The MNIST Database

Explore the MNIST database as a foundational dataset for training generative models like Deep Belief Networks using TensorFlow. Understand its origins, image preprocessing steps, and how it serves as a benchmark for machine learning. Learn to load, preprocess, and prepare MNIST data to train models that generate realistic digit images.

We'll cover the following...

Retrieving and loading the MNIST dataset in TensorFlow

In developing the DBN model, we’ll use a dataset that we have discussed before—the MNIST database, which contains digital images of hand-drawn digitsLeCun, Yann; Léon Bottou; Yoshua Bengio; Patrick Haffner (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE. 86 (11): 2278–2324 from $0$ to $9$ . This database is a combination of two sets of earlier images from the National Institute of Standards and Technology (NIST): Special Database $1$ (digits written by US high school students) and Special Database 2LeCun, Yann; Corinna Cortes; Christopher J.C. Burges. MNIST handwritten digit database, Yann LeCun, Corinna Cortes, and Chris Burges (written by US Census Bureau employees), the sum of which is split into 60,000 training images and 10,000 test images.

The original images in the dataset were all black and white, while the modified dataset normalized them to fit into a $20$ x $20$ ...

1.Introduction to the Course

2.An Introduction to Generative AI

3.Building Blocks of Deep Neural Networks

4.Teaching Networks to Generate Digits

5.Painting Pictures with Neural Networks Using VAEs

Project

6.Image Generation with GANs

Project

7.Style Transfer with GANs

Assessment

8.Deepfakes with GANs

9.The Rise of Methods for Text Generation

10.NLP 2.0: Using Transformers to Generate Text

11.Composing Music with Generative Models

Project

12.Play Video Games with Generative AI: GAIL

13.Emerging Applications in Generative AI

Assessment

14.Conclusion

15.Appendix

The MNIST Database