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

# Chapter Goals:

<ul>
	<li>Add depth to the model through additional fire modules</li>
</ul>


<h2>A. Deeper fire modules</h2>


<p>In order to extract more distinguishing features from the data, we add more fire modules to our model. The additional fire modules will use twice as many filters in the expand layer (from 64 to 128). This follows the same approach outlined in the <b>CNN</b> section, where we increase the number of filters used in the convolution layers deeper into the model.</p>
<p>To avoid overfitting, we apply dropout after our second multi-fire module block, with a dropout rate of 0.5. There is no max pooling layer after this multi-fire module block, since we'll obtain logits in the next chapter using average pooling.</p>
<p>The code below shows the <code>model_layers</code> function with added depth:</p>
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Increase the SqueezeNet model's depth for added performance.

What you'll learn from this course

Image Processing

CNN

SqueezeNet

ResNet

Depth

Chapter Goals: