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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
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