- 面向工程师的实用机器学习和AI(影印版)(英文版)
- - 作者：(美)杰夫·普洛西|责编:张烨
  - 出版社：东南大学
  - ISBN：9787576606577
  - 出版日期：2023/03/01
  - 页数：400
- 售价：63.2

内容大纲
    许多AI入门指南可以说都是变相的微积分书籍，但这本书基本上避开了数学。作者Jeff Prosise帮助工程师和软件开发人员建立了对AI的直观理解，以解决商业问题。需要创建一个系统来检测雨林中非法砍伐的声音、分析文本的情感或预测旋转机械的早期故障？这本实践用书将教你把AI和机器学习应用于职场工作所需的技能。
    书中的示例和插图来自于Prosise在全球多家公司和研究机构教授的AI和机器学习课程。不说废话，也没有可怕的公式——纯粹就是写给工程师和软件开发人员的快速入门，并附有实际操作的例子。
    本书将帮助你：
    ·学习什么是机器学习和深度学习及其用途
    ·理解流行的机器学习算法原理及其应用场景
    ·使用Scikit-Learn在Python中构建机器学习模型，使用Keras和TensorFlow构建神经网络
    ·训练回归模型以及二元和多元分类模型并给其评分
    ·构建面部识别模型和目标检测模型
    ·构建能够响应自然语言查询并将文本翻译成其他语言的语言模型
    ·使用认知服务将AI融入你编写的应用程序中
作者介绍
杰夫·普洛西(Jeff Prosise)是一名工程师，热衷于向工程师和软件开发人员介绍AI 和机器学习的种种神奇之处。作为Wintellect的联合创始人，他已经在微软培训了数千名开发人员，并在一些全球最大规模的软件会议上发表过演讲。此外，Jeff在橡树岭国家实验室和劳伦斯利弗莫尔国家实验室从事高功率激光系统和聚变能源研究。他目前担任Atmosera的首席学习官，帮助客户将AI融入他们的产品。
目录
Foreword
Preface
Part I.  Machine Learning with Scikit-Learn
  1. Machine Learning
    What Is Machine Learning?
      Machine Learning Versus Artificial Intelligence
      Supervised Versus Unsupervised Learning
    Unsupervised Learning with k-Means Clustering
      Applying k-Means Clustering to Customer Data
      Segmenting Customers Using More Than Two Dimensions
    Supervised Learning
      k-Nearest Neighbors
      Using k-Nearest Neighbors to Classify Flowers
    Summary
  2. Regression Models
    Linear Regression
    Decision Trees
    Random Forests
    Gradient-Boosting Machines
    Support Vector Machines
    Accuracy Measures for Regression Models
    Using Regression to Predict Taxi Fares
    Summary
  3. Classification Models
    Logistic Regression
    Accuracy Measures for Classification Models
    Categorical Data
    Binary Classification
      Classifying Passengers Who Sailed on the Titanic
      Detecting Credit Card Fraud
    Multiclass Classification
    Building a Digit Recognition Model
    Summary
  4. Text Classification
    Preparing Text for Classification
    Sentiment Analysis
    Naive Bayes
    Spam Filtering
    Recommender Systems
      Cosine Similarity
      Building a Movie Recommendation System
    Summary
  5. Support Vector Machines
    How Support Vector Machines Work
      Kernels
      Kernel Tricks
    Hyperparameter Tuning
    Data Normalization
    Pipelining
    Using SVMs for Facial Recognition

    Summary
  6. Principal Component Analysis
    Understanding Principal Component Analysis
    Filtering Noise
    Anonymizing Data
    Visualizing High-Dimensional Data
    Anomaly Detection
      Using PCA to Detect Credit Card Fraud
      Using PCA to Predict Bearing Failure
      Multivariate Anomaly Detection
    Summary
  7. Operationalizing Machine Learning Models
    Consuming a Python Model from a Python Client
    Versioning Pickle Files
    Consuming a Python Model from a C# Client
    Containerizing a Machine Learning Model
    Using ONNX to Bridge the Language Gap
    Building ML Models in C# with ML.NET
      Sentiment Analysis with ML.NET
      Saving and Loading ML.NET Models
    Adding Machine Learning Capabilities to Excel
    Summary
Part II.  Deep Learning with Keras and TensorFlow
  8. Deep Learning
    Understanding Neural Networks
    Training Neural Networks
    Summary
  9. Neural Networks
    Building Neural Networks with Keras and TensorFlow
      Sizing a Neural Network
      Using a Neural Network to Predict Taxi Fares
    Binary Classification with Neural Networks
      Making Predictions
      Training a Neural Network to Detect Credit Card Fraud
    Multiclass Classification with Neural Networks
    Training a Neural Network to Recognize Faces
    Dropout
    Saving and Loading Models
    Keras Callbacks
    Summary
  10. Image Classification with Convolutional Neural Networks
    Understanding CNNs
      Using Keras and TensorFlow to Build CNNs
      Training a CNN to Recognize Arctic Wildlife
    Pretrained CNNs
    Using ResNet50V2 to Classify Images
    Transfer Learning
    Using Transfer Learning to Identify Arctic Wildlife
    Data Augmentation
      Image Augmentation with ImageDataGenerator

      Image Augmentation with Augmentation Layers
      Applying Image Augmentation to Arctic Wildlife
    Global Pooling
    Audio Classification with CNNs
    Summary
  11. Face Detection and Recognition
    Face Detection
      Face Detection with Viola-Jones
      Using the OpenCV Implementation of Viola-Jones
      Face Detection with Convolutional Neural Networks
      Extracting Faces from Photos
    Facial Recognition
      Applying Transfer Learning to Facial Recognition
      Boosting Transfer Learning with Task-Specific Weights
      ArcFace
    Putting It All Together: Detecting and Recognizing Faces in Photos
    Handling Unknown Faces: Closed-Set Versus Open-Set Classification
    Summary
  12. Object Detection
    R-CNNs
    Mask R-CNN
    YOLO
    YOLOv3 and Keras
    Custom Object Detection
      Training a Custom Object Detection Model with the Custom Vision Service
      Using the Exported Model
    Summary
  13. Natural Language Processing
    Text Preparation
    Word Embeddings
    Text Classification
      Automating Text Vectorization
      Using TextVectorization in a Sentiment Analysis Model
      Factoring Word Order into Predictions
      Recurrent Neural Networks (RNNs)
      Using Pretrained Models to Classify Text
    Neural Machine Translation
      LSTM Encoder-Decoders
      Transformer Encoder-Decoders
      Building a Transformer-Based NMT Model
      Using Pretrained Models to Translate Text
    Bidirectional Encoder Representations from Transformers (BERT)
      Building a BERT-Based Question Answering System
      Fine-Tuning BERT to Perform Sentiment Analysis
    Summary
  14. Azure Cognitive Services
    Introducing Azure Cognitive Services
      Keys and Endpoints
      Calling Azure Cognitive Services APls
      Azure Cognitive Services Containers

    The Computer Vision Service
    The Language Service
    The Translator Service
    The Speech Service
    Putting It All Together: Contoso Travel
    Summary
Index

内容大纲

作者介绍

目录

同类热销排行榜

推荐书目