Generative AI For Developers: A Practical Implementation

From Theory to Practice: Developing with Generative AI

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

Requirements
No prior experience with generative AI is required. The course is designed to guide students through the learning process, providing all the necessary information and resources to succeed, from foundational concepts to advanced implementation techniques.

Description
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!

Overview
Section 1: Introduction

Lecture 1 Welcome & Course Overview

Lecture 2 Introduction to Generative AI

Section 2: Understanding Generative Models: Concepts and Techniques

Lecture 3 Understanding Models: An Overview

Lecture 4 What are Generative Models?

Lecture 5 Exploring the Diversity of Generative Models

Section 3: Generative Adversarial Networks (GANs)

Lecture 6 Introduction to GANs (Generative Adversarial Networks)

Lecture 7 GAN Components and Variants

Lecture 8 Demo-Generator and discriminator

Lecture 9 Challenges, Ethical Considerations, and Future Trends

Section 4: Variational Autoencoders (VAEs)

Lecture 10 Introduction to VAE Variants (CVAE, VQ-VAE)

Lecture 11 Demo:Variational Autoencoder (VAE) for MNIST Digit Reconstruction

Section 5: Autoencoders

Lecture 12 Introducing Autoencoders

Lecture 13 Autoencoder-based Image Compression and Denoising Demo

Section 6: Large Language Models in Generative AI

Lecture 14 Introducing Large Language Models (LLMs)

Lecture 15 Decoding the Architecture of LLMs

Section 7: Transformer-Based Generative Models

Lecture 16 Transformer-based generative models

Lecture 17 Demo-Transformer Based Translator

Lecture 18 Demo-Transformer Based Sentiment Analysis

Lecture 19 Demo Creative Content Generation with Generative AI

Lecture 20 Demo-Transformer Based Text Generation

This course is tailored for those who want to gain practical, actionable knowledge in generative AI, regardless of their previous experience level in the field.

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