面向工程師的實用機器學習和AI (影印版) 9787576606577 (美)Jeff Prosise

*完成訂單後正常情形下約兩周可抵台。
*本賣場提供之資訊僅供參考，以到貨標的為正確資訊。
印行年月：202303*若逾兩年請先於客服中心或Line洽詢存貨情況，謝謝。
台灣(台北市)在地出版社，每筆交易均開具統一發票，祝您中獎最高1000萬元。
書名：面向工程師的實用機器學習和AI (影印版)
ISBN：9787576606577
出版社：東南大學
著編譯者：(美)Jeff Prosise
頁數：400
所在地：中國大陸 *此為代購商品
書號：1505326
可大量預訂，請先連絡。

內容簡介

許多AI入門指南可以說都是變相的微積分書籍，但這本書基本上避開了數學。作者Jeff Prosise幫助工程師和軟體開發人員建立了對AI的直觀理解，以解決商業問題。需要創建一個系統來檢測雨林中非法砍伐的聲音、分析文本的情感或預測旋轉機械的早期故障？這本實踐用書將教你把AI和機器學習應用於職場工作所需的技能。 書中的示例和插圖來自於Prosise在全球多家公司和研究機構教授的AI和機器學習課程。不說廢話，也沒有可怕的公式——純粹就是寫給工程師和軟體開發人員的快速入門，並附有實際操作的例子。 本書將幫助你： ·學習什麼是機器學習和深度學習及其用途 ·理解流行的機器學習演算法原理及其應用場景 ·使用Scikit-Learn在Python中構建機器學習模型，使用Keras和TensorFlow構建神經網絡 ·訓練回歸模型以及二元和多元分類模型並給其評分 ·構建面部識別模型和目標檢測模型 ·構建能夠響應自然語言查詢並將文本翻譯成其他語言的語言模型 ·使用認知服務將AI融入你編寫的應用程序中

作者簡介

傑夫·普洛西(Jeff Prosise)是一名工程師，熱衷於向工程師和軟體開發人員介紹AI 和機器學習的種種神奇之處。作為Wintellect的聯合創始人，他已經在微軟培訓了數千名開發人員，並在一些全球最大規模的軟體會議上發表過演講。此外，Jeff在橡樹嶺國家實驗室和勞倫斯利弗莫爾國家實驗室從事高功率激光系統和聚變能源研究。他目前擔任Atmosera的首席學習官，幫助客戶將AI融入他們的產品。

目錄

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 Unknow
詳細資料或其他書籍請至台灣高等教育出版社查詢，查後請於客服中心或Line或本社留言板留言，我們即儘速上架。

大陸簡體正版圖書，訂購後正常情形下約兩周可抵台。