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    Deep Learning: Foundations and Concepts by Christopher M. Bishop

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    Unlock the secrets of AI with Deep Learning: Foundations and Concepts. Written by Christopher M. Bishop (author of Pattern Recognition and Machine Learning) and Hugh Bishop, this definitive textbook offers a rigorous, concept-first approach to the field. Covering everything from Transformers and LLMs to Diffusion Models and GNNs, it is the essential resource for mastering the mathematics and theory behind modern deep learning.

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  • -22% designing data intensive applications the big idea martin kleppmann 9781449373320

    Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

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    Master the architecture of modern data systems with Designing Data-Intensive Applications. Authored by Martin Kleppmann, this highly acclaimed O’Reilly text explores the foundational principles behind building reliable, scalable, and maintainable software. Featuring deep dives into distributed systems, database storage engines, and stream processing, it is the essential guide for engineers and architects navigating the complex trade-offs of data-heavy applications.

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  • -46% hands on unsupervised learning using python how to ankur a patel 9781492035640

    Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data

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    Master the power of unlabeled data with Hands-On Unsupervised Learning Using Python. Authored by Ankur A. Patel, this highly practical guide teaches developers how to build production-ready machine learning models using Scikit-learn and TensorFlow. Featuring step-by-step code examples for clustering, anomaly detection, and dimensionality reduction, it is the perfect resource for Python programmers looking to turn raw, unstructured data into actionable business intelligence.

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  • -25% practical statistics for data scientists peter bruce andrew bruce peter 9781492072942

    Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, 2nd Edition

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    Master the statistical foundations of modern machine learning with Practical Statistics for Data Scientists, 2nd Edition. Authored by Peter Bruce, Andrew Bruce, and Peter Gedeck, this highly accessible guide explains over 50 essential statistical concepts and how to apply them using both R and Python. Focusing on practical application rather than dense mathematics, it is the ultimate desk reference for building accurate, data-driven solutions and avoiding common analytical pitfalls.

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