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>Learn how a layer of blocks is organized</li>
	<li>Understand the function for creating a layer of blocks</li>
</ul>


<h2>A. Block layers</h2>

A ResNet model is made up of four block layers. Each block layer will contain a different number of blocks, depending on the total number of weight layers in the ResNet model. For example, an 18 layer ResNet model has 2 blocks in each block layer.

The blocks within each block layer are connected, so the output of block $\small i$ is the input of block $\small i + 1$ in the same layer. Furthermore, the four block layers themselves are connected, so the output of block layer $\small j$ becomes the input of block layer $\small j + 1$.
</div>


Stack multiple blocks together to create a block layer in ResNet.

What you'll learn from this course

Image Processing

CNN

SqueezeNet

ResNet

Block Layer

Chapter Goals:

A. Block layers