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:</h2>
- Iterate through a dataset and extract pixel data using `tf.compat.v1.Session`



# A. Data iteration</h2>

In the previous chapter we set up a next-element tensor using the `get_next` function. The way we actually execute the data extraction is by using the `run` function of `tf.compat.v1.Session`.

Each time we use `sess.run(next_image)`, we are iterating a single step through our dataset. So the first time we use `sess.run(next_image)`, it'll return the first pixel array in <code>dataset</code> (as a NumPy array), and the $i^{th}$ time it'll return the $i^{th}$ pixel array in `dataset`.


Learn how to execute data extraction in TensorFlow.

What you'll learn from this course

Image Processing

CNN

SqueezeNet

ResNet

Execution

Chapter Goals:

A. Data iteration