Introduction to the Math of Neural Networks – Jeff Heaton

If you have read other books I have written, you will know that I try to shield the reader from the mathematics behind Al. Often, you do not need to know the exact math that is used to train a neural network or perform a cluster operation. You simply want the result. This results-based approach is very much the focus of the Encog project. Encog is an advanced machine learning framework that allows you to perform many advanced operations, such as neural networks, genetic algorithm, support vector machines, simulated annealing and other machine learning methods. Encog allows you to use these advanced techniques without needing to know what is happening behind the scenes.

However, sometimes you really do want to know what is going on behind the scenes. You do want to know the math that is involved. In this book, you will learn what happens, behind the scenes, with a neural network. You will also be exposed to the math. There are already many neural network hooks that at first glance appear as a math text. This is not what I seek to produce here. There are already several very good books that achieve a pure mathematical introduction to neural networks. My goal is to produce a mathematically-based neural network book that targets someone who has perhaps only college-level algebra and computer programing background. These are the only two prerequisites for understanding this book, aside from one more that I will mention later in this introduction. Neural networks overlap several bodies of mathematics.

Neural network goals, such as classification, regression and clustering, come from statistics. The gradient descent that goes into bacicpropagation, along with other training methods, requires knowledge of Calculus. Advanced training, such as Levenberg Marquardt, require both Calculus and Matrix Mathematics. To read nearly any academic-level neural network or machine learning targeted book, you will need some knowledge of Algebra, Calculus, Statistics and Matrix Mathematics. However, the reality is that you need only a relatively small amount of knowledge from each of these areas. The goal of this book is to teach you enough math to understand neural networks and their training.

Related posts:

Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Deep Learning with Python - Francois Cholletf
Python Machine Learning Eqution Reference - Sebastian Raschka
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Deep Learning with Python - Francois Chollet
Pattern recognition and machine learning - Christopher M.Bishop
Python Machine Learning - Sebastian Raschka
Artificial Intelligence by example - Denis Rothman
Python Data Structures and Algorithms - Benjamin Baka
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Introduction to Scientific Programming with Python - Joakim Sundnes
Data Science and Big Data Analytics - EMC Education Services
Deep Learning with Theano - Christopher Bourez
Introduction to Deep Learning - Eugene Charniak
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Python Machine Learning Second Edition - Sebastian Raschka & Vahid Mirjalili
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Java Deep Learning Essentials - Yusuke Sugomori
Learn Keras for Deep Neural Networks - Jojo Moolayil
Neural Networks and Deep Learning - Charu C.Aggarwal
Intelligent Projects Using Python - Santanu Pattanayak
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Deep Learning with PyTorch - Vishnu Subramanian
Machine Learning with Python for everyone - Mark E.Fenner
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Deep Learning and Neural Networks - Jeff Heaton
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Python Deep Learning Cookbook - Indra den Bakker