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Generative AI with Python and TensorFlow 2

Explore generative AI with Python and TensorFlow 2, mastering advanced algorithms, implementing models, and leveraging cloud resources to future-proof your skills and lead the GenAI revolution.

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With recent improvements in machine learning and deep learning, generative modeling has seen a tremendous uptick in the number of research works and its applications across different areas. Some of the newer methods (such as GANs) are very powerful yet difficult to control, making the overall learning process both exciting and frustrating. In this course, you’ll explore generative AI, a cutting-edge technology for generating synthetic (yet strikingly realistic) data using advanced machine learning algorithms. You’ll learn the theory and fundamentals and discover the potential and impact of these models through worked examples. You’ll also implement these models using a variety of open-source technologies—the Python programming language, the TensorFlow 2 library for deep neural network development, and cloud computing resources such as Google Colab and the Kubeflow project. Taking this course will help learners explore more complex topics and cutting-edge research with ease.

With recent improvements in machine learning and deep learning, generative modeling has seen a tremendous uptick in the number o...Show More

WHAT YOU'LL LEARN

An understanding of generative models, including their applications, principles of probability, and various techniques used in generative AI

Working knowledge of deep learning, including perceptrons, backpropagation, convolutional neural networks (CNNs), and recurrent neural networks (RNNs)

Hands-on experience implementing generative AI models using TensorFlow 2

Familiarity with emerging applications of generative AI

An understanding of generative models, including their applications, principles of probability, and various techniques used in generative AI

TAKEAWAY SKILLS

TensorFlow

Python

Generative AI

Content

103 Lessons11 Quizzes2 Assessments

Introduction to the Course

1 Lessons

Get familiar with generative AI fundamentals, focusing on GANs, VAEs, and practical applications.

Getting Started

An Introduction to Generative AI

7 Lessons

Look at generative AI's capabilities, applications, probability rules, and modeling benefits.

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

8 Lessons

Examine deep neural networks' architecture, training, specialized models, and optimization techniques.

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

8 Lessons

Grasp the fundamentals of training Deep Belief Networks with TensorFlow 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

7 Lessons

Deepen your knowledge of using VAEs in TensorFlow for image reconstruction and generation.

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

Recognize Handwritten Digits Using a Deep Neural Network

Project

Image Generation with GANs

11 Lessons

Explore generative adversarial networks (GANs), their variants, training processes, and common challenges.

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

Dataset Augmentation with GANs

Project

Style Transfer with GANs

6 Lessons

Master the steps to creative image transformations using GANs for paired and unpaired style transfers.

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

Assessment: Introduction to Generative AI to Style Transfer

Assessment

Deepfakes with GANs

11 Lessons

Learn how to use deepfake technology, focusing on GAN architectures, face swapping, and ethical concerns.

Introduction: Deepfakes with GANs

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

7 Lessons

Discover the logic behind text generation methods, including RNNs, LSTMs, and convolutional networks.

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

Exploring OpenAI API

Project

10.

NLP 2.0: Using Transformers to Generate Text

5 Lessons

Go hands-on with transformer architectures, key NLP concepts, and advanced text generation.

Introduction: Using Transformers to Generate Text

Transformers

GPT Models

Quiz: Using Transformers to Generate Text

Summary: Using Transformers to Generate Text

11.

Composing Music with Generative Models

7 Lessons

Enhance your skills in AI-based music generation using RNNs, GANs, and multi-track 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

Generating New Music with Artificial Intelligence

Project

12.

Play Video Games with Generative AI: GAIL

8 Lessons

Solve problems in reinforcement learning using GAIL and adversarial imitation techniques.

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

13.

Emerging Applications in Generative AI

6 Lessons

Investigate emerging generative AI applications in drug discovery, PDE solutions, video creation, and recipe generation.

Introduction: Applications in Generative AI

Discovering New Drugs

Solving Partial Differential Equations

Creating Videos from Images

Generating Recipes

Summary: Applications in Generative AI

Assessment: Deepfakes using GANs to Emerging Applications

Assessment

14.

Conclusion

1 Lessons

Build on the comprehensive exploration of generative models and their practical applications.

Summary

15.

Appendix

9 Lessons

Learn how to use tools like TensorFlow, Docker, Kubernetes, and Kubeflow for effective AI development.

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

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Packt

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Frequently Asked Questions

Can I use TensorFlow for generative AI?

Yes, you can use TensorFlow for generative AI by implementing models like GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders) to generate new data, such as images, text, and music.

What is GAN in AI?

GAN (Generative Adversarial Network) in AI is a framework consisting of two neural networks, a generator and a discriminator, that compete to generate realistic data samples and distinguish between real and fake data.