Setting Up a TensorFlow Lab

Review the tools available for implementing deep neural networks in various generative AI models.

Our primary tool is the TensorFlow 2.0 frameworkAbadi, Martín, et al. (2016) TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv:1603.04467. https://arxiv.org/ abs/1603.04467., developed by GoogleGoogle. TensorFlow. Retrieved April 26, 2021, from https://www.tensorflow.org/; however, we have also used a number of additional resources to make the implementation process easier.

We can broadly categorize these tools:

Resources for replicable dependency management (Docker, Anaconda).
Exploratory tools for data munging and algorithm hacking (Jupyter).
Utilities to deploy these resources to the cloud and manage their life cycle (Kubernetes, Kubeflow, Terraform).

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Introduction to the Course

An Introduction to Generative AI

Building Blocks of Deep Neural Networks

Teaching Networks to Generate Digits

Painting Pictures with Neural Networks Using VAEs

Image Generation with GANs

Style Transfer with GANs

Assessment: Introduction to Generative AI to Style Transfer

Deepfakes with GANs

The Rise of Methods for Text Generation

NLP 2.0: Using Transformers to Generate Text

Composing Music with Generative Models

Play Video Games with Generative AI: GAIL

Emerging Applications in Generative AI

Assessment: Deepfakes using GANs to Emerging Applications

Conclusion

Appendix

Setting Up a TensorFlow Lab