Eder Santana's Deep Learning with Python

Eder Santana

  • 出版商: Packt Publishing
  • 出版日期: 2019-08-09
  • 售價: $1,400
  • 貴賓價: 9.5$1,330
  • 語言: 英文
  • 頁數: 156
  • 裝訂: Paperback
  • ISBN: 1787280462
  • ISBN-13: 9781787280465
  • 相關分類: Python程式語言DeepLearning
  • 下單後立即進貨 (約3~4週)

商品描述

Key Features

  • Covers the latest concepts in Python deep learning
  • Introduction to Tensorflow
  • Full of examples of solving complicated tasks

Book Description

Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language with its increasing number of libraries that are available in Python.

This book takes you from basic calculus knowledge to understanding backpropagation and its application for training in neural networks for deep learning and understanding automatic differentiation. Through the course, we will provide a thorough training in convolutional, recurrent neural networks and focus on supervised learning and integration into your product offerings such as search, image recognition, and object processing. We will also examine the performance of the sentimental analysis model and will conclude with an introduction to Tensorflow.

By the end of this book, you will be able to confidently start working with deep learning right away.

What you will learn

  • Get the lowdown on backpropagation
  • Perceive and understand automatic differentiation with Theano
  • Explore the powerful mechanism of seamless CPU and GPU usage with Theano
  • Apply convolutional neural networks for image analysis
  • Discover the methods of image classification and harness object recognition using deep learning
  • Get to know recurrent neural networks for the textual sentimental analysis model

商品描述(中文翻譯)

主要特點


  • 涵蓋Python深度學習的最新概念

  • 介紹Tensorflow

  • 提供解決複雜任務的豐富範例

書籍描述

深度學習目前是解決圖像識別、語音識別、物體識別和自然語言等問題的最佳解決方案之一,並且有越來越多的Python庫可供使用。

本書將帶領讀者從基礎的微積分知識到理解反向傳播及其在深度學習中的應用,並深入介紹自動微分。在課程中,我們將提供全面的卷積神經網絡和循環神經網絡的培訓,並重點關注監督學習以及如何將其整合到您的產品中,例如搜索、圖像識別和物體處理。我們還將檢查情感分析模型的性能,並以Tensorflow的介紹作為結尾。

通過閱讀本書,您將能夠自信地開始深度學習的工作。

您將學到什麼


  • 深入了解反向傳播

  • 使用Theano感知和理解自動微分

  • 探索Theano的強大機制,實現無縫的CPU和GPU使用

  • 應用卷積神經網絡進行圖像分析

  • 了解圖像分類的方法,並利用深度學習進行物體識別

  • 瞭解循環神經網絡在文本情感分析模型中的應用