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The Deep Learning with Keras Workshop
Newexperiencescanbeintimidating,butnotthisone!Thisbeginner’sguidetodeeplearningisheretohelpyouexploredeeplearningfromscratchwithKeras,andbeonyourwaytotrainingyourfirsteverneuralnetworks.WhatsetsKerasapartfromotherdeeplearningframeworksisitssimplicity.Withovertwohundredthousandusers,Kerashasastrongeradoptioninindustryandtheresearchcommunitythananyotherdeeplearningframework.TheDeepLearningwithKerasWorkshopstartsbyintroducingyoutothefundamentalconceptsofmachinelearningusingthescikit-learnpackage.Afterlearninghowtoperformthelineartransformationsthatarenecessaryforbuildingneuralnetworks,you'llbuildyourfirstneuralnetworkwiththeKeraslibrary.Asyouadvance,you'lllearnhowtobuildmulti-layerneuralnetworksandrecognizewhenyourmodelisunderfittingoroverfittingtothetrainingdata.Withthehelpofpracticalexercises,you’lllearntousecross-validationtechniquestoevaluateyourmodelsandthenchoosetheoptimalhyperparameterstofine-tunetheirperformance.Finally,you’llexplorerecurrentneuralnetworksandlearnhowtotrainthemtopredictvaluesinsequentialdata.Bytheendofthisbook,you'llhavedevelopedtheskillsyouneedtoconfidentlytrainyourownneuralnetworkmodels.
目錄(71章)
倒序
- 封面
- 版權(quán)信息
- Preface
- 1. Introduction to Machine Learning with Keras
- Introduction
- Data Representation
- Data Preprocessing
- Life Cycle of Model Creation
- scikit-learn
- Keras
- Model Training
- Model Tuning
- Summary
- 2. Machine Learning versus Deep Learning
- Introduction
- Linear Transformations
- Introduction to Keras
- Summary
- 3. Deep Learning with Keras
- Introduction
- Building Your First Neural Network
- Model Evaluation
- Summary
- 4. Evaluating Your Model with Cross-Validation Using Keras Wrappers
- Introduction
- Cross-Validation
- Cross-Validation for Deep Learning Models
- Model Selection with Cross-Validation
- Summary
- 5. Improving Model Accuracy
- Introduction
- Regularization
- L1 and L2 Regularization
- Dropout Regularization
- Other Regularization Methods
- Hyperparameter Tuning with scikit-learn
- Summary
- 6. Model Evaluation
- Introduction
- Accuracy
- Imbalanced Datasets
- Confusion Matrix
- Summary
- 7. Computer Vision with Convolutional Neural Networks
- Introduction
- Computer Vision
- Convolutional Neural Networks
- The Architecture of a CNN
- Image Augmentation
- Summary
- 8. Transfer Learning and Pre-Trained Models
- Introduction
- Pre-Trained Sets and Transfer Learning
- Fine-Tuning a Pre-Trained Network
- Summary
- 9. Sequential Modeling with Recurrent Neural Networks
- Introduction
- Sequential Memory and Sequential Modeling
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM)
- Summary
- Appendix
- 1. Introduction to Machine Learning with Keras
- 2. Machine Learning versus Deep Learning
- 3. Deep Learning with Keras
- 4. Evaluating Your Model with Cross-Validation Using Keras Wrappers
- 5. Improving Model Accuracy
- 6. Model Evaluation
- 7. Computer Vision with Convolutional Neural Networks
- 8. Transfer Learning and Pre-Trained Models
- 9. Sequential Modeling with Recurrent Neural Networks 更新時(shí)間：2021-06-18 18:13:53
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