Generative AI

Course Description

Welcome to the comprehensive course on "Generative AI!" This Generative AI course is designed to provide practical examples and comprehensive explanations that simplify complex concepts of generative artificial intelligence, aiding students in understanding and retaining knowledge for professional application. Through knowledge tests and mock exams, students build confidence and proficiency in developing AI models that can generate text, images, and more. An experienced AI developer is available to clarify doubts and assist with interview preparation.

Course Overview:

Embark on a comprehensive journey from beginner to expert in generative AI with this detailed course. Led by expert instructor Venky Karukuri, this course is meticulously crafted to equip you with the essential skills and knowledge required to excel in creating and deploying generative AI models.

Duration:

90 Days

Course Objectives:

Master the fundamentals of generative AI technologies, including neural networks and deep learning.
Develop skills to create AI models that can generate novel content, such as text, images, and music.
Understand the ethical considerations and implications of using generative AI.
Gain a strong foundation in the tools and platforms used for training and deploying generative AI models.
Prepare effectively for interviews and career opportunities in the emerging field of generative AI.

Curriculum Highlights:

Hands-on Learning: Engage in practical projects that involve using generative AI models for creative and innovative applications.
Comprehensive Coverage: From basic concepts of AI to advanced techniques in generative model training, every aspect is thoroughly explored.
Expert Guidance: Learn from an experienced AI developer with a passion for generative technologies and a wealth of industry knowledge.
Interview Preparation: Acquire strategies to ace technical interviews and secure rewarding job opportunities in the field of generative AI.

Who Should Attend?

Aspiring AI Developers: Individuals with a basic understanding of programming and machine learning who wish to advance their knowledge in generative AI.
Career Switchers: Professionals aiming to transition into roles focused on innovative AI technologies and applications.
Students: Those studying computer science, artificial intelligence, or related fields who wish to deepen their understanding of generative models.
Anyone Interested in Generative AI: Whether you’re curious about the capabilities of AI to create new content or eager to pursue a career in this cutting-edge area, this course is designed for you.

Enroll Today!

By the end of this course, you'll not only understand the fundamentals of Generative AI but also gain practical experience in creating AI models that generate creative outputs, from text to images.

Whether you're an AI enthusiast or a professional exploring the potential of generative models, this course is your gateway to success. Join us and dive into the world of Generative AI!

Pricing:

For pricing and more information, please contact us at contact@venkys.io .

Course Curriculum

Generative AI - Comprehensive Course

Course Structure:

Month 1: Foundations of AI and Machine Learning

Week 1: Introduction to AI and Machine Learning

Basic concepts: Understanding artificial intelligence and machine learning fundamentals.
Generative vs. discriminative models: Key differences and applications.
Setting up the environment: Installing Python, TensorFlow, and PyTorch.

Week 2: Probability and Statistics

Probability theory: Essential concepts for generative models.
Statistical methods: Understanding distributions and their applications.
Bayesian thinking: Incorporating probabilities in generative model development.

Week 3: Introduction to Neural Networks

Neural network fundamentals: Layers, activations, and backpropagation.
Training neural networks: Techniques and tools.
Hands-on practice: Building simple neural networks using TensorFlow or PyTorch.

Week 4: Deep Learning Architectures

CNNs: Using convolutional networks for image-related tasks.
RNNs: Applying recurrent networks to sequence data.
Autoencoders: Basics of unsupervised learning and dimensionality reduction.

Month 2: Introduction to Generative Models

Week 5: Autoencoders and Variational Autoencoders (VAEs)

Understanding autoencoders: How they reconstruct input data.
VAEs: Building models that can generate new samples.
Applications: Using VAEs for image and text generation.

Week 6: Generative Adversarial Networks (GANs)

GAN fundamentals: Architecture of generator and discriminator models.
Building GANs: Step-by-step process for generating images.
Overcoming challenges: Techniques to improve GAN training stability.

Week 7: Advanced GANs and Applications

GAN variants: DCGAN, CGAN, and StyleGAN architectures.
Real-world applications: Implementing GANs in various industries.
Creative projects: Generating art, music, and realistic images.

Week 8: Other Generative Models

Normalizing flows: Understanding flow-based generative models.
Energy-based models: Exploring alternative generative approaches.
Comparative analysis: Evaluating different generative models.

Month 3: Advanced Topics and Project Work

Week 9: Integrating and Tuning Generative Models

Fine-tuning models: Techniques to enhance performance.
Evaluating outputs: Metrics for assessing generated samples.
Customization: Adapting models for specific use cases.

Week 10: Creative Applications of Generative AI

Art and design: Using AI to create visual and musical compositions.
Text generation: Working with models like GPT-2 and GPT-3.
Interactive projects: Building chatbots and AI-driven creative tools.

Week 11: Ethical Considerations and Future of Generative AI

Bias in AI: Understanding and mitigating ethical issues.
Societal impacts: Considering the implications of generative technologies.
Future trends: Exploring upcoming advancements and research.

Week 12: Capstone Project

Project selection: Identifying a real-world problem to solve using generative AI.
Implementation: Building and refining the generative model.
Presentation: Showcasing results and reflecting on learning outcomes.