Deep LearningDeep Learning is at the heart of many cutting edge Artificial Intelligence (AI) applications. In this course and the successive ones, you will understand various aspects of deep learning that will help you toLearn Deep Learning AlgorithmsArchitecture andApplications.Neural Networks is a set of algorithms designed to learn the way our brain works.The biological neurons inspire the structure and the function of the neural networks.Each artificial neuron is modeled as follows:Each neuron receives inputs.Adds weights and biases to the input.Sum inputs with weights and bias.This triggers the Activation function.Activation function will take the weighted sum of the previous step and fire theoutput.Supervised LearningIn Supervised Learning, you give the list of Inputs and Outputs for a Neural Network to Learn.Based on the actual output, the Neuron adjusts its weights and biases. This is achieved by a process called Training.Neural Networks learn using Back Propagation.When the input is combined with the weights and bias to trigger the activation, it is called forward propagation.When the error estimates are propagated backward to adjust the weights and biases, it is called backpropagation.Shallow Learning - Tasks and AlgorithmsSome of the tasks that can be grouped under shallow learning are:Feature Extraction - PreprocessingMapping specific features into vector space - Kernel methodsRule-based decision making - Decision TreesCombining various estimates - Ensemble methodsDeep LearningIn deep learning, you have two or more hidden layers. This allows the algorithm to perform multiple tasks.