Generative AI Training:

Generative AI Training Course with Hands-On Exercises (Online, Onsite and Classroom Live)

Generative AI Training is a comprehensive course that covers the fundamentals of generative AI, including Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL). The Generative AI course is designed for beginners and intermediate learners, and it provides a solid foundation in the principles of generative AI. Participants will also learn about ChatGPT and  OpenAI ChatGPT API, which allows developers to integrate ChatGPT into their own applications, including websites.

The Generative AI course begins with an introduction to generative AI, and it then covers the topics like Natural language processing, Machine learning, and Deep learning. The Generative AI course also includes a number of hands-on projects that allow learners to apply the concepts they have learned.

What’s Included

• 2 days of Generative AI Training with an expert instructor
• Generative AI Electronic Course Guide
• Certificate of Completion
• 100% Satisfaction Guarantee

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Customize it:

• We can tailor this Generative AI Training course to your participants’ backgrounds and needs. We can include or exclude certain topics and shorten or lengthen the course duration.
• We can adjust the emphasis placed on the various topics or build the Generative AI Training course around the mix of technologies of interest to you (including technologies other than those included in this outline).
• If your background is nontechnical, we can exclude the more technical topics, include the topics that may be of special interest to you (e.g., as a manager or policy-maker), and present the Generative AI Training course in manner understandable to lay audiences.

Generative AI – Prerequisite:

The knowledge and skills that a learner must have before attending this Generative AI course are:

• N/A

Generative AI Training – Audience/Target Group:

Generative AI Training is a great course for anyone who wants to learn about generative AI. The Generative AI  course is designed for beginners and intermediate learners, so it is suitable for people with a variety of backgrounds. Some of the people who would benefit from attending this course include:

• Software developers
• Data scientists
• Business professionals
• Students and,
• Anyone interested in learning about generative AI.

Learn the topics such as generative modeling techniques, deep learning frameworks, and applications of generative AI in various fields, such as healthcare, finance, and entertainment.

Generative AI Training – Objectives

Upon completion of this Generative AI course, participants are able to:

• Learn the fundamentals of generative AI and its applications.
• Explain various generative AI models and algorithms.
• Develop skills to implement and train generative AI models.
• Learn techniques for evaluating and improving generative AI models.
• Explore advanced topics in generative AI, such as conditional generation, style transfer, and text-to-image synthesis.
• Apply generative AI techniques to real-world problems and creative applications.
• Explore ethical considerations and challenges associated with generative AI.

Generative AI Training – Course Outlines:

INTRODUCTION TO GENERATIVE AI

• What is Generative AI?
• History of Generative AI
• Applications of Generative AI
• Fundamentals of ChatGPT
• Overview of generative AI models: GANs, VAEs, autoregressive models

INTRODUCTION TO MACHINE LEARNING

• What is machine learning?
• Supervised learning
• Unsupervised learning
• Reinforcement learning

INTRODUCTION TO DEEP LEARNING

• What is deep learning?
• Neural networks
• Convolutional neural networks
• Recurrent neural networks

FUNDAMENTALS OF NATURAL LANGUAGE PROCESSING (NLP)

• What is natural language processing?
• Tokenization
• Stemming
• Lemmatization
• Part-of-speech tagging

FUNDAMENTALS OF GENERATIVE MODELS

• Probability theory and generative modeling
• Maximum likelihood estimation
• Latent space and latent variable models
• Generative Adversarial Networks (GANs)
• GAN architecture and components
• Training GANs: minimax game and adversarial training
• GAN variants: DCGAN, WGAN, CycleGAN
• Variational Autoencoders (VAEs)
• Autoencoders and variational inference
• Conditional generation with VAEs
• Autoregressive Models
• PixelCNN and PixelRNN
• Transformers for generative modeling

EVALUATING AND IMPROVING GENERATIVE MODELS

• Metrics for evaluating generative models
• Challenges and techniques for improving model performance
• Understanding and mitigating mode collapse

ADVANCED TOPICS IN GENERATIVE AI

• Conditional generation and controllable synthesis
• Style transfer and image-to-image translation
• Text-to-image synthesis and image captioning
• Ethical Considerations in Generative AI
• Bias and Fairness in generative models
• Intellectual property (IP) and copyright issues
• Privacy Concerns and data ethics
• Future Directions and Wrap-up

HANDS-ON PROJECTS

• Integrating Building a Chatbot
• Generating text
• Translating languages
• Writing different kinds of creative content
• OpenAI ChatGPT API
• Integrate ChatGPT into your own applications, including websites, mobile apps, productivity software and more.
• Integration of Generative AI and Trusted AI

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