Fake notes is nothing new. They have been existing since long. Today fake notes continues to serve as a political tool around the world, and new technologies are enabling individuals to propagate that fake notes at unprecedented rates. One of those new developments, artificial intelligence, can help intelligence department build a consistent fake notes detector, but AI can also empower others to disseminate and even create new forms of fake notes. To understand how, we need to take a quick detour and explain machine learning — one of the most important sub-domains of artificial intelligence Detecting Fake notes with Machine Learning: Machine learning is, in the most basic sense, a system that learns from its actions and makes decisions accordingly, and it relies in turn on a process called deep learning which breaks down a single complex idea into a series of smaller, more approachable tasks. Thus, conceptually, machine learning can help detect fake notes! An intelligent system that takes notes features as its input and ‘Fake’ or ‘Not Fake’ sticker as output.
Fake notes haven been surging for a while now. There have been initiatives to separate fake notes from the real ones. And as a contribution to create a state of the art model using Neural Networks, this is a project that will help in the very cause of discriminating between fake and real notes.
• To create state of the art model using Neural Networks in Tensorflow
• To detect fake notes from the real ones
• To easily check multiple notes at once using one single program
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning. Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design and board game programs, where they have produced results comparable to and in some cases superior to human experts. Deep learning models are vaguely inspired by information processing and communication patterns in biological nervous systems yet have various differences from the structural and functional properties of biological brains (especially human brain), which make them incompatible with neuroscience evidences.