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>Understand how the ResNet architecture is structured</li>
</ul>


# A. Initial layers

<div id="section1_l9" class="collapse show">
<p>Prior to the block layers, the ResNet model uses a convolution layer and max pooling layer on the input data. The convolution layer uses 7x7 kernels, which is larger than we've seen in previous models. The increased kernel size is specifically for the ImageNet dataset, which has larger images than our previous datasets.</p>
<p>The convolution layer uses a stride size of 2 for dimension reduction. The max pooling layer also uses a stride size of 2. In total, the height and width dimensions of the input are each reduced by a factor of 4 after the initial convolution and pooling layers. This reduces computational cost, since the input data for the first block layer is now 16 times smaller.</p>
</div>


# A. Initial layers

<div id="section1_l9">
<p>Prior to the block layers, the ResNet model uses a convolution layer and max pooling layer on the input data. The convolution layer uses 7x7 kernels, which is larger than we've seen in previous models. The increased kernel size is specifically for the ImageNet dataset, which has larger images than our previous datasets.</p>
<p>The convolution layer uses a stride size of 2 for dimension reduction. The max pooling layer also uses a stride size of 2. In total, the height and width dimensions of the input are each reduced by a factor of 4 after the initial convolution and pooling layers. This reduces computational cost, since the input data for the first block layer is now 16 times smaller.</p>
</div>


Learn about the initial and final layers for ResNet.

What you'll learn from this course

Image Processing

CNN

SqueezeNet

ResNet

Model Layers

Chapter Goals:

A. Initial layers