Masterclass Generative AI for Research and Development

Raj Kumar Thokala

This course covers many topics in Generative AI for Research and Development. Generative AI is a branch of artificial intelligence that focuses on creating new content, including text, images, music, and even code, by learning patterns from existing data. Unlike traditional AI models that primarily analyze and classify data, generative AI can produce human-like outputs, making it a powerful tool for automation, creativity, and problem-solving. With advancements in deep learning and neural networks, generative AI has revolutionized industries such as entertainment, marketing, healthcare, and software development.

What's inside

Learning objectives

Master generative ai for research and development
Understand generative ai principles

Learn generative ai best practices for research and development
Understand the concepts for generative models

Master generative ai for research and development
Understand generative ai principles
Learn generative ai best practices for research and development
Understand the concepts for generative models

Syllabus

Introduction

Basics of Generative AI

What is Generative AI

Generative AI Models

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Masterclass Generative AI for Research and Development with these activities:

Review Basics of Deep Learning

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Refresh your understanding of deep learning concepts to better grasp the underlying mechanisms of generative AI models.

Browse courses on Deep Learning

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Review neural network architectures.
Study backpropagation and gradient descent.
Familiarize yourself with common activation functions.

Read 'Hands-On Generative AI with Python and TensorFlow 2.0' by Pramod Singh

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Supplement your learning with a hands-on guide to implementing generative AI models using Python and TensorFlow.

View Alter Ego on Amazon

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Work through the code examples in the book.
Adapt the examples to different datasets and applications.
Experiment with different model architectures and hyperparameters.

Read 'Generative Deep Learning' by David Foster

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Gain a deeper understanding of generative models by studying a dedicated textbook on the subject.

View Generative Deep Learning on Amazon

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Read the chapters on GANs and VAEs.
Experiment with the code examples provided in the book.
Summarize key concepts from each chapter.

Four other activities

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Experiment with Data Augmentation Techniques

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Improve your understanding of data augmentation by practicing different techniques on a sample dataset.

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Select a dataset for experimentation.
Implement various data augmentation techniques (e.g., rotation, scaling, noise addition).
Evaluate the impact of augmentation on model performance.

Build a Simple Text Generator

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Solidify your understanding of LLMs and NLP pipelines by building a basic text generation model.

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Choose a dataset of text (e.g., Shakespeare, news articles).
Implement tokenization and data preprocessing.
Train a simple LSTM or Transformer model.
Generate text samples and evaluate the results.

Create a Blog Post on Generative AI Applications

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Reinforce your knowledge by researching and writing about real-world applications of generative AI in R&D.

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Research different applications of generative AI.
Choose a specific application to focus on.
Write a blog post explaining the application and its benefits.
Include examples and visuals to illustrate your points.

Create a Presentation on Ethical Considerations in Generative AI

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Deepen your understanding of the ethical implications of generative AI by preparing and delivering a presentation on the topic.

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Research ethical concerns related to generative AI (e.g., bias, misinformation).
Prepare a presentation outlining these concerns and potential mitigation strategies.
Present your findings to a group of peers or colleagues.

Career center

Learners who complete Masterclass Generative AI for Research and Development will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Masterclass Generative AI for Research and Development.

Generative Deep Learning

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Provides a practical introduction to generative deep learning models. It covers a wide range of models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Recurrent Neural Networks (RNNs), with code examples. It's a great resource for understanding the underlying mechanisms and implementing generative AI models, which aligns perfectly with the course's focus on generative AI principles and model understanding. This book would be valuable as additional reading to deepen the understanding of the models discussed in the course.

Generative Deep Learning

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Generative Deep Learning

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Alter Ego

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Offers a practical, hands-on approach to learning generative AI using Python and TensorFlow 2.0. It covers a wide range of generative models, including GANs, VAEs, and autoregressive models, with detailed code examples and step-by-step instructions. The book is particularly useful for those who prefer a practical, code-focused learning style. It provides a solid foundation for building and deploying generative AI applications. This book is best used as a reference while taking the course.