Book Description

Book Description

The necessity for machine learning is everywhere,and most production enterprise applications are written in C# using tools such as Visual Studio,SQL Server,and Microsoft Azur2e. Hands-On Machine Learning with C# uniquely blends together an understanding of various machine learning concepts,techniques of machine learning,and various available machine learning tools through which users can add intelligent features.These tools include image and motion detection,Bayes intuition,and deep learning,to C# .NET applications.
Using this book,you will learn to implement supervised and unsupervised learning algorithms and will be better equipped to create excellent predictive models. In addition,you will learn both supervised and unsupervised forms of regression,mainly logistic and linear regression,in depth. Next,you will use the nuML machine learning framework to learn how to create a simple decision tree. In the concluding chapters,you will use the Accord.Net machine learning framework to learn sequence recognition of handwritten numbers using dynamic time warping. We will also cover advanced concepts such as artificial neural networks,autoencoders,and reinforcement learning.
By the end of this book,you will have developed a machine learning mindset and will be able to leverage C# tools,techniques,and packages to build smart,predictive,and real-world business applications.
Contents
1: MACHINE LEARNING BASICS
2: REFLECTINSIGHT – REAL-TIME MONITORING
3: BAYES INTUITION – SOLVING THE HIT AND RUN MYSTERY AND PERFORMING DATA ANALYSIS
4: RISK VERSUS REWARD – REINFORCEMENT LEARNING
5: FUZZY LOGIC – NAVIGATING THE OBSTACLE COURSE
6: COLOR BLENDING – SELF-ORGANIZING MAPS AND ELASTIC NEURAL NETWORKS
7: FACIAL AND MOTION DETECTION – IMAGING FILTERS
8: ENCYCLOPEDIAS AND NEURONS – TRAVELING SALESMAN PROBLEM
9: SHOULD I TAKE THE JOB – DECISION TREES IN ACTION
10: DEEP BELIEF – DEEP NETWORKS AND DREAMING
11: MICROBENCHMARKING AND ACTIVATION FUNCTIONS
12: INTUITIVE DEEP LEARNING IN C# .NET
13: QUANTUM COMPUTING – THE FUTURE
What You Will Learn
Learn to parameterize a probabilistic problem
Use Naive Bayes to visually plot and analyze data
Plot a text-based representation of a decision tree using nuML
Use the Accord.NET machine learning framework for associative rule-based learning
Develop machine learning algorithms utilizing fuzzy logic
Explore support vector machines for image recognition
Understand dynamic time warping for sequence recognition
Authors
Matt R. Cole
Matt R. Cole is a seasoned developer with 30 years’ experience in Microsoft Windows,C,C++,C#,and .NET. He previously wrote a speech and audio VOIP system for NASA for use with the Space Shuttle and a space station. He is the owner of Evolved AI Solutions,a premier provider of advanced ML/Bio-AI technologies. He developed the first enterprise-grade microservice framework (written fully in C# and .NET) used by a major hedge fund in NYC and also developed the first Bio-AI Swarm framework,which fully integrates mirror and canonical neurons.
Amazon Page

下载地址