Learn machine learning algorithms for image recognition, which is used in various applications in computer vision and self-driving cars.

Neural Networks: Beginners to Advanced

If you want to dive into the technology behind computer vision and self-driving cars, this is where to start. In this module, you'll learn how to process data from image files and create convolutional neural networks (CNNs) to classify different types of images. 

The code for this module 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. 

After completing this module, you will have a solid understanding of why CNNs work and which CNN architectures to use for different tasks.

Neural Networks for Image Recognition

<p>In this section you will learn about image processing with <b>TensorFlow</b> and <b>PIL</b>. Before doing any image-related tasks, we need to convert the images into a usable form for deep learning models. TensorFlow and PIL provide us utilities that make image processing very straightforward.</p>



<h4>A. Image recognition</h4>

<div id="section1_l0" class="collapse show">
<p>Computer vision, the field concerning machines being able to understand images and videos, is one of the hottest topics in the tech industry. Robotics, self-driving cars, and facial recognition all rely on computer vision to work. At the core of computer vision is <i>image recognition</i>, the task of recognizing what an image represents.</p>
<p>Before performing any task related to images, it is almost always necessary to first process the images to make them more suitable as input data. This section of the course focuses on image processing, specifically how we can convert images from JPEG or PNG files to usable data for our neural networks.
</div>


# A. Image recognition

<div id="section1_l0" class="collapse show">
<p>Computer vision, the field concerning machines being able to understand images and videos, is one of the hottest topics in the tech industry. Robotics, self-driving cars, and facial recognition all rely on computer vision to work. At the core of computer vision is <i>image recognition</i>, the task of recognizing what an image represents.</p>
<p>Before performing any task related to images, it is almost always necessary to first process the images to make them more suitable as input data. This section of the course focuses on image processing, specifically how we can convert images from JPEG or PNG files to usable data for our neural networks.
</div>


# A. Image recognition

<div id="section1_l0">
<p>Computer vision, the field concerning machines being able to understand images and videos, is one of the hottest topics in the tech industry. Robotics, self-driving cars, and facial recognition all rely on computer vision to work. At the core of computer vision is <i>image recognition</i>, the task of recognizing what an image represents.</p>
<p>Before performing any task related to images, it is almost always necessary to first process the images to make them more suitable as input data. This section of the course focuses on image processing, specifically how we can convert images from JPEG or PNG files to usable data for our neural networks.
</div>


Image Processing

CNN

SqueezeNet

ResNet

Conclusion

Introduction

A. Image recognition