C++ Machine Learning by Phil Culliton
C++ Machine Learning by Phil Culliton PDF, ePub eBook D0wnl0ad
Key Features
- Get introduced to the concepts of Machine Learning and see how you can implement them in C++, and build efficient data models for training data using popular libraries such as mlpack and Shark
- A detailed guide packed with real-life examples to help you build a solid understanding of Machine Learning.
Book Description
Machine Learning tasks are CPU time-consuming. C++ outperforms any other programming language by allowing access to programming constructs to optimize CPU-based number crunching, precision, and memory management normally abstracted away in higher-level languages.
This book aims to address the challenges associated with C++ machine learning by introducing you to several useful libraries (mlpack, Shogun, and so on); you'll producing a library of your own code along the way that should make common tasks more straightforward.
We begin with a review of the basic concepts you will need to know or brush up on before going further, including math and an intro to the C++ style we'll be using throughout the book. We then deal with the fundamentals of ML—how to handle input, the basic algorithms, and sample cases where the basic algorithms succeed or fail. This is followed by more advanced topics such as complex algorithms, regularization, optimization, and visualizing and understanding data, referring back to earlier work consistently so that you can see the mountains move. We'll then touch upon topics of current interest: computer vision (including sections on CUDA and "deep" learning), natural language processing, and handling very large datasets.
The journey ends with a coda: we go back through the original sample cases, applying what we've learned along the way to rectify the issues we ran into initially.
What you will learn
- Model relationships in your data using supervised learning
- Uncover insights using clustering and t-SNE
- Use ensemble and stack to create more powerful models
- Use cuda-convnet and deep learning to solve image recognition problems
- Build an end-to-end pipeline that turns what you learn into practical, ready-to-use software
- Solve big data problems using Hadoop and Google’s MR4C
About the Author
Phil Culliton has been architecting and building machine learning products since before the dawn of time, before carnivorous whales crawled from the oceans and evolved into Visual Studio and its C compiler, before Python was anything more than a snake to be terrified of. Happily, coding replenishes him like some infernal machine, so, despite being awfully close to 40, building applications still makes him feel like an 18-year-old cracking open Knuth for the first time: excited, confused, and slightly gassy. He lives with his wife and kids in a pleasant town in upstate New York.
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