Back To Course Home

Generative AI with Python and TensorFlow 2

0% completed

Introduction to the Course

Getting Started

An Introduction to Generative AI

Introduction: Generative AI Applications of AI Rules of Probability Why Use Generative Models?Style Transfer and Image Transformation Quiz: Generative AI Summary: Generative AI

Building Blocks of Deep Neural Networks

Introduction: Building Blocks of Deep Neural Networks Perceptrons: A Brain in a Function Multilayer Perceptrons and Backpropagation Varieties of Networks: Convolution and Recursive Networks for Sequence Data Building a Better Optimiser Quiz: Building Blocks of Deep Neural Networks Summary: Building Blocks of Deep Neural Networks

Teaching Networks to Generate Digits

Introduction: Teaching Networks to Generate Digits The MNIST Database Restricted Boltzmann Machines Stacking Restricted Boltzmann Machines to Generate Images Creating an RBM using the TensorFlow Keras layers API Creating a DBN with the Keras Model API Quiz: Teaching Networks to Generate Digits Summary: Teaching Networks to Generate Digits

Painting Pictures with Neural Networks Using VAEs

Introduction: Painting Pictures with Neural Networks Using VAEs Creating Separable Encodings of Images The Variational Objective Importing CIFAR Creating the Network from TensorFlow 2 Quiz: Painting Pictures with Neural Networks Using VAEs Summary: Painting Pictures with Neural Networks Using VAEs

Image Generation with GANs

Introduction: Image Generation with GANs Generative Adversarial Networks (GANs)Training GANs Vanilla GAN Improved GANs—Deep Convolutional GAN Improved GANs—Conditional GAN Improved GANs—Wasserstein GAN Progressive GAN Challenges in GAN Training Quiz: Image Generation with GANs Summary: Image Generation with GANs

Style Transfer with GANs

Introduction: Syle Transfer with GANs Paired Style Transfer Using pix2pix GAN Unpaired Style Transfer Using CycleGAN Related Works Quiz: Style Transfer with GANs Summary: Syle Transfer with GANs

Course Assessment

Assessment: Introduction to Generative AI to Style Transfer

Deepfakes with GANs

Introduction: Deepfakes with GANs Deepfakes Overview Modes of Operation Key Feature Set High-Level Workflow Dataset Preparation Replacement Using Autoencoders Reenactment Using pix2pix Challenges Related to Deepfakes Quiz: Deepfakes with GANs Summary: Deepfakes with GANs

The Rise of Methods for Text Generation

Introduction: Methods of Text Generation Representing Text Text Generation and the Magic of LSTMs Decoding Strategies LSTM Variants and Convolutions for Text Quiz: Methods of Text Generation Summary: Methods of Text Generation

NLP 2.0: Using Transformers to Generate Text

Introduction: Using Transformers to Generate Text Transformers GPT Models Quiz: Using Transformers to Generate Text Summary: Using Transformers to Generate Text

Composing Music with Generative Models

Introduction: Composing Music with Generative Models Getting Started with Music Generation Music Generation Using LSTMs Music Generation Using GANs MuseGAN—Polyphonic Music Generation Quiz: Composing Music with Generative Models Summary: Composing Music with Generative Models

Play Video Games with Generative AI: GAIL

Introduction: Play Video Games with Generative AI Reinforcement Learning Inverse Reinforcement Learning Adversarial Learning and Imitation Running GAIL on PyBullet Gym Training and Results Quiz: Play Video Games with Generative AI Summary: Play Video Games with Generative AI

Emerging Applications in Generative AI

Introduction: Applications in Generative AI Discovering New Drugs Solving Partial Differential Equations Creating Videos from Images Generating Recipes Summary: Applications in Generative AI

Course Assessment

Assessment: Deepfakes using GANs to Emerging Applications

Conclusion

Appendix

Setting Up a TensorFlow Lab

Deep Neural Network Development and Tensorflow

VSCode: An Integrated Development Environment

Docker: A Lightweight Virtualization Solution

Kubernetes: Robust Management of Multi-container Applications

Kubeflow: An End-to-end Machine Learning Lab

A Brief Tour of Kubeflow's Components

Kubeflow Pipelines

Using Kubeflow Katib to Optimize Model Hyperparameters

Using Kubeflow Katib to Optimize Model Hyperparameters

Learn how to optimize the parameters of a Kubeflow pipeline.

Now that we have seen the process for generating results for a single pipeline, our next problem is how to generate the optimal parameters for such a pipeline. Kubeflow has a built-in utility for optimizing models for such parameter grids, called Katib.

Get hands-on with 1400+ tech skills courses.