Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Generative AI
- Defining Generative AI
- The history and progression of Generative AI
- Essential concepts and key terminology
- A look at the applications and potential of Generative AI
Machine Learning Fundamentals
- Getting started with machine learning
- Categories of machine learning: Supervised, Unsupervised, and Reinforcement Learning
- Fundamental algorithms and models
- Preparing data and engineering features
Deep Learning Essentials
- Neural networks and the basics of deep learning
- Activation functions, loss functions, and optimization techniques
- Addressing overfitting, underfitting, and applying regularization
- An introduction to TensorFlow and PyTorch
Overview of Generative Models
- Various types of generative models
- Distinguishing between discriminative and generative models
- Practical use cases for generative models
Variational Autoencoders (VAEs)
- Comprehending autoencoders
- The structural design of VAEs
- The concept of latent space and its importance
- Practical exercise: Constructing a basic VAE
Generative Adversarial Networks (GANs)
- Introduction to GANs
- GAN architecture: The Generator and the Discriminator
- Training GANs and associated challenges
- Practical exercise: Developing a basic GAN
Advanced Generative Models
- Introduction to Transformer architectures
- An overview of GPT (Generative Pretrained Transformer) models
- Utilizing GPT for text generation
- Practical exercise: Generating text using a pre-trained GPT model
Ethics and Broader Impacts
- Ethical considerations in the use of Generative AI
- Addressing bias and ensuring fairness in AI models
- Future impacts and the practice of responsible AI
Industry Applications of Generative AI
- Generative AI in the realms of art and creativity
- Applications in business operations and marketing
- The role of Generative AI in science and research
Capstone Project
- Conceiving and proposing a Generative AI project
- Gathering and preparing the dataset
- Selecting and training the model
- Evaluating outcomes and presenting results
Conclusion and Path Forward
Requirements
- Proficiency in fundamental Python programming concepts
- Familiarity with basic mathematical principles, particularly in probability and linear algebra
Target Audience
- Software Developers
14 Hours
Testimonials (2)
the tips and recommended prompts that we can take away from this training
Lee Mei Lin - ST Engineering IHQ Pte Ltd
Course - InVideo AI: Creating Engaging Short-Form Videos
use of proper and effective prompt
