Generative Ai Mastery: Fundamentals To Real-World Impact

[mitsumi]

Generative Ai Mastery: Fundamentals To Real-World Impact

Published 9/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.78 GB | Duration: 3h 36m

Master the Basics of Generative AI and Explore Real-World Applications

What you'll learn
Understand the fundamentals of Generative AI and its real-world applications
Differentiate between various types of Generative AI models, including GANs and other architectures
Set up an environment to experiment with and implement Generative AI models
Train, evaluate, and fine-tune Generative AI models for tasks like image generation and text creation
Requirements
No prior experience in AI or machine learning is required. This course is beginner-friendly
Basic understanding of Python programming is recommended, but not mandatory.
Description
Unlock the potential of Generative AI with this comprehensive course designed to take you from foundational concepts to advanced, real-world applications. In this course, you'll dive deep into the cutting-edge field of Generative AI, exploring everything from basic AI models to sophisticated frameworks like GANs, VAEs, and RNNs, while gaining hands-on experience building your own generative models.Throughout the course, you'll learn how to implement popular models, train them for various tasks, and fine-tune pre-trained models using advanced techniques like transfer learning and attention mechanisms. Whether you're interested in generating realistic images, text, or even pioneering research, this course will equip you with the skills and knowledge to excel.Key topics include:Fundamentals of Generative AI: What it is, types of models, and real-world applications.Generative Adversarial Networks (GANs): Architecture, training, and image generation applications.Variational Autoencoders (VAEs): Building models for data generation and real-world use cases.Recurrent Neural Networks (RNNs): Sequence generation using RNNs and LSTMs, with hands-on text generation.Transfer Learning in Generative AI: Fine-tuning pre-trained models for generative tasks in image and text.Attention Mechanisms: Implementing self-attention in generative models and understanding Transformer models.Ethical Considerations: Exploring fairness, bias, privacy, and responsible use of Generative AI.This course is packed with hands-on projects and exercises, allowing you to apply the knowledge in real-world contexts. By the end of the course, you'll have a robust portfolio of AI projects and the ability to develop and implement your own Generative AI solutions, setting you up for success in fields like AI research, machine learning engineering, and data science.Who is this course for:AI enthusiasts looking to understand Generative AI from the ground up.Developers, data scientists, and machine learning engineers seeking to enhance their skills.Professionals interested in the practical applications of AI in industries like healthcare, finance, and media.Join now and become a master in Generative AI, ready to solve real-world challenges and contribute to the future of AI innovation!
Overview
Section 1: Introduction to Generative AI
Lecture 1 What is Generative AI?
Lecture 2 Applications of Generative AI
Lecture 3 Types of Generative AI Models
Lecture 4 Challenges in Generative AI
Lecture 5 Hands-on: Setting Up Environment
Lecture 6 Hands-on: Setting up environment - Demo
Section 2: Generative Adversarial Networks (GANs)
Lecture 7 Overview of GANs
Lecture 8 Architecture of GANs
Lecture 9 Training GANs
Lecture 10 GANs in Image Generation
Lecture 11 Hands-on: Implementing GANs
Lecture 12 Hands-on: Implementing GANs - Demo
Section 3: Variational Autoencoders (VAEs)
Lecture 13 Introduction to VAEs
Lecture 14 Encoder and Decoder in VAEs
Lecture 15 Objective Function of VAEs
Lecture 16 Applications of VAEs
Lecture 17 Hands-on: Building VAE Models
Lecture 18 Hands-on: Building VAE models - Demo
Section 4: Sequence Generation with Recurrent Neural Networks (RNNs)
Lecture 19 Basics of RNNs
Lecture 20 Sequence Generation Using RNNs
Lecture 21 Long Short-Term Memory (LSTM) Networks
Lecture 22 Training RNNs for Sequence Generation
Lecture 23 Hands-on: Text generation with RNNs - Demo
Section 5: Transfer Learning in Generative AI
Lecture 24 Transfer Learning Basics
Lecture 25 Fine-Tuning Pre-Trained Models for Generative Tasks
Lecture 26 Transfer Learning in Image Generation
Lecture 27 Transfer Learning in Text Generation
Lecture 28 Hands-on: Transfer Learning for Generative AI
Lecture 29 Hands-on: Transfer learning for Generative AI - Demo
Section 6: Attention Mechanism in Generative AI
Lecture 30 Introduction to Attention Mechanism
Lecture 31 Implementing Attention Mechanism in Generative Models
Lecture 32 Applications of Attention Mechanism in Generative AI
Lecture 33 Hands-on: Perform style transfer on images
Section 7: Evaluation and Ethical Considerations in Generative AI
Lecture 34 Evaluation Metrics for Generative Models
Lecture 35 Fairness and Bias in Generative AI
Lecture 36 Privacy Concerns in Generative Models
Lecture 37 Responsible Use of Generative AI
Lecture 38 Hands-on: Ethics in Generative AI
Section 8: Future Trends in Generative AI
Lecture 39 Current Research Trends in Generative AI
Lecture 40 Generative AI in Healthcare and Other Domains
Lecture 41 Advancements in Generative Models
Lecture 42 Potential Impact of Generative AI on Society
Lecture 43 Hands-on: Exploring Cutting-Edge Applications
Beginners interested in Artificial Intelligence and curious about how AI models generate content like images, text, and music.,Developers and programmers looking to expand their skills by working with Generative AI models and tools.,Tech professionals and students eager to understand the fundamentals of Generative AI and its applications in various industries.

Screenshots

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