Generative AI
The Generative AI Engineering for Innovators course by DataWorks is a specialized program crafted to introduce participants to the revolutionary field of generative artificial intelligence. This course focuses on cutting-edge technologies and methodologies used to create generative AI models that can produce original content, such as text, images, music, and more. Covering the fundamentals of machine learning and deep learning, the curriculum progresses to more advanced topics such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers. Through a blend of theoretical understanding and hands-on projects, participants will learn to develop their own generative AI models, enabling them to lead innovation in creative industries, tech startups, and research.
5.0 (110 Ratings)
Course Info
What I will learn?
What I will learn?
Introduction to generative AI and its applications across various domains.
Fundamental concepts of machine learning and deep learning essential for generative AI.
Detailed exploration of Generative Adversarial Networks (GANs) and their applications.
Working with Variational Autoencoders (VAEs) for content generation.
Leveraging transformers for generating text and understanding natural language processing (NLP) techniques.
Hands-on experience with tools and frameworks such as TensorFlow and PyTorch.
Best practices for training, fine-tuning, and deploying generative AI models.
Ethical considerations and societal impacts of generative AI technologies.
Course Content
Latest Trends and Advancements in Generative AI: Exploring current developments and future directions in generative AI.
Theoretical Foundations of Generative AI: Understanding the underlying principles and theories.
Deep Neural Networks for Generative AI: Exploring deep learning architectures suitable for generative tasks.
Optimization Techniques: Techniques for optimizing the performance of generative models.
Model Architectures: Studying various model architectures used in generative AI.
Generative Adversarial Networks (GANs): In-depth study of GANs and their applications.
Variational Autoencoders (VAEs): Exploring VAEs and their uses in generative tasks.
Other Generative Models: Overview of other types of generative models.
Advanced NLP Techniques: Exploring sophisticated techniques in NLP using generative models.
Text Generation: Techniques for generating text using generative AI.
Language Models: Deep dive into language models like GPT-3.
Generative AI in Art and Design: Utilizing generative AI for creative purposes in visual arts and design.
Music Generation: Techniques for generating music using AI.
Other Creative Domains: Exploring the use of generative AI in various creative fields.
Ethics in AI: Exploring the ethical considerations in the use of generative AI.
Biases in Generative Models: Identifying and addressing biases in generative AI.
Responsible AI Practices: Best practices for ethical use of generative AI.
Scaling Generative AI Models: Techniques for scaling generative models to handle larger datasets or more complex tasks.
Cloud Computing for AI: Utilizing cloud platforms to scale generative AI applications.
Performance Optimization: Strategies for optimizing the performance of generative AI systems.
Comprehensive Generative AI Solution: Integrating knowledge and skills acquired to develop an innovative generative AI solution.
Application of Generative AI: Addressing a real-world problem or creating a novel application using generative AI.
Upskill for your Dream Job
Hiring Partners
Course Requirements
Course Requirements
A solid understanding of Python programming.
Basic knowledge of machine learning and neural networks.
Access to a computer capable of running AI development tools and frameworks.
FAQ's
This course is designed for individuals with a foundational knowledge of machine learning, aiming to specialize in the generative AI space. It covers both fundamental concepts and advanced techniques.
Participants will work on several projects, including creating AI-generated art, developing text-based AI models for content creation, and experimenting with music generation.
While having a powerful computer is beneficial for model training, the course will also provide guidance on using cloud-based platforms for computationally intensive tasks.
Completing this course opens up opportunities in AI-driven industries, creative fields utilizing AI, tech startups innovating with generative models, and research roles focused on AI development.
Yes, DataWorks awards a certificate upon completion of the Generative AI Engineering for Innovators course, recognizing your advanced skills in this cutting-edge technology area.
Earning Potential
9 LPA
min
15 LPA
avg
25 LPA
max
Generative AI Tools Covered
OpenAI
GPT-3/4
PyTorch
spaCy
TensorFlow
Google AI
Platform
Seaborn
Let’s explore further the implications of transitioning to online training
Course Certificate
The Cyber Security Practitioner Programming Course Certificate focuses on enhancing coding skills for securing applications and systems. The curriculum covers topics like secure coding practices, ethical hacking, and defensive programming. It’s ideal for developers and security professionals aiming to bolster their cybersecurity expertise.
Course Reviews
The trainers at DataWorks.pro are experts in their fields
David.S
I gained valuable insights and skills at DataWorks.pro
Ritesh.A
I gained practical skills and knowledge at DataWorks.pro
Tapan.N
I achieved my career objectives with DataWorks.pro's help
