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Overview
Generative AI with Python and Pytorch is a comprehensive course designed to take learners on an in-depth journey through the world of generative AI. This course will cover everything from the foundational concepts of generative adversarial networks (GANs) to advanced multimodal AI architectures. Learners will gain hands-on experience coding these architectures from scratch using Python and Pytorch, enabling them to understand both the theory and the practical implementation of cutting-edge generative models.
Objectives
By the end of this course, leaner will be able to:
- Master Generative AI Architectures: Learn how to code various generative AI architectures from scratch, including GANs, Stable Diffusion, and multimodal AI models.
- Deep Dive into AI Concepts: Gain an in-depth understanding of the key concepts behind generative AI, with detailed explanations before each coding section.
- Hands-on Coding Experience: Implement generative networks that produce human faces, create images from text prompts, and more, through hands-on coding exercises.
- Explore Advanced Techniques: Learn to combine different AI models, such as segmentation architectures with generative models, for advanced applications like image editing.
- Understand Neural Network Latent Space: Delve into the latent space of neural networks to understand how they learn mappings, with a special guided visualization section.
Prerequisites
- Basic Python Programming Skills: A solid understanding of Python is required to follow the coding exercises.
- Familiarity with Deep Learning Concepts: Some prior knowledge of deep learning and neural networks is recommended.
- Interest in AI and Machine Learning: A strong interest in artificial intelligence and its creative applications is essential.
- Willingness to Learn by Doing: The course is hands-on, so a desire to code and experiment with AI models is important.
- Understanding of Pytorch (Optional): Some experience with Pytorch will be beneficial, but not mandatory.
Course Outline
Module 1: Introduction to Generative AI and GANsCourses2024-08-19T15:13:46+05:30
- Overview of generative AI and its significance.
- Understanding and coding basic GAN architectures.
Module 2: Advanced Generative NetworksCourses2024-08-19T15:17:38+05:30
- In-depth coding of advanced generative models.
- Creating human faces with generative networks.
Module 3: Multimodal AI – Text and Image IntegrationCourses2024-08-19T15:18:27+05:30
- Combining text prompts with generative models to create images.
- Coding exercises for multimodal AI applications.
Module 4: Image Editing with Generative AICourses2024-08-19T15:20:30+05:30
- Programming a combination of segmentation and generative models.
- Practical example: Editing clothes in a picture.
Module 5: Exploring Latent Space in Neural NetworksCourses2024-08-19T15:21:36+05:30
- Journey into the latent space of neural networks.
- Guided visualization and advanced concepts in neural network mappings.
