Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks

Umberto Michelucci

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商品描述

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function. 

The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions. 

Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy). 

What You Will Learn

 

  • Implement advanced techniques in the right way in Python and TensorFlow
  • Debug and optimize advanced methods (such as dropout and regularization)
  • Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on)
  • Set up a machine learning project focused on deep learning on a complex dataset

 

Who This Book Is For

Readers with a medium understanding of machine learning, linear algebra, calculus, and basic Python programming. 

 

商品描述(中文翻譯)

在深度學習中,與優化算法、超參數調整、dropout和錯誤分析等高級主題一起工作,以及解決訓練深度神經網絡時遇到的典型問題的策略。您將首先研究主要用於單個神經元的激活函數(ReLu、sigmoid和Swish),了解如何使用TensorFlow執行線性和邏輯回歸,以及選擇正確的成本函數。

下一部分介紹了具有多層和神經元的更複雜的神經網絡架構,並探討了權重的隨機初始化問題。整個章節專門介紹了神經網絡錯誤分析的完整概述,並提供了解決由變異、偏差、過擬合和來自不同分佈的數據集引起的問題的示例。

《應用深度學習》還討論了如何完全從頭實現邏輯回歸,而不使用任何Python庫,除了NumPy,以讓您體會到像TensorFlow這樣的庫如何實現快速和高效的實驗。每種方法都包含案例研究,以將所有理論信息付諸實踐。您將發現編寫優化Python代碼的技巧(例如使用NumPy向量化循環)。

您將學到什麼:

- 在Python和TensorFlow中正確實現高級技術
- 調試和優化高級方法(例如dropout和正則化)
- 進行錯誤分析(以了解是否存在偏差問題、變異問題、數據偏移問題等)
- 在複雜數據集上建立一個專注於深度學習的機器學習項目

本書適合對機器學習、線性代數、微積分和基本Python編程有中等理解的讀者。