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>
- Learn about convolutions</li>
- Write a convolution layer for the neural network


# A. Filters and kernels

As mentioned at the end of the **Image Processing** section, filters play a huge role in image recognition. We use filters to transform inputs and extract features that allow our model to recognize certain images. A very high-level example of this would be a curve detecting filter, which allows our model to distinguish between digits with curves (e.g. $8$) and digits without curves (e.g. $1$).

The weights of a filter are defined through a <i>kernel matrix</i>. The kernel is usually a square matrix and its weights are just floating point numbers.


$$\begin{bmatrix}
0.0 & 1.0 & 0.4 \\
0.1 & 0.0 & 0.0 \\
1.2 & 0.0 & 1.1
\end{bmatrix} $$
Example kernel weight matrix, with 3x3 dimension.

Like all neural network weights, the filter's weights are trainable variables. We train our neural network (via the kernel matrix weights) to produce filters that are able to extract the most useful hidden features.

When the input data has multiple channels, a filter will have a separate kernel matrix per channel. The MNIST dataset only has one channel, but for other types of image data (e.g. RGB), we would train the model to obtain optimal weights for each channel's kernel matrix.





Learn how convolutions work and the role they play in CNNs.

What you'll learn from this course

Image Processing

CNN

SqueezeNet

ResNet

Convolution

Chapter Goals:

A. Filters and kernels