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>Write a utility function to stack multiple fire modules</li>
</ul>


<h2>A. Utility function</h2>

<div id="section1_l4" class="collapse show">
<p>While the SqueezeNet model uses very few parameters, it still has many layers. The original architecture, which was built for the larger ImageNet dataset, uses 8 fire modules. Our model is a condensed version, and only uses 4. However, that is still several fire modules, making it useful to write a utility function that can stack multiple layers.</p>
<p>In general, when we deal with more complex model architectures, there are going to be repetitions of the main building blocks in the model. Rather than writing layers upon layers of the same code, we can create a utility function that allows us to combine multiple building blocks. This is especially helpful for models that have dozens or even hundreds of layers, which is the case in the <b>ResNet</b> section.</p>
</div>


<h2>Chapter Goals:</h2>
<ul>
	<li>Write a utility function to stack multiple fire modules</li>
</ul>


<h2>A. Utility function</h2>

<div id="section1_l4">
<p>While the SqueezeNet model uses very few parameters, it still has many layers. The original architecture, which was built for the larger ImageNet dataset, uses 8 fire modules. Our model is a condensed version, and only uses 4. However, that is still several fire modules, making it useful to write a utility function that can stack multiple layers.</p>
<p>In general, when we deal with more complex model architectures, there are going to be repetitions of the main building blocks in the model. Rather than writing layers upon layers of the same code, we can create a utility function that allows us to combine multiple building blocks. This is especially helpful for models that have dozens or even hundreds of layers, which is the case in the <b>ResNet</b> section.</p>
</div>


Stack multiple fire modules together by creating a utility function.

What you'll learn from this course

Image Processing

CNN

SqueezeNet

ResNet

Stacking Fire

Chapter Goals:

A. Utility function