Foundational Courses
Generative AI Specialist
To equip learners with a strong foundation in generative AI models.
5
44 enrolled students
Objective
To equip learners with a strong foundation in generative AI models, from autoencoders to advanced GANs and GPT-based text models, along with hands-on experience in image synthesis, style transfer, and conversational AI.
Basic To Advance
You will progress through this course from basics to advanced level.
Duration
3 Months
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Modules
Module 1: Introduction to Autoencoders and Variational Autoencoders
Objective:
Introduce the fundamental concepts of autoencoders and their application in dimensionality reduction and anomaly detection, leading into more advanced Variational Autoencoders (VAEs).
Topics:
- Autoencoders for dimensionality reduction and anomaly detection
- Practical: Implement an autoencoder for image reconstruction
Hands-on Exercise:
Build and Train an Autoencoder for Image Reconstruction
Use a dataset like MNIST or Fashion MNIST to build a simple autoencoder. Train the model to learn a compressed representation of the input images and then reconstruct the images from the compressed representation. After training, evaluate the reconstruction quality visually and calculate reconstruction error metrics (e.g., mean squared error).
Module 2: Generative Adversarial Networks (GANs)
Objective:
Provide a foundational understanding of GANs, covering the roles of the Generator and Discriminator, and exploring the process of adversarial training.
Topics Covered:
- Generator and Discriminator roles, adversarial training
- Practical: Build a DCGAN to generate new images
Hands-on Exercise:
Build a Deep Convolutional GAN (DCGAN) to Generate Images Create a DCGAN using the Fashion MNIST or CIFAR-10 dataset to generate new images. Start by building the generator and discriminator networks, then train the GAN by feeding random noise into the generator and using the discriminator to distinguish between real and fake images. Visualize the generated images over time to track model progress.
Module3: Advanced GAN Architectures
Objective:
Explore advanced GAN architectures like DCGAN, Conditional GAN, and StyleGAN, and examine their applications for generating high-quality images.
Topics Covered:
- DCGAN, Conditional GAN, StyleGAN
- Practical: Experiment with StyleGAN to generate high-quality images
Hands-on Exercise:
Generate High-Quality Images with StyleGAN Use a pre-trained StyleGAN model (available from repositories like NVIDIA’s official StyleGAN or Hugging Face) to generate high-quality synthetic images. Experiment with manipulating latent vectors to control aspects of the generated images (e.g., facial expressions, lighting, or styles).
Fine-tune the model on a custom dataset, if desired, to generate images with specific attributes.
Module 4: Generative Text Models (ChatGPT and GPT-based Models)
Objective:
Dive into the GPT architecture, focusing on fine-tuning and deploying generative text models for applications like chatbots and conversational AI.
Topics:
- GPT architecture, fine-tuning, and deployment
- Practical: Fine-tune a GPT model for a custom text generation task (e.g., chatbot)
Hands-on Exercise:
Fine-Tune GPT-3 or GPT-2 for a Custom Chatbot Task
Use the Hugging Face transformers library to fine-tune a pre-trained GPT model on a custom text corpus related to your chatbot’s domain (e.g., customer service, technical support). Train the model to generate conversational responses based on user input. Evaluate the chatbot’s performance by interacting with it and iterating on the fine-tuning process to improve response quality.
Module 5: Capstone Project
Objective:
Consolidate learning by building a generative AI application, such as a GAN-based image generator or a conversational AI chatbot.
Project:
Develop a generative AI application like a GAN-based image generator or a conversational AI chatbot.
Hands-on Exercise:
- Option 1: GAN-based Image Generator
- Option 2: Conversational AI Chatbot
Frequently Asked Questions
1. What is the Generative AI Specialist course about?
The Generative AI Specialist course is designed to provide in-depth knowledge of generative AI technologies, including machine learning models like GANs, transformers, and large language models. It covers both theoretical foundations and practical applications in various domains.
2. Who is this course for?
This course is suitable for data scientists, AI engineers, software developers, and tech enthusiasts who want to specialize in generative AI. Some prior experience in AI or machine learning is helpful but not mandatory.
3. What will I learn in this course?
You will learn how to build, fine-tune, and deploy generative AI models, including text, image, and audio generators. The course covers key models such as GPT, BERT, StyleGAN, and DALL-E, along with ethical considerations and real-world applications.
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