Generative AI for Professionals
Unleash the Power of LLMs
Gen AI in Action
Corporate Training
Real world skills
Master Generative AI
Foundations of Generative AI and Mastering LLM powered Apps are the Course offers training on LLMs, GANs, and VAEs, featuring hands-on projects in text generation, image creation, and chatbot development. Participants will gain the skills to enhance productivity and drive innovation using cutting-edge generative AI techniques.
What to expect from this course
Hands-On Experience
- Work on real-world projects that involve generating text, images, and interactive chatbots.
- Collaborate on team-based assignments to simulate professional environments.
- Use industry-standard tools and libraries like Hugging Face, TensorFlow, and PyTorch.
In-Depth Knowledge
- Understand the architecture and functioning of LLMs, GANs, and VAEs.
- Explore advanced concepts like prompt engineering and fine-tuning techniques for model optimization.
- Apply various evaluation metrics to assess the performance of generative models.
Productivity Enhancement
- Learn strategies to automate repetitive tasks using generative AI tools.
- Apply generative techniques to create content and streamline workflows.
- Develop solutions that leverage AI to solve business challenges and enhance operational efficiency.
Foundations of Generative AI
What you’ll learn on this course
Duration
40 hours
Level
Intermediate
Course Format
Instructor led
Study Method
Online | offline | bootcamp
01
Introduction to Generative AI & Large language models
6 hours | 1 Case Study | 1 Assignment
Overview of Generative AI, Historical context and evolution, Applications of Generative AI, Ethical considerations, Understanding LLMs, Key architectures: Transformers, RNNs, etc. Popular LLMs (GPT, BERT, etc.)
02
Text Generation Techniques
6 hours | 1 Case Study | 1 Assignment
Markov Chains, RNNs, LSTMs, and Transformers, emphasizing architectures and implementations. Fine-tuning of pre-trained models, prompt engineering, and evaluate outputs with BLEU and ROUGE metrics while addressing bias and
03
Image Generation Techniques
6 hours | 1 Case Study | 1 Assignment
Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, focusing on their architectures and functionalities. Training GANs and VAEs and evaluate generated images using metrics like Inception Score and Fréchet Inception Distance, addressing challenges like mode collapse and bias.
04
Advanced Prompt Engineering
4 hours| 1 Assignment
Principles of prompt engineering and its critical role in generative AI. Analyze prompt design methodologies, including zero-shot, few-shot, and chain-of-thought prompting, to optimize model outputs. Craft effective prompts for language and image models, and assess performance through quantitative metrics and qualitative evaluations, prompt bias and context sensitivity.
05
Retrieval-Augmented Generation (RAG)
6 hours | 1 Assignment | 1 Case Study
- Retrieval-Augmented Generation (RAG) architecture, Integration of retrieval systems with generative models, Embedding queries and documents, Impact of retrieval strategies on performance
- Hands-on implementation for knowledge retrieval, Evaluation metrics, retrieval noise, data integration
06
Interaction with Data, Model Adaptation and Fine-Tuning
6 hours | 1 Assignment
Techniques for querying and interacting with data, Chatbots and conversational AI, Integrating LLMs with databases, Techniques for adapting pre-trained models, Fine-tuning strategies for specific tasks, Evaluating model performance
Mastering LLM-Powered Apps
What you’ll learn on this course
Duration
40 hours
Level
Advanced
Course Format
Instructor led
Study Method
Online | offline | bootcamp
01
LLMs and the AI Paradigm Shift
5 hours | 1 Case Study | 1 Assignment
Overview of LLMs & Foundation Models, Understanding the Transformer architecture: Attention mechanism, encoder-decoder, Key architectures: GPT, BERT, T5, and their variants, Word embeddings work in transformers, Self-attention, multi-head attention, Encoder, decoder, and stacking layers, Training an LLM, Pre-training vs fine-tuning, Model evaluation, Base models vs. customized models, Code completion and suggestion systems (e.g., GitHub Copilot), Setting up an orchestrator to integrate LLMs with APIs, databases, and external tools, Choosing the right framework for embedding LLMs ex: LangChain, Hugging Face, Haystack, and others
02
Selecting and Customizing LLMs for Your Application
6 hours | 1 Case Study | 1 Assignment
Choosing the Right LLM: GPT-4, Claude, Gemini – Comparisons, Open-source models LLaMA-2: Architecture, Falcon LLM and Mistral, Decision framework for selecting the right LLM, Prompt Engineering: Structuring effective inputs for LLMs, pitfalls in prompt design (e.g., ambiguity, biases), Key principles, writing concise and specific prompts, Improving the model’s transparency, Refining prompts through multiple attempts, Advanced prompt engineering techniques, Few-shot learning, ReAct (Reasoning and Acting), Combining reasoning and task execution in prompts, Model Customization & Fine-tuning a pre-trained model for customer service or technical support applications
03
Building Conversational and Interactive LLM-Powered Applications
8 hours | 1 Case Study | 1 Assignment
Conversational AI: Building a chatbot using the Transformer model: Basic architecture and setup, Adding advanced features to conversational bots, Using APIs for dynamic information retrieval (e.g., weather, stock prices), Using LangChain to connect LLMs with data sources, APIs, and databases, Creating data pipelines, Front-end development with Streamlit, Building a simple question-answering interface, Developing advanced bots that support multi-turn conversations, Integrating tools like Wolfram Alpha, Google Search, or OpenAI’s GPT models, Using text, images, and audio inputs in a conversational AI setup (e.g., DALL-E, Whisper)
04
LLMs in Recommendation Systems and Search Engines
8 hours| 1 Assignment
Collaborative filtering, content-based filtering, matrix factorization, Neural networks for personalized recommendations, Semantic search, LLMs for content generation and analysis, Implementing a content-based recommendation system using LLMs, Text embeddings and similarity search, Building a recommendation engine with user data (e.g., product, movie, or music recommendations), Visualizing recommendations in a user-friendly interface using Streamlit, Building search interfaces for semantic search and LLM-powered recommendations
05
Multimodal AI, Structured Data, and Advanced LLM Applications
8 hours | 1 Assignment | 1 Case Study
Multimodal models: GPT-4 with vision, vision-language transformers, Building multimodal applications, Integrating audio-visual tools: Using Whisper for audio processing and DALL-E for image generation, Understanding relational databases (SQL, NoSQL) and integrating with LLMs, LangChain agents for interacting with databases: SQL agent, SQL databases, and query generation, Building data-driven applications with LLMs, Using LLMs for algorithm development, debugging, and code completion (e.g., CodeLlama, StarCoder), Building a code assistant with LLMs, Setting up and using the code interpreter for dynamic execution of code with LLMs
06
Responsible AI, Ethics, and Emerging Trends in LLMs
5 hours | 1 Assignment
Ethical AI practices, Bias detection in training data and model outputs, Transparency and explainability in AI models, Model-level responsibility, Metaprompt-level responsibility. User interface-level responsibility, Fairness in LLM-generated content (e.g., gender, racial bias), Security and privacy considerations, Data privacy regulations (GDPR, CCPA) for AI applications, secure deployment and user safety, Latest trends in generative AI, The future of small language models
The great thing about AI is that it allows us to focus on the creativity of our ideas
This course is for you
Looking to deepen expertise in generative AI and apply it to real-world projects.
Aiming to build intelligent, AI-driven applications and expand your technical skill set.
Seeking to understand AI capabilities to enhance product innovation and deployment.
Interested in exploring advanced AI concepts and staying current in the rapidly evolving field of generative models.
Batch Opens
Starting
Jan 25, 2025
Early bird discount
10% off - register on before Jan 22, 2025
Mode
Instructor Led Online Training
Duration
02 months
Weekends
Saturday & Sunday
Timing
2:00 PM to 4:00 PM
Step 1
Get More Info
+91 98415 57655
training@vyoam.in
Step 2
Payment
UPI ID: vyoamaisolutions@ybl
- Email: vyoamtech@gmail.com
Step 3
Enrollment form
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Courses
VYOAM’s corporate training in Artificial Intelligence and Data Science is designed to transform your employees' foundational analytics knowledge into advanced, industry-relevant expertise. Through hands-on, real-world applications, this program equips your team with the practical skills essential for success in today’s data-driven environment.
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