Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines - Image 1

£44.99

Packt Publishing Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines

Name: Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines
Brand: Packt Publishing
Price: 44.16 GBP
Availability: InStock

AI & Machine Learning

Price History & Forecast

Last 28 days • 28 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 28 days • 2 price levels

Days at Price

Current Price

Price Analysis

Most common price: £44 (24 days, 85.7%)

Price range: £44 - £45

Price levels: 2 different prices over 28 days

Description

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 Description C++ 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. Table of Contents Introduction to Machine Learning with C++ Data Processing Measuring Performance and Selecting Models Clustering Anomaly Detection Dimensionality Reduction Classification Recommender Systems Ensemble Learning Neural Networks for Image Classification Sentiment Analysis with Recurrent Neural Networks Exporting and Importing Models Deploying Models on Mobile and Cloud Platforms

Product Specifications

Brand: Packt Publishing
Format: Paperback
ASIN: 1789955335
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 15 May 2020
Listed Since: 04 May 2020

Barcode

No barcode data available

Packt Publishing Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

A Greater Foundation for Machine Learning Engineering: The Hallmarks of the Great Beyond in Pytorch, R, Tensorflow, and Python

Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-world image applications

Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools, and Applications

Machine Learning Hero: Master Data Science with Python Essentials: Machine Learning with Python Hands-On Guide from Beginner to Expert: 1 (Mastering the AI Revolution)

Implementing Useful Algorithms in C++

Machine Learning with CUDA: Enhancing Neural Network Performance (GPU Mastery Series: Unlocking CUDA's Power using pyCUDA)

Scala for Machine Learning - Second Edition: Build systems for data processing, machine learning, and deep learning

Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow

Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

Assessing and Improving Prediction and Classification: Theory and Algorithms in C++

MATLAB for Machine Learning: Practical examples of regression, clustering and neural networks

Deep Learning and AI Superhero: Mastering TensorFlow, Keras, and PyTorch: Advanced Machine Learning and AI, Neural Networks, and Real-World Projects (Mastering the AI Revolution)

The Machine Learning Solutions Architect Handbook: Create machine learning platforms to run solutions in an enterprise setting

Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features

Pattern Recognition with Neural Networks in C++

scikit-learn : Machine Learning Simplified: Implement scikit-learn into every step of the data science pipeline

Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks (CNN, DNN, GNN, RNN, ANN, LSTM, GAN) to Natural Language Processing (NLP)

Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks (CNN, DNN, GNN, RNN, ANN, LSTM, GAN) to Natural Language Processing (NLP)

Machine Learning and its Applications

Machine Learning With Go: Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language

Modern Computer Vision with PyTorch: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch