Develop essential data science & ai skills with expert instruction and practical examples.
This course teaches the practical design of a Neural network simulator using C++. It is recommended for all levels of C++ programmers with a theoretical knowledge of Neural network and looking forward to implement them in practice. The course interactively simulates the Neural network from the design of the class called Neuron, to the implementation of the Neuron layers in Vectors and finally the top level design consisting of the input layer, hidden layer and the output layer.
Some random training samples will be generated which will be feed to the input layer through a vector and progress to the output layer through feed forward. The back propagation is also implemented which enables us to calculate the error and update the weight for a more accurate result. The training samples used in this course is for demonstration as the concept of sample generation is well explained.
At the end of the course the student should be able generate real samples for testings. Some of the Core concepts we will learn in this course includes:Feed forward. Bias Neuron.
Transfer function. Back propagation. Activation function.
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