,如线性函数、阶跃函数、Sigmoid、双曲正切和ReLU,并指导读者选择合适的激活函数。 接下来,作者介绍了多层感知器和前馈与反馈网络的概念,以及梯度下降法在优化神经网络学习算法中的作用。他们还讨论了神经网络的分类,例如使用R的neuralnet和nnet库实现的简单示例。书中还提到了深度学习,特别是多层神经网络和深度自编码器的实现,以及H2O框架在构建DNN时的应用。 对于模型训练和测试,书中提到了机器学习的三种主要类型:监督学习、无监督学习和强化学习。作者详细阐述了评估模型性能的各种指标,如混淆矩阵、真正例率、真负例率、准确率、精确率、召回率、F分数和ROC曲线。他们还讨论了如何避免过拟合和实现模型的泛化能力,以及数据预处理和ensemble预测在神经网络中的应用。 此外,书中还涵盖了RNN和LSTM在时间序列预测中的应用,以及CNN的四个基本步骤:滤波、池化、ReLU和全连接层的投票分类。作者提供了使用RNN预测湿度的例子,以及使用CNN的常见架构LeNet。最后,书中通过实际案例展示了神经网络在图像识别、文本预测、主成分分析和肿瘤检测等高级主题中的应用,同时介绍了R与TensorFlow和Keras的集成,以及H2O和darch库在自动编码器中的应用。 《Packt_Neural_Networks_with_R》是关于神经网络和深度学习在R语言中应用的一本全面而实用的指南,适合于希望使用R语言进行数据分析和模型构建的初学者和专业人士。
2017-11-13 上传
Uncover the power of artificial neural networks by implementing them through R code. About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who This Book Is For This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn Set up R packages for neural networks and deep learning Understand the core concepts of artificial neural networks Understand neurons, perceptrons, bias, weights, and activation functions Implement supervised and unsupervised machine learning in R for neural networks Predict and classify data automatically using neural networks Evaluate and fine-tune the models you build. In Detail Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you'll build a solid foundation knowledge of how a neural network learns from data, Table of Contents Chapter 1. Neural Network and Artificial Intelligence Concepts Chapter 2. Learning Process in Neural Networks Chapter 3. Deep Learning Using Multilayer Neural Networks Chapter 4. Perceptron Neural Network Modeling – Basic Models Chapter 5. Training and Visualizing a Neural Network in R Chapter 6. Recurrent and Convolutional Neural Networks Chapter 7. Use Cases of Neural Networks – Advanced Topics