9788197950483 - practical c++ machine learning: hands-on strategies for developing simple machine learning models using c++ data structures and libraries di sutherland, anais (16 risultati)
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Paperback or Softback. Condizione: New. Practical C++ Machine Learning: Hands-on strategies for developing simple machine learning models using C++ data structures and libraries. Book.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Practical C++ Machine Learning introduces C++ programmers to the world of machine learning. If you know C++ but haven't worked with machine learning solutions before, this book is a good place to start learning the basics and exper…imenting with the language's essential concepts and techniques.The book starts off by showing you how to set up a development environment and put together some basic neural networks using the Flashlight library. It then covers essential tasks like data preprocessing, model training, and evaluation, with practical examples that show how machine learning works in a C++ context. You will also learn strategies for dealing with common problems like overfitting and performance optimization. The next few chapters get into more complex topics like convolutional neural networks, model deployment, and some key performance tuning techniques. This will help you develop and integrate your own models into applications.By the end of the book, you will have essential hands-on experience and a better clarity to explore and expand your machine learning knowledge in C++. This book doesn't aim to cover everything, but it does serve as a good starting point for you to confidently dive into the world of machine learning and deep learning.Key LearningsUse Flashlight to set up a C++ environment for machine learning projects.Implement neural networks from scratch to gain a hands-on understanding.Preprocess and augment data effectively to improve model performance.Train and evaluate models using appropriate loss functions and metrics.Explore overfitting challenges with techniques like regularization and dropout.Build advanced architectures like ResNet.Apply transfer learning to leverage pre-trained models.Deploy models and integrate them into real-world C++ apps.Implement real-time inference with optimized performance.Improve performance using GPU acceleration and multi-threading techniques.Table of ContentGetting Started with C++ Machine LearningData Handling and PreprocessingBuilding a Simple Neural NetworkTraining Deep Neural NetworksConvolutional Neural NetworksImproving Model PerformanceAdvanced Neural Network ArchitecturesDeployment and IntegrationParallelism and Performance Scaling 176 pp. Englisch.
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Practical C++ Machine Learning introduces C++ programmers to the world of machine learning. If you know C++ but haven't worked with machine learning solutions before, this book is a good place to start learning the basics and experimen…ting with the language's essential concepts and techniques.The book starts off by showing you how to set up a development environment and put together some basic neural networks using the Flashlight library. It then covers essential tasks like data preprocessing, model training, and evaluation, with practical examples that show how machine learning works in a C++ context. You will also learn strategies for dealing with common problems like overfitting and performance optimization. The next few chapters get into more complex topics like convolutional neural networks, model deployment, and some key performance tuning techniques. This will help you develop and integrate your own models into applications.By the end of the book, you will have essential hands-on experience and a better clarity to explore and expand your machine learning knowledge in C++. This book doesn't aim to cover everything, but it does serve as a good starting point for you to confidently dive into the world of machine learning and deep learning.Key LearningsUse Flashlight to set up a C++ environment for machine learning projects.Implement neural networks from scratch to gain a hands-on understanding.Preprocess and augment data effectively to improve model performance.Train and evaluate models using appropriate loss functions and metrics.Explore overfitting challenges with techniques like regularization and dropout.Build advanced architectures like ResNet.Apply transfer learning to leverage pre-trained models.Deploy models and integrate them into real-world C++ apps.Implement real-time inference with optimized performance.Improve performance using GPU acceleration and multi-threading techniques.Table of ContentGetting Started with C++ Machine LearningData Handling and PreprocessingBuilding a Simple Neural NetworkTraining Deep Neural NetworksConvolutional Neural NetworksImproving Model PerformanceAdvanced Neural Network ArchitecturesDeployment and IntegrationParallelism and Performance ScalingLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 176 pp. Englisch.
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Taschenbuch. Condizione: Neu. Practical C++ Machine Learning | Hands-on strategies for developing simple machine learning models using C++ data structures and libraries | Anais Sutherland | Taschenbuch | Englisch | 2024 | GitforGits | EAN 9788197950483 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Her…sfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Practical C++ Machine Learning introduces C++ programmers to the world of machine learning. If you know C++ but haven't worked with machine learning solutions before, this book is a good place to start learning the basics and experiment…ing with the language's essential concepts and techniques.The book starts off by showing you how to set up a development environment and put together some basic neural networks using the Flashlight library. It then covers essential tasks like data preprocessing, model training, and evaluation, with practical examples that show how machine learning works in a C++ context. You will also learn strategies for dealing with common problems like overfitting and performance optimization. The next few chapters get into more complex topics like convolutional neural networks, model deployment, and some key performance tuning techniques. This will help you develop and integrate your own models into applications.By the end of the book, you will have essential hands-on experience and a better clarity to explore and expand your machine learning knowledge in C++. This book doesn't aim to cover everything, but it does serve as a good starting point for you to confidently dive into the world of machine learning and deep learning.Key LearningsUse Flashlight to set up a C++ environment for machine learning projects.Implement neural networks from scratch to gain a hands-on understanding.Preprocess and augment data effectively to improve model performance.Train and evaluate models using appropriate loss functions and metrics.Explore overfitting challenges with techniques like regularization and dropout.Build advanced architectures like ResNet.Apply transfer learning to leverage pre-trained models.Deploy models and integrate them into real-world C++ apps.Implement real-time inference with optimized performance.Improve performance using GPU acceleration and multi-threading techniques.Table of ContentGetting Started with C++ Machine LearningData Handling and PreprocessingBuilding a Simple Neural NetworkTraining Deep Neural NetworksConvolutional Neural NetworksImproving Model PerformanceAdvanced Neural Network ArchitecturesDeployment and IntegrationParallelism and Performance Scaling.
