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 class="section-title">Chapter Goals:</h2>
<ul>
	<li>Learn how to create a dataset of image files</li>
	<li>Convert a dataset of image files into decoded pixel data</li>
</ul>


<h2>A. Image dataset</h2>

<div id="section1_l4" class="collapse show">
<p>Normally when we do image related tasks we're dealing with a large amount of image data. In this case, it's best to use a TensorFlow dataset, i.e. <code><a href="https://www.tensorflow.org/api_docs/python/tf/data/Dataset" target="_blank">tf.data.Dataset</a></code>, to store all the images. We can create a dataset using the <code><a href="https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices" target="_blank">from_tensor_slices</a></code> function.</p>
<p>The <code>Dataset</code> class makes it easier and more efficient to perform tasks with all the image files. If you want to learn more about using TensorFlow's <code>tf.data</code> API, check out the <a href='https://www.educative.io/collection/6083138522447872/5873588626259968/'>Industry Case Study</a> course on Educative.</p>
</div>


<h2>Chapter Goals:</h2>
<ul>
	<li>Learn how to create a dataset of image files</li>
	<li>Convert a dataset of image files into decoded pixel data</li>
</ul>


<h2>A. Image dataset</h2>

<div id="section1_l4">
<p>Normally when we do image related tasks we're dealing with a large amount of image data. In this case, it's best to use a TensorFlow dataset, i.e. <code><a href="https://www.tensorflow.org/api_docs/python/tf/data/Dataset" target="_blank">tf.data.Dataset</a></code>, to store all the images. We can create a dataset using the <code><a href="https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices" target="_blank">from_tensor_slices</a></code> function.</p>
<p>The <code>Dataset</code> class makes it easier and more efficient to perform tasks with all the image files. If you want to learn more about using TensorFlow's <code>tf.data</code> API, check out the <a href='https://www.educative.io/collection/6083138522447872/5873588626259968/'>Industry Case Study</a> course on Educative.</p>
</div>


Create a dataset of image files and convert them into pixel data.

What you'll learn from this course

Image Processing

CNN

SqueezeNet

ResNet

Dataset

Chapter Goals:

A. Image dataset