Welcome to Unit 2: Python and TensorFlow 2 in Generative AI
Welcome to Unit 2 of Generative Artificial Intelligence, where we explore the powerful combination of Python, TensorFlow 2, and modern Generative AI technologies.
This unit provides students with a practical foundation for understanding how Python and TensorFlow can be used to prepare data, build computational models, work with Generative AI datasets, and implement pre-trained AI models. The concepts are explained in a simple, structured, and beginner-friendly manner, making them suitable for students who are beginning their journey in Generative AI.
You will begin by exploring the fundamentals of Python and TensorFlow 2 and understand their role in developing and implementing AI applications. The unit then focuses on data preprocessing and cleaning, an essential step for preparing high-quality datasets for Generative AI systems.
Students will also learn how to visualize data distributions and patterns to better understand datasets before applying AI models. This helps in identifying trends, inconsistencies, and important characteristics of the data.
A major focus of this unit is TensorFlow 2, including its computation graph and eager execution concepts. Students will understand how TensorFlow performs numerical computations and supports the development and training of modern machine learning and deep learning models.
The unit further introduces the Hugging Face Transformers Library, providing practical exposure to pre-trained Transformer-based models. Students will also learn the fundamentals of fine-tuning pre-trained models for domain-specific text and image generation applications.
📚 In this Unit, You will Learn:
🔹 Introduction to Python and TensorFlow 2
🔹 Overview of Python for Generative AI
🔹 Overview and Features of TensorFlow 2
🔹 Role of Python and TensorFlow in Generative AI
🔹 Data Preprocessing and Cleaning
🔹 Preparing Data for Generative AI Applications
🔹 Handling and Preparing Generative AI Datasets
🔹 Data Visualization
🔹 Visualizing Data Distributions
🔹 Identifying Patterns in Generative AI Datasets
🔹 Introduction to TensorFlow Computation Graph
🔹 Understanding Eager Execution
🔹 TensorFlow Operations and Model Development
🔹 Practical Implementation using Hugging Face Transformers
🔹 Introduction to Pre-Trained Transformer Models
🔹 Using Pre-Trained Models for Generative AI
🔹 Fine-Tuning Pre-Trained Models
🔹 Domain-Specific Text Generation
🔹 Domain-Specific Image Generation
Throughout this unit, students will learn how Python and TensorFlow 2 support the development of Generative AI applications, while practical exposure to Hugging Face Transformers will introduce them to the modern ecosystem of pre-trained AI models.
By the end of this unit, students will be able to prepare and visualize Generative AI datasets, understand TensorFlow's computation graph and eager execution, work with pre-trained Transformer models, and understand the fundamentals of fine-tuning models for domain-specific text and image generation.
This unit provides an important foundation for advanced topics such as Deep Learning, Transformer Models, Natural Language Processing, Computer Vision, Prompt Engineering, Large Language Models, and Generative AI Applications.
🎓 This Study Material is Useful for:
🎓 BCA Students
🎓 MCA Students
🎓 B.Sc. (IT) Students
🎓 Computer Science & IT Beginners
🎓 Python Beginners
🎓 Artificial Intelligence & Generative AI Beginners
🎓 Machine Learning & Deep Learning Students
🎓 Hugging Face & Transformer Beginners
🎓 Competitive Exam Preparation
⭐ About Ruparel Education
Ruparel Education Pvt. Ltd., Junagadh has been providing quality education and professional computer training for more than 27 years. Our mission is to help students build strong technical skills through practical learning, industry-oriented courses, and expert guidance.
Prepared & Compiled By
Mr. Uday Shah
Head of Information Technology (HOD–IT)
Ruparel Education Pvt. Ltd., Junagadh
Assistant Professor
Faculty of Computer Applications
Noble University
🌐 Blog: https://uday-shah.blogspot.com
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