Introduction

The brain is the most important organ of the human body. It is the central processing unit for all the functions performed by us. Weighing only 1.5 kilos, it has around 86 billion neurons. A neuron is defined as a cell transmitting nerve impulses or electrochemical signals. The brain is a complex network of neurons which process information through a system of several interconnected neurons. It has always been challenging to understand the brain functions; however, due to advancements in computing technologies, we can now program neural networks artificially.

The discipline of ANN arose from the thought of mimicking the functioning of the same human brain that was trying to solve the problem. The drawbacks of conventional approaches and their successive applications have been overcome within well-defined technical environments.

AI or machine intelligence is a field of study that aims to give cognitive powers to computers to program them to learn and solve problems. Its objective is to simulate computers with human intelligence. AI cannot imitate human intelligence completely; computers can only be programmed to do some aspects of the human brain.

Machine learning is a branch of AI which helps computers to program themselves based on the input data. Machine learning gives AI the ability to do data-based problem solving. ANNs are an example of machine learning algorithms.

Deep learning (DL) is complex set of neural networks with more layers of processing, which develop high levels of abstraction. They are typically used for complex tasks, such as image recognition, image classification, and hand writing identification.

Most of the audience think that neural networks are difficult to learn and use it as a black box. This book intends to open the black box and help one learn the internals with implementation in R. With the working knowledge, we can see many use cases where neural networks can be made tremendously useful seen in the following image: