舉報

會員
The Applied TensorFlow and Keras Workshop
Machinelearninggivescomputerstheabilitytolearnlikehumans.Itisbecomingincreasinglytransformationaltobusinessesinmanyforms,andakeyskilltolearntoprepareforthefuturedigitaleconomy.Asabeginner,you’llunlockaworldofopportunitiesbylearningthetechniquesyouneedtocontributetothedomainsofmachinelearning,deeplearning,andmoderndataanalysisusingthelatestcutting-edgetools.TheAppliedTensorFlowandKerasWorkshopbeginsbyshowingyouhowneuralnetworkswork.Afteryou’veunderstoodthebasics,youwilltrainafewnetworksbyalteringtheirhyperparameters.Tobuildonyourskills,you’lllearnhowtoselectthemostappropriatemodeltosolvetheprobleminhand.Whiletacklingadvancedconcepts,you’lldiscoverhowtoassembleadeeplearningsystembybringingtogetheralltheessentialelementsnecessaryforbuildingabasicdeeplearningsystem-data,model,andprediction.Finally,you’llexplorewaystoevaluatetheperformanceofyourmodel,andimproveitusingtechniquessuchasmodelevaluationandhyperparameteroptimization.Bytheendofthisbook,you'llhavelearnedhowtobuildaBitcoinappthatpredictsfutureprices,andbeabletobuildyourownmodelsforotherprojects.
目錄(30章)
倒序
- 封面
- 版權信息
- Preface
- 1. Introduction to Neural Networks and Deep Learning
- Introduction
- What are Neural Networks?
- Why Do Neural Networks Work So Well?
- Configuring a Deep Learning Environment
- Exploring a Trained Neural Network
- Summary
- 2. Real-World Deep Learning: Predicting the Price of Bitcoin
- Introduction
- Choosing the Right Model Architecture
- Structuring Your Problem
- Using Keras as a TensorFlow Interface
- From Data Preparation to Modeling
- Summary
- 3. Real-World Deep Learning: Evaluating the Bitcoin Model
- Introduction
- Hyperparameter Optimization
- Summary
- 4. Productization
- Introduction
- Handling New Data
- Summary
- Appendix
- Chapter 01: Introduction to Neural Networks and Deep Learning
- Chapter 02: Real-World Deep Learning: Predicting the Price of Bitcoin
- Chapter 3: Real-World Deep Learning: Evaluating the Bitcoin Model
- Chapter 4: Productization 更新時間:2021-06-11 18:57:27
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