C Machine Learning
Download C Machine Learning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get C Machine Learning book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
MACHINE LEARNING
Note: Anyone can request the PDF version of this practice set/workbook by emailing me at [email protected]. You can also get full PDF books in quiz format on our youtube channel https://www.youtube.com/@SmartQuizWorld-n2q .. I will send you a PDF version of this workbook. This book has been designed for candidates preparing for various competitive examinations. It contains many objective questions specifically designed for different exams. Answer keys are provided at the end of each page. It will undoubtedly serve as the best preparation material for aspirants. This book is an engaging quiz eBook for all and offers something for everyone. This book will satisfy the curiosity of most students while also challenging their trivia skills and introducing them to new information. Use this invaluable book to test your subject-matter expertise. Multiple-choice exams are a common assessment method that all prospective candidates must be familiar with in today?s academic environment. Although the majority of students are accustomed to this MCQ format, many are not well-versed in it. To achieve success in MCQ tests, quizzes, and trivia challenges, one requires test-taking techniques and skills in addition to subject knowledge. It also provides you with the skills and information you need to achieve a good score in challenging tests or competitive examinations. Whether you have studied the subject on your own, read for pleasure, or completed coursework, it will assess your knowledge and prepare you for competitive exams, quizzes, trivia, and more.
Hands-On Machine Learning with C++
Author: Kirill Kolodiazhnyi
language: en
Publisher: Packt Publishing Ltd
Release Date: 2020-05-15
Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets Key Features Become familiar with data processing, performance measuring, and model selection using various C++ libraries Implement practical machine learning and deep learning techniques to build smart models Deploy machine learning models to work on mobile and embedded devices Book DescriptionC++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This book makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples. This book will get you hands-on with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You’ll cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you’ll explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you’ll learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format. By the end of this C++ book, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.What you will learn Explore how to load and preprocess various data types to suitable C++ data structures Employ key machine learning algorithms with various C++ libraries Understand the grid-search approach to find the best parameters for a machine learning model Implement an algorithm for filtering anomalies in user data using Gaussian distribution Improve collaborative filtering to deal with dynamic user preferences Use C++ libraries and APIs to manage model structures and parameters Implement a C++ program to solve image classification tasks with LeNet architecture Who this book is for You will find this C++ machine learning book useful if you want to get started with machine learning algorithms and techniques using the popular C++ language. As well as being a useful first course in machine learning with C++, this book will also appeal to data analysts, data scientists, and machine learning developers who are looking to implement different machine learning models in production using varied datasets and examples. Working knowledge of the C++ programming language is mandatory to get started with this book.
Machine Learning with R Quick Start Guide
Author: Iván Pastor Sanz
language: en
Publisher: Packt Publishing Ltd
Release Date: 2019-03-29
Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more. Key FeaturesUse R 3.5 to implement real-world examples in machine learningImplement key machine learning algorithms to understand the working mechanism of smart modelsCreate end-to-end machine learning pipelines using modern libraries from the R ecosystemBook Description Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline. From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling. By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R. What you will learnIntroduce yourself to the basics of machine learning with R 3.5Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your resultsLearn to build predictive models with the help of various machine learning techniquesUse R to visualize data spread across multiple dimensions and extract useful featuresUse interactive data analysis with R to get insights into dataImplement supervised and unsupervised learning, and NLP using R librariesWho this book is for This book is for graduate students, aspiring data scientists, and data analysts who wish to enter the field of machine learning and are looking to implement machine learning techniques and methodologies from scratch using R 3.5. A working knowledge of the R programming language is expected.