In this 13-video course, learners can explore how to work with machine learning frameworks and Python to implement training algorithms for neural networks. You will learn the concept and characteristics of perceptrons, a single layer neural network that aggregates the weighted sum of inputs, and returns either zero or one, and neural networks. You will then explore some of the prominent learning rules that to apply in neural networks, and the concept of supervised and unsupervised learning. Learn several types of neural network algorithms, and several training methods. Next, you will learn how to prepare and curate data by using Amazon SageMaker, and how to implement an artificial neural network training process using Python, and other prominent and essential learning algorithms to train neural networks. You will learn to use Python to train artificial neural networks, and how to use Backpropagation in Keras to implement multilayer perceptrons or neural networks. Finally, this course demonstrates how to implement regularization in multilayer perceptrons by using Keras.
Training Neural Networks: Implementing the Learning Process
identify the subject areas covered in this course
describe the characteristics of perceptrons and neural networks
recognize the essential components of perceptrons and perceptron learning algorithms
identify the different types of learning rules that can be applied in neural networks
compare the supervised and unsupervised learning methods of artificial neural networks
list neural network algorithms that can be used to solve complex problems across domains
prepare and curate data for neural network training implementation
implement the artificial neural network training process using Python
recall the algorithms that can be used to train neural networks
implement backpropagation using Python to train artificial neural networks
use backpropagation and Keras to implement multi-layer perceptron or neural net
implement regularization in multilayer perceptron using Keras
compare the supervised and unsupervised learning methods, recall algorithms that can be used to train neural networks, and implement backpropagation using Python to train ANN