rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Applied Deep Learning with Python
  name:
    name part: Galea, Alex
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Capelo, Luis
    role:
      role term: author.
  name:
    name part: Safari, an O'Reilly Media Company
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: xx
    date issued: 2018
    edition: 1st edition.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (334 pages)
  abstract: A hands-on guide to deep learning that's filled with intuitive explanations and engaging practical examples Key Features Designed to iteratively develop the skills of Python users who don't have a data science background Covers the key foundational concepts you'll need to know when building deep learning systems Complete with step-by-step exercises and activities to help you build the skills you need for the real world Book Description Taking an approach that uses the latest developments in the Python ecosystem, you'll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before you train your first predictive model. You'll then explore a variety of approaches to classification such as support vector networks, random decision forests and k-nearest neighbors to build on your knowledge before moving on to advanced topics. After covering classification, you'll go on to discover ethical web scraping and interactive visualizations, which will help you professionally gather and present your analysis. Next, you'll start building your keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. You'll then be guided through a trained neural network, which will help you explore common deep learning network architectures (convolutional, recurrent, and generative adversarial networks) and deep reinforcement learning. Later, you'll delve into model optimization and evaluation. You'll do all this while working on a production-ready web application that combines TensorFlow and Keras to produce meaningful user-friendly results. By the end of this book, you'll be equipped with the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively. What you will learn Discover how you can assemble and clean your very own datasets Develop a customized machine learning classification strategy Build, train and enhance your…
  note: Galea, Alex.
  note: Electronic resource.
  note: Electronic reproduction. Boston, MA : Safari, Available via World Wide Web. 2018.
  note: Mode of access: World Wide Web.
  identifier: 9781789804744
  identifier: 9781789806991
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record creation date: 040918
    record change date: 20260129225150.7
    record identifier: in00004147100