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>Add an additional convolution and max pooling layer to the CNN</li>
</ul>


<h2>A. Adding extra layers</h2>

<div id="section1_l5" class="collapse show">
<p>Like all neural networks, CNNs can benefit from additional layers. The additional layers allow a CNN to essentially stack multiple filters together for use on the image data. However, similar to building any neural network, we need to be careful of how many additional layers we add. If we add too many layers to a model, we run the risk of having it overfit to the training data and therefore generalizing very poorly. Furthermore, each additional layer adds computational complexity and increases training time for our model.</p>
<p>Since the MNIST images are pretty simple and only have one channel, we won't need more than one additional convolution and max pooling layer. However, in the next two sections of this course you will build CNNs for classifying much more complex images, necessitating the use of many more convolution layers.</p>
</div>


<h2>Chapter Goals:</h2>
<ul>
	<li>Add an additional convolution and max pooling layer to the CNN</li>
</ul>


<h2>A. Adding extra layers</h2>

<div id="section1_l5">
<p>Like all neural networks, CNNs can benefit from additional layers. The additional layers allow a CNN to essentially stack multiple filters together for use on the image data. However, similar to building any neural network, we need to be careful of how many additional layers we add. If we add too many layers to a model, we run the risk of having it overfit to the training data and therefore generalizing very poorly. Furthermore, each additional layer adds computational complexity and increases training time for our model.</p>
<p>Since the MNIST images are pretty simple and only have one channel, we won't need more than one additional convolution and max pooling layer. However, in the next two sections of this course you will build CNNs for classifying much more complex images, necessitating the use of many more convolution layers.</p>
</div>


Discover the nuances of increasing the size and depth of a CNN.

What you'll learn from this course

Image Processing

CNN

SqueezeNet

ResNet

Multiple Layers

Chapter Goals:

A. Adding extra layers