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 MNIST database</li>
	<li>Initialize the model with input and output size</li>
</ul>


<h2>A. MNIST</h2>

<div id="section1_l1" class="collapse show">
<p>The MNIST (Modified National Institute of Standards and Technology) database contains 60,000 training examples and 10,000 testing examples. The database contains grayscale handwritten digits that were resized to fit in a 20x20 pixel box, which was then centered in a 28x28 image (padded with whitespace). The images were resized using an <a href="https://en.wikipedia.org/wiki/Spatial_anti-aliasing" target="_blank">anti-aliasing</a> technique to minimize image distortion.</p>
</div>


<h2>Chapter Goals:</h2>
<ul>
	<li>Learn about the MNIST database</li>
	<li>Initialize the model with input and output size</li>
</ul>


<h2>A. MNIST</h2>

<div id="section1_l1">
<p>The MNIST (Modified National Institute of Standards and Technology) database contains 60,000 training examples and 10,000 testing examples. The database contains grayscale handwritten digits that were resized to fit in a 20x20 pixel box, which was then centered in a 28x28 image (padded with whitespace). The images were resized using an <a href="https://en.wikipedia.org/wiki/Spatial_anti-aliasing" target="_blank">anti-aliasing</a> technique to minimize image distortion.</p>
</div>


Learn about the MNIST dataset along with the input and output sizes for the data.

What you'll learn from this course

Image Processing

CNN

SqueezeNet

ResNet

Initialization

Chapter Goals:

A. MNIST