舉報 
會員
The Applied Artificial Intelligence Workshop
Youalreadyknowthatartificialintelligence(AI)andmachinelearning(ML)arepresentinmanyofthetoolsyouuseinyourdailyroutine.ButdoyouwanttobeabletocreateyourownAIandMLmodelsanddevelopyourskillsinthesedomainstokickstartyourAIcareer?TheAppliedArtificialIntelligenceWorkshopgetsyoustartedwithapplyingAIwiththehelpofpracticalexercisesandusefulexamples,allputtogethercleverlytohelpyougaintheskillstotransformyourcareer.Thebookbeginsbyteachingyouhowtopredictoutcomesusingregression.You’llthenlearnhowtoclassifydatausingtechniquessuchask-nearestneighbor(KNN)andsupportvectormachine(SVM)classifiers.Asyouprogress,you'llexplorevariousdecisiontreesbylearninghowtobuildareliabledecisiontreemodelthatcanhelpyourcompanyfindcarsthatclientsarelikelytobuy.Thefinalchapterswillintroduceyoutodeeplearningandneuralnetworks.Throughvariousactivities,suchaspredictingstockpricesandrecognizinghandwrittendigits,you'lllearnhowtotrainandimplementconvolutionalneuralnetworks(CNNs)andrecurrentneuralnetworks(RNNs).BytheendofthisappliedAIbook,you'llhavelearnedhowtopredictoutcomesandtrainneuralnetworksandbeabletousevarioustechniquestodevelopAIandMLmodels.
目錄(65章)
倒序
- 封面
- 版權信息
- Preface
- 1. Introduction to Artificial Intelligence
- Introduction
- Fields and Applications of AI
- AI Tools and Learning Models
- The Role of Python in AI
- Python for Game AI
- Heuristics
- Pathfinding with the A* Algorithm
- Introducing the A* Algorithm
- Game AI with the Minmax Algorithm and Alpha-Beta Pruning
- The Minmax Algorithm
- DRYing Up the Minmax Algorithm – the NegaMax Algorithm
- Summary
- 2. An Introduction to Regression
- Introduction
- Linear Regression with One Variable
- Linear Regression with Multiple Variables
- Polynomial and Support Vector Regression
- Support Vector Regression
- Summary
- 3. An Introduction to Classification
- Introduction
- The Fundamentals of Classification
- Data Preprocessing
- The K-Nearest Neighbors Classifier
- Classification with Support Vector Machines
- Summary
- 4. An Introduction to Decision Trees
- Introduction
- Decision Trees
- The Confusion Matrix
- Random Forest Classifier
- Summary
- 5. Artificial Intelligence: Clustering
- Introduction
- Defining the Clustering Problem
- Clustering Approaches
- The K-Means Algorithm
- The Mean Shift Algorithm
- Clustering Performance Evaluation
- Summary
- 6. Neural Networks and Deep Learning
- Introduction
- Artificial Neurons
- Neurons in TensorFlow
- Neural Network Architecture
- Activation Functions
- Forward Propagation and the Loss Function
- Backpropagation
- Optimizers and the Learning Rate
- Regularization
- Deep Learning
- Computer Vision and Image Classification
- Recurrent Neural Networks (RNNs)
- Summary
- Appendix
- 1. Introduction to Artificial Intelligence
- 2. An Introduction to Regression
- 3. An Introduction to Classification
- 4. An Introduction to Decision Trees
- 5. Artificial Intelligence: Clustering
- 6. Neural Networks and Deep Learning 更新時間：2021-06-18 18:25:27
推薦閱讀
- Learning SQL Server Reporting Services 2012
- 觸摸屏實用技術與工程應用
- Creating Dynamic UI with Android Fragments
- Mastering Delphi Programming：A Complete Reference Guide
- Unity 5.x Game Development Blueprints
- INSTANT ForgedUI Starter
- Mastering Manga Studio 5
- 微服務分布式架構基礎與實戰：基于Spring Boot + Spring Cloud
- 筆記本電腦應用技巧
- Intel Edison智能硬件開發指南：基于Yocto Project
- The Deep Learning with PyTorch Workshop
- Learning Less.js
- 從企業級開發到云原生微服務：Spring Boot實戰
- 零基礎輕松學修電腦主板
- Hands-On One-shot Learning with Python
- Nagios系統監控實踐（原書第2版）
- 電腦組裝與硬件維修從入門到精通
- 微處理器及控制電路識圖
- SOA架構：服務和微服務分析及設計（原書第2版）
- 實戰Alibaba Sentinel：深度解析微服務高并發流量治理
- 多媒體技術教程
- Arduino項目開發：物聯網應用
- Vue.js 3 Cookbook
- Building Smart LEGO MINDSTORMS EV3 Robots
- 玩轉3D打印
- Getting started with IntelliJ IDEA
- 電腦組裝與維修從入門到精通
- 計算機主板維修不是事兒（第2版）
- 零基礎輕松學修筆記本電腦
- Spring Boot+Spring Cloud微服務開發實戰
