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Overview
The Generative AI for Business Transformation course offers a deep dive into the world of generative artificial intelligence, focusing on its transformative potential for businesses. Participants will explore advanced generative models, including GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), while learning how to apply these models in various business contexts. This course covers practical applications in image and text generation, data augmentation, creative content creation, and more, equipping participants with the skills to leverage generative AI for innovation and business growth.
Objectives
By the end of this course, leaner will be able to:
- Gain an in-depth understanding of generative AI models and their business applications.
- Learn about key generative models such as GANs and VAEs.
- Apply generative AI techniques in practical business scenarios, including image synthesis, text generation, and data augmentation.
- Understand and implement evaluation metrics for assessing generative models.
- Utilize generative AI to drive innovation and efficiency in business processes
Prerequisites
- Fundamental understanding of machine learning concepts and algorithms.
- Familiarity with Python or other programming languages.
- Understanding of neural networks and deep learning principles.
- Experience with deep learning frameworks like TensorFlow or PyTorch is beneficial but not mandatory.
Course Outline
Module 1: Machine Learning and Industry Use CasesCourses2024-08-16T20:37:42+05:30
- Overview of machine learning applications in various industries.
- Discussion on how AI is transforming business operations.
Module 2: Introduction to Deep Learning & TensorFlowCourses2024-08-16T20:58:15+05:30
- Basics of deep learning.
- Introduction to TensorFlow as a tool for building AI models.
Module 3: Introduction to CNNs & RNNsCourses2024-08-16T20:36:12+05:30
- Understanding CNNs and their applications.
- Hands-on project: “What’s that Pet?” – A pet image classification task.
- Techniques for evaluating, improving, and tuning CNNs.
Module 4: Generative Adversarial Networks with PythonCourses2024-08-16T20:34:41+05:30
- Introduction to GANs.
- Hands-on project: Building GANs using Python.
- Techniques for tuning and improving GAN performance.
Module 5: Advanced Generative Adversarial NetworksCourses2024-08-16T20:33:14+05:30
- Deep dive into advanced GAN architectures and applications.
- Case studies of GANs in business applications.
