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

## 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>
</div>


# Chapter Goals:

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


Increase the SqueezeNet model's depth for added performance.

Image Processing

CNN

SqueezeNet

ResNet

Conclusion

Depth

Chapter Goals:

A. Deeper fire modules