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  • Sách/Book

  • Deep Learning with JAX

    • Authors: Grigory Sapunov (2023)

  • Deep Learning with JAX teaches you how to use JAX and its ecosystem to build neural networks. You’ll learn by exploring interesting examples including an image classification tool, an image filter application, and a massive scale neural network with distributed training across a cluster of TPUs. Discover how to work with JAX for hardware and other low-level aspects and how to solve common machine learning problems with JAX. By the time you’re finished with this awesome book, you’ll be ready to start applying JAX to your own research and prototyping!
  • Sách/Book

  • Deep learning in time series analysis

    • Authors: Arash Gharehbagh (2023)

  • The concept of deep machine learning becomes easier to understandable by paying attention to the cyclic stochastic time series and a time series whose content is non-stationary not only within the cycles, but also over the cycles as the beat to beat variations. This book introduces original deep learning methods for classification of such the time series using proposed clustering methods as the learning tools at the deep level
  • Sách/Book

  • Generative Deep Learning

    • Authors: David Foster (2023)

  • The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative.
  • Sách/Book

  • Deep learning research applications for natural language processing

    • Authors: L. Ashok Kumar (2023)

  • This book delves into issues of natural language processing, a subset of artificial intelligence that enables computers to understand the meaning of human language using techniques of machine learning and deep learning algorithms to discern a words' semantic meanings
  • Sách/Book

  • Deep learning for crack-like object detection

    • Authors: Kaige Zhang (2023)

  • With the development of artificial intelligence (AI), the deep learning technique has achieved great success. However, using deep learning for high accurate crack localization is non-trivial. Based on deep learning, this book has solved a bunch of important issues existing in crack-like object detection, and finished a practical smart pavement surface inspection system. By introducing those method and the system, this book gives the reader an easy way to get into the computer vision and deep learning research area. In addition, this research performs a preliminary study about the future AI system, which provides a concept that has potential to realize fully automatic crack detection without human's intervention
  • Sách/Book

  • Modern deep learning for tabular data : novel approaches to common modeling problems

    • Authors: Zian Wang; Andre Ye (2023)

  • Who This Book Is For Data scientists and researchers of all levels from beginner to advanced looking to level up results on tabular data with deep learning or to understand the theoretical and practical aspects of deep tabular modeling research. Applicable to readers seeking to apply deep learning to all sorts of complex tabular data contexts, including business, finance, medicine, education, and security
  • Sách/Book

  • Learning JavaScript Design Patterns : A JavaScript and React Developer's Guide

    • Authors: Osmani, Addy (2023)

  • Architectural patterns for structuring your components and apps More than 20 design patterns in JavaScript and React, applicable for developers at any level Different pattern categories including creational, structural, and behavioral Essential performance patterns including dynamic imports and code-splitting Rendering patterns such as server-side rendering, hydration, Islands architecture, and more
  • Sách/Book

  • Streaming Data Mesh : A Model for Optimizing Real-Time Data Services

    • Authors: Dulay, Hubert (2023)

  • Data lakes and warehouses have become increasingly fragile, costly, and difficult to maintain as data gets bigger and moves faster. Data meshes can help your organization decentralize data, giving ownership back to the engineers who produced it. This book provides a concise yet comprehensive overview of data mesh patterns for streaming and real-time data services. With this book, you will:
  • Sách/Book

  • Scaling machine learning with Spark : distributed ML with MLlib, TensorFlow, and PyTorch

    • Authors: Polak, Adi (2023)

  • Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better.