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

In this section of the course you will be building a memory-efficient CNN known as [SqueezeNet](https://arxiv.org/pdf/1602.07360.pdf). Although small, its performance is on par with much larger models such as [AlexNet](https://en.wikipedia.org/wiki/AlexNet), the breakthrough deep CNN that won the 2012 [ImageNet Challenge](https://en.wikipedia.org/wiki/ImageNet#ImageNet_Challenge). We will train the model on the [CIFAR-10](https://en.wikipedia.org/wiki/CIFAR-10) dataset, which contains 60,000 images in 10 different classes.



What you'll learn from this course

Image Processing

CNN

SqueezeNet

ResNet

Introduction