Hands-On Neural Network Programming with C#: Add powerful neural network capabilities to your C# enterprise applications

Matt R. Cole

  • 出版商: Packt Publishing
  • 出版日期: 2018-09-28
  • 售價: $1,380
  • 貴賓價: 9.5$1,311
  • 語言: 英文
  • 頁數: 328
  • 裝訂: Paperback
  • ISBN: 1789612012
  • ISBN-13: 9781789612011
  • 相關分類: C#
  • 相關翻譯: C# 神經網絡編程 (簡中版)
  • 立即出貨 (庫存=1)



Create and unleash the power of neural networks by implementing C# and .Net code

Key Features

  • Get a strong foundation of neural networks with access to various machine learning and deep learning libraries
  • Real-world case studies illustrating various neural network techniques and architectures used by practitioners
  • Cutting-edge coverage of Deep Networks, optimization algorithms, convolutional networks, autoencoders and many more

Book Description

Neural networks have made a surprise comeback in the last few years and have brought tremendous innovation in the world of artificial intelligence.

The goal of this book is to provide C# programmers with practical guidance in solving complex computational challenges using neural networks and C# libraries such as CNTK, and TensorFlowSharp. This book will take you on a step-by-step practical journey, covering everything from the mathematical and theoretical aspects of neural networks, to building your own deep neural networks into your applications with the C# and .NET frameworks.

This book begins by giving you a quick refresher of neural networks. You will learn how to build a neural network from scratch using packages such as Encog, Aforge, and Accord. You will learn about various concepts and techniques, such as deep networks, perceptrons, optimization algorithms, convolutional networks, and autoencoders. You will learn ways to add intelligent features to your .NET apps, such as facial and motion detection, object detection and labeling, language understanding, knowledge, and intelligent search.

Throughout this book, you will be working on interesting demonstrations that will make it easier to implement complex neural networks in your enterprise applications.

What you will learn

  • Understand perceptrons and how to implement them in C#
  • Learn how to train and visualize a neural network using cognitive services
  • Perform image recognition for detecting and labeling objects using C# and TensorFlowSharp
  • Detect specific image characteristics such as a face using Accord.Net
  • Demonstrate particle swarm optimization using a simple XOR problem and Encog
  • Train convolutional neural networks using ConvNetSharp
  • Find optimal parameters for your neural network functions using numeric and heuristic optimization techniques.

Who this book is for

This book is for Machine Learning Engineers, Data Scientists, Deep Learning Aspirants and Data Analysts who are now looking to move into advanced machine learning and deep learning with C#. Prior knowledge of machine learning and working experience with C# programming is required to take most out of this book

Table of Contents

  1. A Quick Refresher
  2. Building our first Neural Network Together
  3. Decision Tress and Random Forests
  4. Face and Motion Detection
  5. Training CNNs using ConvNetSharp
  6. Training Autoencoders Using RNNSharp
  7. Replacing Back Propagation with PSO
  8. Function Optimizations; How and Why
  9. Finding Optimal Parameters
  10. Object Detection with TensorFlowSharp
  11. Time Series Prediction and LSTM Using CNTK
  12. GRUs Compared to LSTMs, RNNs, and Feedforward Networks
  13. Appendix A- Activation Function Timings
  14. Appendix B- Function Optimization Reference




  • 通過訪問各種機器學習和深度學習庫,建立神經網絡的堅實基礎

  • 實際案例研究,展示實踐者使用的各種神經網絡技術和架構

  • 深度網絡、優化算法、卷積網絡、自編碼器等尖端技術的全面覆蓋







  • 了解感知器以及如何在C#中實現它們

  • 學習如何使用認知服務訓練和可視化神經網絡

  • 使用C#和TensorFlowSharp進行圖像識別,檢測和標記對象

  • 使用Accord.Net檢測特定圖像特徵,如面部

  • 使用簡單的XOR問題和Encog演示粒子群優化

  • 使用ConvNetSharp訓練卷積神經網絡

  • 使用數值和啟發式優化技術為您的神經網絡函數找到最佳參數




  1. 快速複習

  2. 一起構建我們的第一個神經網絡

  3. 決策樹和隨機森林

  4. 面部和運動檢測

  5. 使用ConvNetSharp訓練CNN

  6. 使用RNNSharp訓練自編碼器

  7. 用PSO替換反向傳播

  8. 函數優化:如何以及為什麼

  9. 找到最佳參數

  10. 使用TensorFlowSharp進行物體檢測

  11. 使用CNTK進行時間序列預測和LSTM

  12. GRU與LSTM、RNN和前饋網絡的比較

  13. 附錄A-激活函數時間

  14. 附錄B-函數優化參考