In recent years, convolutional neural networks (CNNs) have shown remarkable performance in many computer vision tasks, such as object recognition. In this course, you’ll explore the most popular deep learning library — Google's TensorFlow — and how it can be used with the Elastic Stack to build a contextual image classification system. Essential concepts are presented, such as TensorFlow data types, data structures, and how to create a convolutional neural networks to perform deep learning on visual data. In a series of labs, these concepts are put into practice and the results are shared to the Elastic Stack for searching, analyzing, and visualizing. After completing this course you will be able to build a deep learning neural network with TensorFlow and share that information back into the Elastic Stack.
- Overview of TensorFlow
- TensorFlow Libraries and Extensions
- Neural Networks
- Exploring Results with the Elastic Stack
This course is a module of the Data Science specialization. Find out how our focused Training Specializations can help you with your use case.
Data Scientists, Software Developers
Virtual - 1 Day | 2-3 hours
- We recommend taking the following foundational courses (or having equivalent knowledge):
- Some exposure to machine learning concepts
- Basic Python
- Stable internet connection
- Mac, Linux, or Windows
- Latest version of Chrome or Firefox (other browsers not supported)
- Disable any ad blockers and restart your browser before class