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What you'll learn?
- Understand Generative AI Fundamentals: Students will gain a solid understanding of the principles and techniques behind generative AI, including various models
- Implement Generative AI Models: Students will learn how to implement and customize generative AI models using popular frameworks such as TensorFlow and PyTorch
- Evaluate and Optimize AI Models: Students will develop skills in evaluating the performance of generative AI models through metrics such as fidelity, diversity
- Deploy Generative AI Solutions: Students will acquire practical knowledge on how to deploy generative AI models into production environments
Course Overview
Unlock the power of Generative AI and elevate your development skills with this comprehensive course designed for developers and data scientists. Generative AI for Developers: A Practical Implementation provides hands-on training to help you master the latest techniques in machine learning, deep learning, and artificial intelligence.
In this course, you will:
Explore Generative AI models such as GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
Learn how to implement AI solutions that can generate realistic images, text, and other data formats.
Gain practical experience with popular libraries like Transformers.
Understand the ethical considerations and best practices for using Generative AI in your projects.
Work on real-world case studies and projects to build your portfolio, demonstrating your ability to deploy AI models in a professional environment.
Whether you're looking to enhance your resume, stay ahead in the rapidly evolving field of technology, or simply explore the fascinating world of Generative AI, this course has everything you need. By the end of this course, you will be equipped with the knowledge and skills to create innovative AI applications, implement cutting-edge machine learning techniques, and contribute to the next generation of intelligent systems.
Enroll now and start your journey towards becoming a proficient Generative AI developer!
Pre-requisites
- Strong Python Programming Skills: Proficiency in Python programming, including data structures, control flow, and object-oriented programming.
- Machine Learning Fundamentals: A solid understanding of core machine learning concepts, such as supervised and unsupervised learning, model evaluation, and hyperparameter tuning.
Target Audience
- Primarily consists of individuals with a strong foundation in machine learning and a keen interest in generative AI.
- Data Scientists and Machine Learning Engineers: Professionals looking to expand their skillset and delve deeper into generative techniques.
- Research Scientists: Researchers in academia or industry who aim to leverage generative AI for groundbreaking research.
- Software Engineers and Developers: Developers seeking to build innovative applications powered by generative AI.
- Students and Enthusiasts: Individuals with a passion for AI and a desire to learn about cutting-edge techniques.
Curriculum 20 Lectures
Section 1 : Introduction
- Lecture 2 :
- Introduction to Generative AI
Section 2 : Understanding Generative Models: Concepts and Techniques
- Lecture 1 :
- Understanding Models: An Overview
- Lecture 2 :
- What are Generative Models?
- Lecture 3 :
- Exploring the Diversity of Generative Models
Section 3 : Generative Adversarial Networks (GANs)
- Lecture 1 :
- Introduction to GANs (Generative Adversarial Networks)
- Lecture 2 :
- GAN Components and Variants
- Lecture 3 :
- Generator and Discriminator
- Lecture 4 :
- Challenges, Ethical Considerations, and Future Trends
Section 4 : Variational Autoencoders (VAEs)
- Lecture 1 :
- Introduction to VAE Variants (CVAE, VQ-VAE)
- Lecture 2 :
- Demo - Variational Autoencoder (VAE) for MNIST Digit Reconstruction
Section 5 : Autoencoders
- Lecture 1 :
- Introducing Autoencoders
- Lecture 2 :
- Autoencoder-based Image Compression and Denoising Demo
Section 6 : Large Language Models in Generative AI
- Lecture 1 :
- Introducing Large Language Models (LLMs)
- Lecture 2 :
- Decoding the Architecture of LLMs
Section 7 : Transformer-Based Generative Models
- Lecture 1 :
- Transformer-Based Generative Models
- Lecture 2 :
- Demo - Transformer-Based Translator
- Lecture 3 :
- Demo-Transformer-Based Sentiment Analysis
- Lecture 4 :
- Demo - Creative Content Generation with Generative AI
- Lecture 5 :
- Demo - Transformer-Based Text Generation
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Yes. All of the courses comes with online video based lectures created by certified instructors. Instructors have crafted these courses with a blend of high quality interactive videos, lectures, quizzes & real world projects to give you an indepth knowledge about the topic.Can i play & pause the course as per my convenience?
Yes absolutely & thats one of the advantage of self-paced courses. You can anytime pause or resume the course & come back & forth from one lecture to another lecture, play the videos mulitple times & so on.How do i contact the instructor for any doubts or questions?
Most of these courses have general questions & answers already covered within the course lectures. However, if you need any further help from the instructor, you can use the inbuilt Chat with Instructor option to send a message to an instructor & they will reply you within 24 hours. You can ask as many questions as you want.Do i need a pc to access the course or can i do it on mobile & tablet as well?
Brilliant question? Isn't it? You can access the courses on any device like PC, Mobile, Tablet & even on a smart tv. For mobile & a tablet you can download the Learnfly android or an iOS app. If mobile app is not available in your country, you can access the course directly by visting our website, its fully mobile friendly.Do i get any certificate for the courses?
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