Learn machine learning algorithms for image recognition, which is used in various applications in computer vision and self-driving cars.

Neural Networks: Beginners to Advanced

If you want to dive into the technology behind computer vision and self-driving cars, this is where to start. In this module, you'll learn how to process data from image files and create convolutional neural networks (CNNs) to classify different types of images. 

The code for this module 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. 

After completing this module, you will have a solid understanding of why CNNs work and which CNN architectures to use for different tasks.

Neural Networks for Image Recognition

Congratulations! We’ve successfully completed the “Neural Networks for Image Recognition” module.

## Summary
We began by learning the basics of image processing and eventually applied our learning to practical problems using various tools such as CNN, SqueezeNet, and ResNet. We not only learned the theoretical concepts but also had the hands-on practice of image recognition on the datasets of MINST and CIFAR-10.

## What’s next?
Now that we are familiar with image recognition with machine learning, it’s time for us to focus on creating GAN using PyTorch.


Congratulations! We’ve successfully completed the “Neural Networks for Image Recognition” module.

# Summary
We began by learning the basics of image processing and eventually applied our learning to practical problems using various tools such as CNN, SqueezeNet, and ResNet. We not only learned the theoretical concepts but also had the hands-on practice of image recognition on the datasets of MINST and CIFAR-10.

# What’s next?
Now that we are familiar with image recognition with machine learning, it’s time for us to focus on creating GAN using PyTorch.


Here's a quick summary of the "Neural Networks for Image Recognition" module.

Image Processing

CNN

SqueezeNet

ResNet

Conclusion

Final Remarks

Summary