Explore essential machine learning components used to learn, train, and build neural networks and prominent clustering and classification algorithms in this 12-video course. The use of hyperparameters and perceptrons in artificial neuron networks (ANNs) is also covered. Learners begin by studying essential ANN components required to process data, and also different paradigms of learning used in ANN. Examine essential clustering techniques that can be applied on ANN, and the roles of the essential components that are used in building neural networks. Next, recall the approach of generating deep neural networks from perceptrons; learn how to classify differences between models and hyperparameters and specify the approach of tuning hyperparameters. You will discover types of classification algorithm that can be used in neural networks, and features of essential deep learning frameworks for building neural networks. Explore how to choose the right neural network framework for neural network implementations from the perspective of usage scenarios and fitment model, and define computational models that can be used to build neural network models. The concluding exercise concerns ANN training and classification.
identify the key subject areas covered in this course
describe the essential artificial neural network components that are required for processing data
recognize the different paradigms of learning that are used in artificial neural network
list the essential clustering techniques that can be applied on artificial neural network
recognize the roles of the essential components that are used in building neural networks
recall the approach of generating deep neural networks from perceptrons
classify the differences between models and hyperparameter and specify the approach of tuning hyperparameters
define the prominent types of classification algorithm that can be used in neural networks
describe the prominent features of essential deep learning frameworks for building neural networks
recognize how to choose the right neural network framework for neural network implementations from the perspective of usage scenarios and fitment model
define the computational models that can be used to build neural network models
list the essential components of ANN for processing data, recall the clustering techniques that can be applied on ANN, differentiate between models and hyperparameters, and specify the types of classification algorithms that can be used in ANN