Convolutional and Recurrent Neural Networks
Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level
Overview/Description
Some tasks aren't suitable for traditional neural networks and require specialized neural networks. In this course you will learn about convolutional and recurrent neural networks and the types of problems they can solve.

Target Audience
Anyone interested in understanding machine learning and using it to solve problems

Prerequisites
None

Expected Duration (hours)
0.7

Lesson Objectives Convolutional and Recurrent Neural Networks

start the course
describe convolutional neural networks, how they are different from regular neural networks, and how they are used
describe the high level architecture of convolutional neural networks
describe how convolution layers are set in convolutional neural networks
describe how pooling layers work in convolutional neural networks
describe some training considerations for convolutional neural networks and how training can differ from traditional neural networks
describe regularization and how it applies to convolutional neural networks
implement and train a convolutional neural network in TensorFlow
perform regularizing to a convolutional neural network in TensorFlow
describe recurrent neural networks, how they are different from regular neural networks, and how they are used
describe the architecture of a recurrent neural network
implement an LSTM network in TensorFlow
use RNNs to perform time-series analysis in TensorFlow
use TensorFlow to create a CNN that classifies images

Course Number: sd_exml_a05_it_enus

Expertise Level
Beginner