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Authors: Aurélien Géron (2022) - Third edition, explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started: Use Scikit-learn to track an example ML project end to end; Explore several models, including support vector machines, decision trees, random forests, and ensemble methods; Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection.
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Authors: Andrew Troelsen (2021) - This essential classic provides a comprehensive foundation in the C# programming language and the framework it lives in. Now in its 10th edition, you will find the latest C# 9 and .NET 5 features served up with plenty of "behind the curtain" discussion designed to expand developers’ critical thinking skills when it comes to their craft. Coverage of ASP.NET Core, Entity Framework Core, and more, sits alongside the latest updates to the new unified .NET platform, from performance improvements to Windows Desktop apps on .NET 5, updates in XAML tooling, and expanded coverage of data files and data handling. Going beyond the latest features in C# 9, all code samples are rewritten for this latest release.
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Authors: Chih-Lung Lin (2024) - This reprint brings together contributions from leading experts in their fields. Each paper provides valuable insights into the latest trends, methods, and challenges in state-of-the-art applications of machine learning for pattern recognition. In addition, the research in each paper not only showcases the latest advancements in machine learning algorithms but also discusses their successful applications and the challenges encountered in real-world scenarios.
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Authors: Holden Karau (2023) - With this book, you'll learn how to: Accelerate your ML workflows with integrations including PyTorch. Handle key skew and take advantage of Spark's new dynamic partitioning. Make your code reliable with scalable testing and validation techniques. Make Spark high performance. Deploy Spark on Kubernetes and similar environments.Take advantage of GPU acceleration with RAPIDS and resource profiles. Get your Spark jobs to run faster. Use Spark to productionize exploratory data science projects. Handle even larger datasets with Spark. Gain faster insights by reducing pipeline running times. Become an O’Reilly member and get unlimited acces.
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Authors: Joseph Howse (2023) - Computer vision is a rapidly evolving science in the field of artificial intelligence, encompassing diverse use cases and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You'll be able to put theory into practice by building apps with OpenCV 5 and Python 3.
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Authors: Dr. Logan Song (2023) - As cloud computing continues to revolutionize IT, professionals face the challenge of keeping up with rapidly evolving technologies. This book provides a clear roadmap for mastering cloud concepts, developing hands-on expertise, and obtaining professional certifications, making it an essential resource for those looking to advance their careers in cloud computing.
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Authors: Shannon Bradshaw (2019) - This book shows you how to: Work with MongoDB, perform write operations, find documents, and create complex queries. Index collections, aggregate data, and use transactions for your application. Configure a local replica set and learn how replication interacts with your application. Set up cluster components and choose a shard key for a variety of applications. Explore aspects of application administration and configure authentication and authorization
Use stats when monitoring, back up and restore deployments, and use system settings when deploying MongoDB
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Authors: Aston Zhang (2023) - Deep learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse fields as computer vision, natural language processing, and automatic speech recognition. Applying deep learning requires you to simultaneously understand how to cast a problem, the basic mathematics of modeling, the algorithms for fitting your models to data, and the engineering techniques to implement it all. This book is a comprehensive resource that makes deep learning approachable, while still providing sufficient technical depth to enable engineers, scientists, and students to use deep learning in their own work.
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Authors: Raymond McLeod, Jt (2007) - Giới thiệu toàn diện về hệ thống thông tin quản lý trong doanh nghiệp, tập trung vào cách thức công nghệ thông tin hỗ trợ quản trị, chiến lược kinh doanh và ra quyết định. Trình bày các hệ thống ERP, CRM, thương mại điện tử và các xu hướng công nghệ mới.
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Authors: Daniel Jurafsky (2025) - In the first part of the book we introduce the fundamental suite of algorithmic and linguistic tools that make up the modern neural large language model. We begin with tokenization and preprocessing, including Unicode, and then proceed to intro- duce many basic language modeling ideas using simple n-gram language models, we then introduce the algorithms which are the components of large language models logistic regression, embeddings, and feedforward networks. Next we are ready to introduce the principles of large language modeling, encoder, decoders and pretrain- ing, then the fundamental transformer architecture, then masked language model and other architectures like RNNs and LSTMs, information retrieval and retrieval- based algorithms like RAG, machine translation and the encode...
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