Machine learning
Discover the fascinating realm of machine learning, a critical aspect of artificial intelligence that empowers systems to learn from data without explicit programming. This category features a diverse range of books that cater to both beginners and seasoned professionals, covering essential concepts, techniques, and real-world applications. Ideal for anyone looking to deepen their understanding and enhance their skills in this rapidly evolving field.
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Gödel, Escher, Bach: An Eternal Golden Braid
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Deep Learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville
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Mathematics for Machine Learning
Cheng Soon Ong, A. Aldo Faisal, Marc Peter Deisenroth
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How We Learn: The New Science of Education and the Brain
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Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
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Prompt Engineering for Everyone: A Self-Taught, Human-Centered Approach to AI Programming
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Deep Learning: Foundations and Concepts
Christopher M. Bishop, Hugh Bishop
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Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems
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Hands-On Large Language Models: Language Understanding and Generation
Maarten Grootendorst, Jay Alammar
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Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
Michael Munn, Sara Robinson, Valliappa Lakshmanan
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Data Engineering with Generative and Agentic AI on AWS: Building an AI-Augmented Data Practice for the Enterprise
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Bitcoin: A Game-Theoretic Analysis
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Building Large Language Models from Scratch: Design, Train, and Deploy LLMs with PyTorch
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Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
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Hbr's 10 Must Reads on AI (with Bonus Article How to Win with Machine Learning by Ajay Agrawal, Joshua Gans, and AVI Goldfarb)
Thomas H Davenport, Ajay Agrawal, Marco Iansiti, Harvard Business Review, Tsedal Neeley
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The Handbook of Data Science and AI: Generate Value from Data with Machine Learning and Data Analytics
Annalisa Cadonna, Barbora Antosova Vesela, Jana Eder, Katherine Munro, Danko Nikolic, Stefan Papp, Wolfgang Weidinger, Zoltan Toth, Karin Bruckmüller
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Machine Learning for Asset Managers
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The Little Learner: A Straight Line to Deep Learning
Daniel P. Friedman, Anurag Mendhekar
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Reinforcement Learning for Finance: A Python-Based Introduction
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Mastering Retrieval-Augmented Generation: Advanced Techniques and Production-Ready Solutions for Enterprise AI
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Building and Training Generative AI Models: A Practical Guide to Generative AI Development and Scaling
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Reinforcement Learning: Industrial Applications of Intelligent Agents
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The Art of Machine Learning: A Hands-On Guide to Machine Learning with R
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Ensemble Methods for Machine Learning
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Machine Learning with Python
Tarkeshwar Barua, Ritesh Kumar Jain, Ruchi Doshi, Kamal Kant Hiran
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Category „Machine learning“
Delve into the fascinating world of machine learning, a groundbreaking field at the intersection of technology and data analysis. This category offers a diverse selection of books that cater to enthusiasts, students, and professionals alike. Whether you're just starting your journey or looking to deepen your understanding, these resources will guide you through the complexities of algorithms, data processing, and predictive modeling.
The evolution of machine learning has transformed industries, enabling innovations in areas such as healthcare, finance, and technology. With roots tracing back to the early days of artificial intelligence, the discipline has matured significantly, now allowing machines to learn from data and improve over time. Readers will find not only theoretical insights but also practical applications that demonstrate how machine learning is redefining possibilities.
Exploring this category uncovers a wealth of knowledge, including essential concepts such as supervised and unsupervised learning, neural networks, and natural language processing. Each title has been carefully selected to provide value, presenting complex topics in an accessible manner. This makes it easier for readers to apply what they've learned, whether in academic pursuits or real-world applications.
Additionally, the machine learning literature encompasses works from esteemed authors and leading experts who share their expertise and experiences within the field. These insights not only enhance the learning experience but also inspire readers to explore the frontier of artificial intelligence. By engaging with these texts, individuals can become well-versed in the latest trends, applications, and ethical considerations of machine learning.
With a commitment to quality and relevancy, this category serves as a crucial resource for anyone eager to understand and harness the power of machine learning. Dive in and transform your understanding of how machines learn and adapt to our ever-changing world.