A Neural Network in 11 lines of Python

This is an excellent tutorial on neural networks that does a good job of explaining not only how they work but why they work as well. The python code is very easy to follow.

There is code for a very simple example and a more advanced one.

https://iamtrask.github.io/2015/07/12/basic-python-network/

see also:

http://denson-data-science.blogspot.com/2015/09/neural-network-step-by-step.html

Neural Networks for Machine Learning

This is an online course from the University of Toronto.  If you can spend about 8 hours a week for 8 weeks you should be thoroughly familiar with ANN's.

Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc. We'll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

Neural Networks for Machine Learning | Coursera

Basic Neural Network Tutorial : C++ Implementation and Source Code

This tutorial is in two parts, one is the theory of ANN and the other part is a C++ implementation with hints on how to modify it efficiently.  If you are new to neural networks and you are a C++ programmer this is a great place to start.

Basic Neural Network Tutorial – Theory | Taking Initiative

Basic Neural Network Tutorial : C++ Implementation and Source Code | Taking Initiative

Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey

This journal article discusses the application of various machine learning methods to malware detection and information security.


This research synthesizes a taxonomy for classifying detection methods of new malicious code by Machine Learning (ML) methods based on static features extracted from executables. The taxonomy is then operationalized to classify research on this topic and pinpoint critical open research issues in light of emerging threats. The article addresses various facets of the detection challenge, including: file representation and feature selection methods, classification algorithms, weighting ensembles, as well as the imbalance problem, active learning, and chronological evaluation. From the survey we conclude that a framework for detecting new malicious code in executable files can be designed to achieve very high accuracy while maintaining low false positives (i.e. misclassifying benign files as malicious). The framework should include training of multiple classifiers on various types of features (mainly OpCode and byte n-grams and Portable Executable Features), applying weighting algorithm on the classification results of the individual classifiers, as well as an active learning mechanism to maintain high detection accuracy. The training of classifiers should also consider the imbalance problem by generating classifiers that will perform accurately in a real-life situation where the percentage of malicious files among all files is estimated to be approximately 10%.


Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey