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Advanced Machine Learning with Python
最新章節:
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ThistitleisforPythondevelopersandanalystsordatascientistswhoarelookingtoaddtotheirexistingskillsbyaccessingsomeofthemostpowerfulrecenttrendsindatascience.Ifyou’veeverconsideredbuildingyourownimageortext-taggingsolution,orofenteringaKagglecontestforinstance,thisbookisforyou!PriorexperienceofPythonandgroundinginsomeofthecoreconceptsofmachinelearningwouldbehelpful.
目錄(65章)
倒序
- 封面
- 版權頁
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- eBooks discount offers and more
- Preface
- What is advanced machine learning?
- What should you expect from this book?
- What this book covers
- What you need for this book
- Who this book is for
- Conventions
- Reader feedback
- Customer support
- Chapter 1. Unsupervised Machine Learning
- Principal component analysis
- Introducing k-means clustering
- Self-organizing maps
- Further reading
- Summary
- Chapter 2. Deep Belief Networks
- Neural networks – a primer
- Restricted Boltzmann Machine
- Deep belief networks
- Further reading
- Summary
- Chapter 3. Stacked Denoising Autoencoders
- Autoencoders
- Stacked Denoising Autoencoders
- Further reading
- Summary
- Chapter 4. Convolutional Neural Networks
- Introducing the CNN
- Further Reading
- Summary
- Chapter 5. Semi-Supervised Learning
- Introduction
- Understanding semi-supervised learning
- Semi-supervised algorithms in action
- Further reading
- Summary
- Chapter 6. Text Feature Engineering
- Introduction
- Text feature engineering
- Further reading
- Summary
- Chapter 7. Feature Engineering Part II
- Introduction
- Creating a feature set
- Feature engineering in practice
- Further reading
- Summary
- Chapter 8. Ensemble Methods
- Introducing ensembles
- Using models in dynamic applications
- Further reading
- Summary
- Chapter 9. Additional Python Machine Learning Tools
- Alternative development tools
- Further reading
- Summary
- Appendix A. Chapter Code Requirements
- Index 更新時間:2021-07-14 10:53:32
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