C Programming For Machine Learning Applications

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C Machine Learning

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Author : Phil Culliton
language : en
Publisher:
Release Date : 2017-12-29

C Machine Learning written by Phil Culliton and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-29 with categories.

Get introduced to the concepts of Machine Learning and build efficient data models in C++About This Book* 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.Who This Book Is ForThe target audience is C++ developers who want to get into machine learning, or knowledgeable ML programmers who don't know C++ well but want to use it, and libraries written in it, in their work. The reader should be conversant with at least one programming language, and have some familiarity with strongly-typed languages and vectors/matrices.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 MR4CIn DetailMachine 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.

Deep Learning For Coders With Fastai And Pytorch

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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29

Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Machine Learning Applications In Software Engineering

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Author : Du Zhang
language : en
Publisher: World Scientific
Release Date : 2005

Machine Learning Applications In Software Engineering written by Du Zhang and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.

A collection of previously published articles from a variety of publications.

Hands On Machine Learning With C

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Author : Kirill Kolodiazhnyi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-05-15

Hands On Machine Learning With C written by Kirill Kolodiazhnyi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-15 with Computers categories.

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 Applications In Software Engineering

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Author : Du Zhang
language : en
Publisher: World Scientific
Release Date : 2005

Machine Learning Applications In Software Engineering written by Du Zhang and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.

A collection of previously published articles from a variety of publications.

Machine Learning Applications

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Author : Indranath Chatterjee
language : en
Publisher: John Wiley & Sons
Release Date : 2023-12-08

Machine Learning Applications written by Indranath Chatterjee and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-08 with Computers categories.

Machine Learning Applications Practical resource on the importance of Machine Learning and Deep Learning applications in various technologies and real-world situations Machine Learning Applications discusses methodological advancements of machine learning and deep learning, presents applications in image processing, including face and vehicle detection, image classification, object detection, image segmentation, and delivers real-world applications in healthcare to identify diseases and diagnosis, such as creating smart health records and medical imaging diagnosis, and provides real-world examples, case studies, use cases, and techniques to enable the reader’s active learning. Composed of 13 chapters, this book also introduces real-world applications of machine and deep learning in blockchain technology, cyber security, and climate change. An explanation of AI and robotic applications in mechanical design is also discussed, including robot-assisted surgeries, security, and space exploration. The book describes the importance of each subject area and detail why they are so important to us from a societal and human perspective. Edited by two highly qualified academics and contributed to by established thought leaders in their respective fields, Machine Learning Applications includes information on: Content based medical image retrieval (CBMIR), covering face and vehicle detection, multi-resolution and multisource analysis, manifold and image processing, and morphological processing Smart medicine, including machine learning and artificial intelligence in medicine, risk identification, tailored interventions, and association rules AI and robotics application for transportation and infrastructure (e.g., autonomous cars and smart cities), along with global warming and climate change Identifying diseases and diagnosis, drug discovery and manufacturing, medical imaging diagnosis, personalized medicine, and smart health records With its practical approach to the subject, Machine Learning Applications is an ideal resource for professionals working with smart technologies such as machine and deep learning, AI, IoT, and other wireless communications; it is also highly suitable for professionals working in robotics, computer vision, cyber security and more.

Python Programming For Machine Learning

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Author : Dr.A.Kalpana
language : en
Publisher: Leilani Katie Publication
Release Date : 2024-08-28

Python Programming For Machine Learning written by Dr.A.Kalpana and has been published by Leilani Katie Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-28 with Computers categories.

Dr.A.Kalpana, Assistant Professor, Department of Computer Applications, Agurchand Manmull Jain College, Chennai, Tamil Nadu, India. Mrs.S.Sree Priya, Assistant Professor, Department of Computer Applications (BCA), Guru Nanak College (Autonomous), Velachery, Chennai, Tamil Nadu, India. Dr.K.Sivakami, Associate Professor and Head, Department of Computer Science, Nadar Saraswathi College of Arts and Science (Autonomous), Theni, Tamil Nadu, India.

Advances In Machine Learning Applications In Software Engineering

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Author : Zhang, Du
language : en
Publisher: IGI Global
Release Date : 2006-10-31

Advances In Machine Learning Applications In Software Engineering written by Zhang, Du and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-10-31 with Computers categories.

"This book provides analysis, characterization and refinement of software engineering data in terms of machine learning methods. It depicts applications of several machine learning approaches in software systems development and deployment, and the use of machine learning methods to establish predictive models for software quality while offering readers suggestions by proposing future work in this emerging research field"--Provided by publisher.

Artificial Intelligence With Python

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Author : Prateek Joshi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-01-27

Artificial Intelligence With Python written by Prateek Joshi and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-27 with Computers categories.

Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.

Machine Learning For Kids

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Author : Dale Lane
language : en
Publisher: No Starch Press
Release Date : 2021-02-09

Machine Learning For Kids written by Dale Lane and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-09 with Computers categories.

A hands-on, application-based introduction to machine learning and artificial intelligence (AI). Create compelling AI-powered games and applications using the Scratch programming language. AI Made Easy with 13 Projects Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence. AI is based on the idea that computers can learn on their own, with your help. Machine Learning for Kids will introduce you to machine learning, painlessly. With this book and its free, Scratch-based companion website, you’ll see how easy it is to add machine learning to your own projects. You don’t even need to know how to code! Step by easy step, you’ll discover how machine learning systems can be taught to recognize text, images, numbers, and sounds, and how to train your models to improve them. You’ll turn your models into 13 fun computer games and apps, including: A Rock, Paper, Scissors game that recognizes your hand shapes A computer character that reacts to insults and compliments An interactive virtual assistant (like Siri or Alexa) A movie recommendation app An AI version of Pac-Man There’s no experience required and step-by-step instructions make sure that anyone can follow along! No Experience Necessary! Ages 12+