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>Learn about the CIFAR-10 dataset</li>
	<li>Initialize the model with data dimensions</li>
</ul>

<h2>A. CIFAR-10</h2>

The CIFAR-10 (Canadian Institute for Advanced Research) dataset contains 60,000 color images with dimensions 32x32. The images are distributed evenly across 10 categories: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck. We split the dataset into 50,000 images for training and 10,000 images for testing.

The CIFAR-10 dataset is available for download (along with the 100 category version, CIFAR-100), on Alex Krizhevsky's <a href="https://www.cs.toronto.edu/~kriz/cifar.html" target="_blank">website</a>.
(Fun fact: Alex Krizhevsky helped invent the AlexNet model mentioned in the previous chapter, which incidentally was named after him. The LeNet model from the CNN section was also named after its inventor, Yann LeCun. SqueezeNet, however, is not named after its developers.)

<h2>Chapter Goals:</h2>
<ul>
	<li>Learn about the CIFAR-10 dataset</li>
	<li>Initialize the model with data dimensions</li>
</ul>

The CIFAR-10 dataset is available for download (along with the 100 category version, CIFAR-100), on Alex Krizhevsky's <a href="https://www.cs.toronto.edu/~kriz/cifar.html" target="_blank">website</a>.
(Fun fact: Alex Krizhevsky helped invent the AlexNet model mentioned in the previous chapter, which incidentally was named after him. The LeNet model from the CNN section was also named after its inventor, Yann LeCun. SqueezeNet, however, is not named after its developers.)

Learn about the CIFAR-10 dataset along with the input and output sizes of the data.

What you'll learn from this course

Image Processing

CNN

SqueezeNet

ResNet

Initialization

Chapter Goals:

A. CIFAR-10