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 the high-level architecture of SqueezeNet</li>
	<li>Understand the use of non-fire module convolution layers</li>
</ul>


<h2>A. Overview</h2>

<div id="section1_l5" class="collapse show">
<p>As mentioned in the previous chapter, we'll be building a condensed version of the SqueezeNet model. The differences between our model and the original are:
	<ul>
		<li>Our model uses only the first 4 fire modules from the original SqueezeNet (rather than all 8)</li>
		<li>The initial convolution layer for our model uses fewer filters (64 vs. 96) and a smaller kernel size (3x3 vs. 7x7)</li>
		<li>The max pooling layers in our model use smaller filters (2x2 vs. 3x3)</li>
	</ul>
The changes are mainly due to the fact that our dataset is CIFAR-10, which has significantly smaller images and fewer categories than ImageNet, the dataset used to train the original SqueezeNet. We go in-depth on the ImageNet dataset in the <b>ResNet</b> section.</p>
</div>


<h2>Chapter Goals:</h2>
<ul>
	<li>Learn the high-level architecture of SqueezeNet</li>
	<li>Understand the use of non-fire module convolution layers</li>
</ul>


<h2>A. Overview</h2>

<div id="section1_l5">
<p>As mentioned in the previous chapter, we'll be building a condensed version of the SqueezeNet model. The differences between our model and the original are:
	<ul>
		<li>Our model uses only the first 4 fire modules from the original SqueezeNet (rather than all 8)</li>
		<li>The initial convolution layer for our model uses fewer filters (64 vs. 96) and a smaller kernel size (3x3 vs. 7x7)</li>
		<li>The max pooling layers in our model use smaller filters (2x2 vs. 3x3)</li>
	</ul>
The changes are mainly due to the fact that our dataset is CIFAR-10, which has significantly smaller images and fewer categories than ImageNet, the dataset used to train the original SqueezeNet. We go in-depth on the ImageNet dataset in the <b>ResNet</b> section.</p>
</div>


Learn about the high-level architecture of SqueezeNet.

What you'll learn from this course

Image Processing

CNN

SqueezeNet

ResNet

Model Layers

Chapter Goals:

A. Overview