Course details

Convolutional Neural Networks: Implementing & Training

Convolutional Neural Networks: Implementing & Training


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

This course explores machine learning convolutional neural networks (CNNs), which are popular for implementation in image and audio processing. Learners explore AI (artificial intelligence), and the issues surrounding implementation, how to approach organizational talent and strategy, and how to prepare for AI architecture in this 8-video course. You will learn to use the Google Colab tool, and to implement image recognition classifier by using CNN, Keras, and TensorFlow. Next, learn to install and implement a model, and use it for image classification. You will examine the artificial neural network ResNet (residual neural network), and how it builds on constructs known from pyramidal cells and cerebral cortex. You will also study PyTorch, an open-source machine learning library that enables fast, flexible experimentation, and efficient production through a hybrid front end, and learn to use the PyTorch ecosystem tool to develop and implement neural networks. Finally, this course demonstrates how to create a data set by using Training CNN by using PyTorch to categorize garments.



Expected Duration (hours)
0.5

Lesson Objectives

Convolutional Neural Networks: Implementing & Training

  • Course Overview
  • implement image recognition classifier using convolutional neural networks, Keras, and TensorFlow
  • describe ResNet layers and blocks
  • list the essential PyTorch ecosystem tools that can be used to develop and implement neural networks
  • install and configure PyTorch
  • implement convolutional neural networks (CNNs) using PyTorch
  • use PyTorch to train convolutional neural networks (CNNs) to categorize garments
  • install and configure PyTorch and implement convolutional neural networks (CNNs) using PyTorch
  • Course Number:
    it_mlodcndj_02_enus

    Expertise Level
    Intermediate