Neural Networks are computing systems vaguely inspirited by the biological neural networks that constitute animal brains. An ANN is based on collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing.

Artificial neural networks are built like the human brain, with neuron nodes interconnected like a web. The human brain has hundreds of billions of cells called neurons. Each neuron is made up of a cell body that is responsible for processing information by carrying information towards (inputs) and away (outputs) from the brain.

An ANN has hundreds or thousands of artificial neurons called processing units, which are interconnected by nodes. These processing units are made up of input and output units. The input units receive various forms and structures of information based on an internal weighting system, and the neural network attempts to learn about the information presented to produce one output report. Just like humans need rules and guidelines to come up with a result or output, ANNs also use a set of learning rules called backpropagation, an abbreviation for backward propagation of error, to perfect their output results.

In this, you will learn how to solve numerically based datasets, images, text, time-series data set with the artificial neural network, convolution neural network, and recurrent neural network with Python. Learn Handson by doing 3 projects in this course. All the code files and datasets included in this course.