Gain insights into image data processing and CNNs for image classification using TensorFlow. Delve into CNN architectures and their applications, requiring Python and TensorFlow knowledge.

irml.tar.gz

image-rec

If you want to dive into the technology behind computer vision and self driving cars, this is where to start.

In this course you'll learn how to process data from image files and create convolutional neural networks (CNNs) to classify different types of images. After completing this course, you will have a solid understanding of why CNNs work and which CNN architectures to use for different tasks.

The code for this course is built around the TensorFlow framework, one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Knowledge of Python and TensorFlow are prerequisites. 

This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

Image Recognition with Machine Learning

<h2>A. Closing thoughts</h2>

Image recognition is one of the most essential use cases of machine learning right now, and is inherently relevant to any task involving images or videos. From healthcare and agriculture to manufacturing and retail, many companies across a variety of different industries are leveraging these technologies to get ahead.


<h2>B. Course recap</h2>
This course has provided an overview of how to perform image recognition from data processing to model predictions. You’ll be able to take the practical lessons and actionable insights from this course and apply them to your projects. 

<h2>C. Topics covered</h2>
<ul>
<li> Image processing </li>
<li> Convolutional neural networks </li>
<li> Image classification </li>
<li> SqueezeNet </li>
<li> ResNet </li>

<h2>D. Industry standard frameworks used in this course</h2>
<ul>
<li>NumPy</li>
<li>Pillow</li>
<li>TensorFlow</li>

What you'll learn from this course

Image Processing

CNN

SqueezeNet

ResNet

Course Conclusion

A. Closing thoughts

B. Course recap

C. Topics covered

D. Industry standard frameworks used in this course