Generative AI with Python and TensorFlow 2

This course will cover generative AI and its applications, including theory, examples, and implementations using Python, TensorFlow, and Kubeflow.

Advanced

103 Lessons

16h

Certificate of Completion

This course will cover generative AI and its applications, including theory, examples, and implementations using Python, TensorFlow, and Kubeflow.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

2 Assessments

62 Playgrounds

11 Quizzes

This course includes

2 Assessments

62 Playgrounds

11 Quizzes

Course Overview

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 algori...Show More

TAKEAWAY SKILLS

Tensorflow

Python

Generative Ai

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

What You'll Learn

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

Course Content

Introduction to the Course

This chapter explores generative AI techniques for creating synthetic data using Python and TensorFlow.

Getting Started

An Introduction to Generative AI

This chapter explores generative AI's potential in creating data, enhancing applications, and addressing challenges.

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

This chapter explores foundational concepts of deep learning, neural networks, 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

This chapter explores deep belief networks and restricted Boltzmann machines for digit generation.

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

This chapter explores how variational autoencoders generate and reconstruct complex images using neural networks.

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

11 Lessons

This chapter explores generative adversarial networks (GANs) and their advanced architectures for image generation.

Style Transfer with GANs

6 Lessons

This chapter explores creative applications of GANs for image style transfer and translation.

Assessment: Introduction to Generative AI to Style Transfer

Assessment

Deepfakes with GANs

11 Lessons

This chapter explores deepfake technology, its applications, challenges, and ethical implications in content creation.

The Rise of Methods for Text Generation

7 Lessons

This chapter explores advanced text generation methods, including LSTMs, representations, and decoding strategies.

10.

NLP 2.0: Using Transformers to Generate Text

5 Lessons

This chapter explores transformer architectures and GPT models for advanced text generation in NLP.

11.

Composing Music with Generative Models

7 Lessons

This chapter explores advancements in AI-driven music generation using LSTMs and GANs.

12.

Play Video Games with Generative AI: GAIL

8 Lessons

This chapter explores generative AI's role in reinforcement learning and imitation for complex environments.

13.

Emerging Applications in Generative AI

6 Lessons

This chapter explores diverse applications of generative AI in drug discovery, PDEs, video synthesis, and recipe generation.

Assessment: Deepfakes using GANs to Emerging Applications

Assessment

14.

Conclusion

1 Lesson

This chapter is a recap of the course content.

15.

Appendix

9 Lessons

This chapter explores the setup and management of TensorFlow and Kubeflow for efficient AI workflows.

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

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