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E-raamat: Practical Machine Learning with Rust: Creating Intelligent Applications in Rust

  • Formaat: PDF+DRM
  • Ilmumisaeg: 10-Dec-2019
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484251218
  • Formaat - PDF+DRM
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  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Formaat: PDF+DRM
  • Ilmumisaeg: 10-Dec-2019
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484251218

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Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, youll dive into the more specific fields of machine learning, such as computer vision and natural language processing, and look at the Rust libraries that help create applications for those domains. We will also look at how to deploy these applications either on site or over the cloud.





After reading Practical Machine Learning with Rust, you will have a solid understanding of creating high computation libraries using Rust. Armed with the knowledge of this amazing language, you will be able to create applications that are more performant, memory safe, and less resource heavy.





 





What You Will Learn









Write machine learning algorithms in Rust Use Rust libraries for different tasks in machine learning Create concise Rust packages for your machine learning applications Implement NLP and computer vision in Rust Deploy your code in the cloud and on bare metal servers

























 





Who This Book Is For 





Machine learning engineers and software engineers interested in building machine learning applications in Rust.
About the Author xi
Acknowledgments xiii
Introduction xv
Chapter 1 Basics of Rust
1(30)
1.1 Why Rust?
1(1)
1.2 A Better Reference
2(3)
1.3 Rust Installation
5(2)
1.4 Package Manager and Cargo
7(1)
1.5 Creating New Applications in Rust
7(2)
1.6 Variables in Rust
9(4)
1.6.1 Mutation and Shadowing
11(2)
1.6.2 Variable Scoping
13(1)
1.7 Data Types
13(1)
1.8 Functions
14(1)
1.9 Conditions
15(2)
1.9.1 If Conditions
15(1)
1.9.2 Pattern Matching
16(1)
1.10 References and Borrowing
17(5)
1.10.1 Mutable References
20(2)
1.11 Object-Oriented Programming
22(5)
1.11.1 Structures
22(1)
1.11.2 Traits
23(1)
1.11.3 Methods and impl
24(2)
1.11.4 Enumerations
26(1)
1.12 Writing Tests
27(1)
1.13 Summary
28(1)
1.14 References
29(2)
Chapter 2 Supervised Learning
31(76)
2.1 What Is Machine Learning?
31(1)
2.2 Dataset Specific Code
32(9)
2.3 Rusty_Machine Library
41(1)
2.4 Linear Regression
42(10)
2.5 Gaussian Process
52(2)
2.6 Generalized Linear Models
54(3)
2.7 Evaluation of Regression Models
57(4)
2.7.1 MAE and MSE
57(2)
2.7.2 R-Squared Error
59(2)
2.8 Classification Algorithms
61(41)
2.8.1 Iris Dataset
62(5)
2.8.2 Logistic Regression
67(1)
2.8.3 Decision Trees
68(2)
2.8.4 Random Forest
70(2)
2.8.5 XGBoost
72(5)
2.8.6 Support Vector Machines
77(2)
2.8.7 K Nearest Neighbors
79(5)
2.8.8 Neural Networks
84(10)
2.8.9 Model Evaluation
94(8)
2.9 Conclusion
102(1)
2.10 Bibliography
102(5)
Chapter 3 Unsupervised and Reinforcement Learning
107(34)
3.1 K-Means Clustering
108(4)
3.2 Gaussian Mixture Model
112(7)
3.3 Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
119(2)
3.4 Principal Component Analysis
121(2)
3.5 Testing an Unsupervised Model
123(4)
3.6 Reinforcement Learning
127(10)
3.7 Conclusion
137(1)
3.8 Bibliography
137(4)
Chapter 4 Working with Data
141(46)
4.1 JSON
141(8)
4.2 XML
149(5)
4.3 Scraping
154(4)
4.4 SQL
158(8)
4.5 NoSQL
166(6)
4.6 Data on s3
172(6)
4.7 Data Transformations
178(5)
4.8 Working with Matrices
183(3)
4.9 Conclusion
186(1)
4.10 Bibliography
186(1)
Chapter 5 Natural Language Processing
187(42)
5.1 Sentence Classification
188(13)
5.2 Named Entity Recognition
201(12)
5.3 Chatbots and Natural Language Understanding (NLU)
213(14)
5.3.1 Building an Inference Engine
219(8)
5.4 Conclusion
227(2)
Chapter 6 Computer Vision
229(48)
6.1 Image Classification
229(25)
6.1.1 Convolutional Neural Networks (CNN)
230(2)
6.1.2 Rust and Torch
232(1)
6.1.3 Torch Dataset
232(8)
6.1.4 CNN Model
240(6)
6.1.5 Model Building and Debugging
246(3)
6.1.6 Pretrained Models
249(5)
6.2 Transfer Learning
254(10)
6.2.1 Training
256(1)
6.2.2 Neural Style Transfer
257(7)
6.3 Tensorflow and Face Detection
264(11)
6.4 Conclusion
275(1)
6.5 Bibliography
276(1)
Chapter 7 Machine Learning Domains
277(38)
7.1 Statistical Analysis
277(13)
7.2 Writing High Performance Code
290(4)
7.3 Recommender Systems
294(18)
7.3.1 Command Line
296(3)
7.3.2 Downloading Data
299(1)
7.3.3 Data
300(2)
7.3.4 Model Building
302(5)
7.3.5 Model Prediction
307(5)
7.4 Conclusion
312(1)
7.5 Bibliography
313(2)
Chapter 8 Using Rust Applications
315(32)
8.1 Rust Plug-n-Play
315(21)
8.1.1 Python
316(11)
8.1.2 Java
327(9)
8.2 Rust in the Cloud
336(10)
8.3 Conclusion
346(1)
8.4 Bibliography
346(1)
Index 347
Joydeep Bhattacharjee is a machine learning engineer. He likes creating software tools and processes with a focus on clean code. He is a huge believer in tech and the ability of tech to move the world forward. His expertise includes data exploration, statistical modeling, machine learning algorithms, and data visualization. His is currently working at Nineleaps as a principal engineer.