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>Obtain the logits for each digit class</li>
</ul>


<h2>A. Multiclass logits</h2>

<div id="section1_l8" class="collapse show">
<p>Since there are 10 possible digits an MNIST image can be, we use a 10 neuron fully-connected layer to obtain the logits for each digit class. The logits are the output of the <code>model_layers</code> function.</p>
<p>The rest of the model follows the standard format for multiclass classification:
	<ul>
		<li>Softmax applied to the logits to convert them into per class probabilities</li>
		<li>The <code>labels</code> are one-hot vectors, where the "hot index" corresponds to the digit in the MNIST image</li>
		<li>Softmax cross entropy to calculate loss</li>
	</ul>
</p>
</div>


<h2>Chapter Goals:</h2>
<ul>
	<li>Obtain the logits for each digit class</li>
</ul>


<h2>A. Multiclass logits</h2>

<div id="section1_l8">
<p>Since there are 10 possible digits an MNIST image can be, we use a 10 neuron fully-connected layer to obtain the logits for each digit class. The logits are the output of the <code>model_layers</code> function.</p>
<p>The rest of the model follows the standard format for multiclass classification:
	<ul>
		<li>Softmax applied to the logits to convert them into per class probabilities</li>
		<li>The <code>labels</code> are one-hot vectors, where the "hot index" corresponds to the digit in the MNIST image</li>
		<li>Softmax cross entropy to calculate loss</li>
	</ul>
</p>
</div>


Use a fully-connected layer to extract multiclass logits from the CNN.

What you'll learn from this course

Image Processing

CNN

SqueezeNet

ResNet

Logits

Chapter Goals:

A. Multiclass logits