Deep Learning for Beginners A beginner's guide to getting up and running with deep learning from scratch using Python

Pablo Rivas

  • 出版商: Packt Publishing
  • 出版日期: 2020-09-18
  • 售價: $1,350
  • 貴賓價: 9.5$1,283
  • 語言: 英文
  • 頁數: 341
  • 裝訂: Paperback
  • ISBN: 1838640851
  • ISBN-13: 9781838640859
  • 相關分類: DeepLearning
  • 相關翻譯: 深度學習初學者指南 (簡中版)
  • 立即出貨 (庫存=1)

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商品描述

Key Features

  • Understand the fundamental machine learning concepts useful in deep learning
  • Learn the underlying mathematical and statistical concepts as you implement smart deep learning models from scratch
  • Explore easy-to-understand examples and use cases that will help you build a solid foundation in DL

Book Description

With information on the web exponentially increasing, it has become more difficult than ever to navigate through everything to find reliable content that will help you get started with deep learning (DL). This book is designed to help you if you're a beginner looking to work on deep learning and build deep learning models from scratch, and already have the basic mathematical and programming knowledge required to get started.

The book begins with a basic overview of machine learning, guiding you through setting up popular Python frameworks. You will also understand how to prepare data by cleaning and preprocessing it for deep learning, and gradually go on to explore neural networks. A dedicated section will give you insights into the working of neural networks by helping you get hands-on with training single and multiple layers of neurons. Later, you will cover popular neural network architectures such as CNNs, RNNs, AEs, VAEs, and GANs with the help of simple examples and even build models from scratch. At the end of each chapter, you will find a question and answer section to help you test what you've learned through the course of the book.

By the end of this book, you'll be well-versed with deep learning concepts and have the knowledge you need to use specific algorithms with various tools for different tasks.

What you will learn

  • Implement recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) in image classification and NLP
  • Understand the mathematical terminology associated with DL algorithms
  • Explore the role of convolutional neural networks (CNNs) in computer vision and signal processing
  • Understand the ethical implications of DL modeling
  • Code a generative adversarial network (GAN) and a variational autoencoder (VAE) to generate images from a learned latent space
  • Implement visualization techniques to compare deep and variational autoencoders

Who This Book Is For

This book is for aspiring data scientists and deep learning engineers who want to get started with the fundamentals of deep learning and neural networks. Although no prior knowledge of deep learning or machine learning is required, familiarity with linear algebra and Python programming is necessary to get started.

商品描述(中文翻譯)

主要特點


  • 了解在深度學習中有用的基本機器學習概念

  • 在從頭開始實現智能深度學習模型時學習底層的數學和統計概念

  • 探索易於理解的例子和使用案例,幫助您建立深度學習的堅實基礎

書籍描述

隨著網絡上的信息指數級增長,要找到可靠的內容來幫助您入門深度學習(DL)變得比以往更加困難。本書旨在幫助初學者從頭開始進行深度學習並構建深度學習模型,並且已經具備了開始所需的基本數學和編程知識。

本書首先對機器學習進行基本概述,引導您設置流行的Python框架。您還將了解如何通過清理和預處理數據為深度學習做準備,並逐漸探索神經網絡。一個專門的部分將通過幫助您親自訓練單層和多層神經元來讓您深入了解神經網絡的工作原理。隨後,您將使用簡單的例子甚至從頭開始構建流行的神經網絡架構,如CNN、RNN、AE、VAE和GAN。在每章的結尾,您將找到問答部分,以幫助您測試您在本書中學到的知識。

通過閱讀本書,您將熟悉深度學習概念,並具備使用各種工具和特定算法執行不同任務所需的知識。

您將學到什麼


  • 在圖像分類和自然語言處理中實現循環神經網絡(RNN)和長短期記憶網絡(LSTM)

  • 了解與DL算法相關的數學術語

  • 探索卷積神經網絡(CNN)在計算機視覺和信號處理中的作用

  • 了解DL建模的道德影響

  • 編寫生成對抗網絡(GAN)和變分自編碼器(VAE)以從學習的潛在空間生成圖像

  • 實現可視化技術以比較深度和變分自編碼器

適合閱讀對象

本書適合有志成為數據科學家和深度學習工程師的人士,他們想要入門深度學習和神經網絡的基礎知識。雖然不需要深度學習或機器學習的先備知識,但需要熟悉線性代數和Python編程才能開始閱讀。

作者簡介

Pablo Rivas

Dr. Pablo Rivas is an Assistant Professor of Computer Science at Marist College in Poughkeepsie, New York. He worked in the industry for a decade as a software engineer before becoming an academic. He is a Senior Member of the IEEE, ACM, and SIAM. He was formerly at NASA Goddard Space Flight Center, and at Baylor University performing post-doctoral research and teaching. He considers himself an ally of women in technology, a deep learning evangelist, machine learning ethicist, and is a proponent of the democratization of machine learning and artificial intelligence in general. He teaches machine learning and deep learning courses with applications in natural language processing and computer vision. Dr. Rivas is a published author and all his papers are related to machine learning, computer vision, and machine learning ethics; he recently became a certified online instructor; and he is also a machine learning consultant of the New York State Cloud Computing and Analytics Center. Prof. Rivas prefers Vim over Emacs and spaces over tabs.

作者簡介(中文翻譯)

Pablo Rivas

Dr. Pablo Rivas是紐約州普基普西的馬里斯特學院的計算機科學助理教授。在成為學者之前,他在軟體工程師領域工作了十年。他是IEEE、ACM和SIAM的高級會員。他曾在NASA戈達德太空飛行中心和貝勒大學進行博士後研究和教學。他自認為是科技領域中女性的盟友,是深度學習的倡導者,機器學習倫理學家,並支持機器學習和人工智能的民主化。他教授應用於自然語言處理和計算機視覺的機器學習和深度學習課程。Rivas博士是一位已發表論文的作者,他的所有論文都與機器學習、計算機視覺和機器學習倫理有關;他最近成為了一位認證的線上教師;他還是紐約州雲計算和分析中心的機器學習顧問。Rivas教授更喜歡Vim而不是Emacs,並且喜歡使用空格而不是制表符。