Generative AI Professional Program

Join the Generative AI Professional program and become proficient in creating cutting-edge generative AI solutions!

Course Overview

The Generative AI Professional program is designed to provide participants with comprehensive skills and knowledge in developing, implementing, and optimizing generative AI models and applications. This course covers fundamental concepts and advanced techniques in generative AI, enabling professionals to build robust solutions for various real-world scenarios including text generation, image synthesis, and more.

Program Objectives

By the end of this program, participants will be able to:

1. Understand the principles and fundamentals of generative AI.
2. Work with popular Python libraries and frameworks for generative AI.
3. Develop and deploy generative models for text, images, and other data types.
4. Implement advanced generative techniques such as GANs, VAEs, transformers, and large language models (LLMs).
5. Utilize deep learning techniques for generative AI tasks, including from-scratch development and fine-tuning of LLMs.
6. Apply generative AI solutions to real-world problems in various industries.
7. Optimize and evaluate generative AI models for performance and accuracy.

Target Audience

This program is ideal for:

• Data scientists and machine learning engineers seeking to specialize in generative AI.
• Software developers looking to integrate generative AI capabilities into their applications.
• Researchers and academics interested in the latest advancements in generative AI.
• Students and recent graduates aiming to enter the field of AI and machine learning.

Prerequisites

Participants should have:

• Basic knowledge of Python programming.
• Understanding of fundamental machine learning concepts.
• Familiarity with basic data manipulation and analysis using libraries such as NumPy and Pandas.

Course Modules

Module 1: Introduction to Generative AI

• Overview of generative AI and its applications
• Basic concepts in generative modeling
• Understanding different types of generative models

Module 2: Generative Adversarial Networks (GANs)

• Introduction to GANs and their architecture
• Implementing basic GANs with TensorFlow and PyTorch
• Techniques to improve GAN training: DCGAN, WGAN, and more

Module 3: Variational Autoencoders (VAEs)

• Fundamentals of VAEs and their applications
• Building and training VAEs using TensorFlow and PyTorch
• Exploring latent spaces and generating new data

Module 4: Text Generation with Transformers

• Understanding transformer models and their architecture
• Implementing text generation with GPT-3, GPT-4, and other transformers
• Fine-tuning pre-trained transformer models for specific tasks
• From-scratch development of large language models (LLMs)

Module 5: Image Synthesis and Style Transfer

• Techniques for image generation and synthesis
• Implementing neural style transfer
• Using GANs for high-quality image synthesis

Module 6: Advanced Generative Techniques

• Implementing sequence-to-sequence models for data generation
• Exploring diffusion models and their applications
• Building and training advanced generative models with Hugging Face Transformers

Module 7: Fine-tuning Large Language Models (LLMs)

• Introduction to large language models (LLMs)
• Fine-tuning LLMs for specific NLP tasks
• Evaluating and optimizing fine-tuned models

Module 8: Specialized Generative AI Applications

• Music and audio generation
• Video synthesis and generation
• Creating art and design with generative AI

Module 9: Model Optimization and Deployment

• Techniques for model optimization and acceleration
• Evaluating generative model performance and quality
• Deploying generative AI models in production environments
• Creating REST APIs for generative AI models

Module 10: Hands-On Projects and Applications

• Real-world generative AI projects
• Hands-on assignments and exercises
• Integrating generative AI solutions into applications
• Capstone project: end-to-end generative AI application

Tools and Technologies

• Python: TensorFlow, PyTorch, Hugging Face Transformers, NumPy, Pandas
• Jupyter Notebooks: For interactive coding and model development
• TensorFlow/Keras: For building and training deep learning models
• PyTorch: For advanced deep learning tasks
• Flask/FastAPI: For deploying models as web services

Evaluation and Certification

Participants will be assessed through:

• Quizzes and assignments for each module
• A capstone project demonstrating their generative AI skills
• Final exam

Upon successful completion, participants will receive a "Generative AI Professional" certificate, recognizing their expertise in developing and deploying generative AI applications using Python.

Course Duration

The program is designed to be completed over 12 weeks, with a combination of online lectures, hands-on exercises, and project work.

Enrollment

If the 'Apply for Course' button is active you may enroll apply for enrollment to this course now. For enrollment details and course schedules, please visit our website or contact our admissions office.

Contact Information

• Email: admissions@onecampusacademy.com
• Phone: +1 (475) 209-1037
• Website: learn.onecampusacademy.com