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of your data. This book makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement;
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Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today's most popular;
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also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and;
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on TensorFlow Who This Book Is For This book is ideal for data scientists who are familiar with C++ or Python and perform machine;
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Dig deep into the data with a hands-on guide to machine learning with updated examples and more! Machine Learning: Hands-On for Developers;
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model, X-means and Reinforcement learning algorithm with Q learning, R learning, TD learning, SARSA Learning, and so forth. Hands-on machine;
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. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch. After completing Hands-on Machine Learning with;
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models Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz Book DescriptionMachine;
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. The final chapter contains a detailed end-to-end solution with neural networks in Pytorch. After completing Hands-on Machine Learning with;
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Dig deep into the data with a hands-on guide to machine learningMachine Learning: Hands-On for Developers and Technical;
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hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines;
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intelligence and machine learning services available on AWS. With this book, you'll work through hands-on exercises and learn to use these services to;
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is a complete guide to help you become well versed with machine learning on the IBM Cloud using Python. Hands-On Machine Learning with IBM;
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Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine;
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Discover the skill-sets required to implement various approaches to Machine Learning with Python Key Features Explore unsupervised learning;
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with Python Scikit-Learn library is explained to audience with an emphasis on supervised learning and classification. Image processing on IoT;
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with Python Scikit-Learn library is explained to audience with an emphasis on supervised learning and classification. Image processing on IoT;
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Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming;
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provide hands-on machine learning solutions with scikit-learn and Python toolkits. The book begins with an explanation of machine learning;
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This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale;
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Develop smart applications without spending days and weeks building machine-learning models. With this practical book, you'll learn how to;
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Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies;
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fast, efficient, and accurate deep learning tasks. Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations;
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can't account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the;
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Accord.NET, Weka, and F# * Run machine learning jobs on the Cloud with MBrace * Perform mathematical operations on matrices and vectors using;
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Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The;
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TensorFlow on your computer Learn the fundamentals of statistical regression and neural networks Visualize the machine learning process with;
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Einde inhoud
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