• Overview
  • Schedule Classes
  • What you'll learn
  • Curriculum
  • Feature
  • FAQs
Original price was: $1,599.00.Current price is: $999.00.
Request Class
overviewbg

Overview

Working with Large Language Models offers a comprehensive exploration of the cutting-edge technologies that are revolutionizing natural language processing and artificial intelligence. This immersive training program provides participants with in-depth knowledge of language models ranging from statistical approaches to state-of-the-art transformer architectures and multimodal systems. Participants will gain hands-on expertise in implementing, fine-tuning, and deploying various language models to solve real-world challenges across multiple domains.

The course offers a practical journey through the evolution of language models, from foundational statistical approaches to advanced neural architectures like PaLM 2, Gemini, Llama, and Mistral. By combining theoretical foundations with extensive hands-on implementation, participants will learn to leverage powerful open-source frameworks, create custom applications, and integrate language models with various modalities, including images and voice. The curriculum bridges academic concepts with practical deployment scenarios, empowering developers and data scientists to harness these transformative technologies effectively.

Cognixia’s Working with Large Language Models program stands at the intersection of practical application and technological innovation. Participants will not only master the technical aspects of implementing various language models but will also develop a nuanced understanding of model architecture, scaling strategies, and multimodal capabilities. The course goes beyond traditional LLM training by introducing cutting-edge concepts in text-to-image generation, voice synthesis, and enterprise deployment of advanced AI models, preparing professionals to lead innovation in this rapidly evolving field.

Schedule Classes


Looking for more sessions of this class?

Talk to us

What you'll learn

  • Master the fundamentals of language models
  • Implement and fine-tune various language models
  • Build practical applications, including chatbots and conversational agents
  • Leverage enterprise-grade models
  • Explore multimodal AI capabilities
  • Develop practical skills with industry tools

Prerequisites

  • Foundational understanding of machine learning concepts
  • Adept with programming languages, particularly Python

Curriculum

  • Overview of Txt2Txt GenAI
  • Introduction to Unimodal Mappings
  • Understanding the Significance of Txt2Txt GenAI in AI
  • Hands-on session: Exploring the basics of Txt2Txt GenAI
  • Interactive exercises: Working with unimodal mappings
  • Introduction to Statistical Language Models
  • Exploring the Applications of Statistical Language Models
  • Hands-on Experience with Statistical Language Models
  • Hands-on workshop: Working with Statistical Language Models
  • Interactive exercises: Experimenting with various Statistical Language Models
  • Overview of Neural Language Models
  • Deep Dive into the Architecture of Neural Language Models
  • Exploring the Applications of Neural Language Models
  • Hands-on session: Working with Neural Language Models
  • Interactive exercises: Understanding the architecture of Neural Language Models
  • Introduction to SLM and PLM in Python and Keras
  • Exploring the Implementation of SLM and PLM
  • Hands-on Experience with SLM and PLM in Python and Keras
  • Hands-on workshop: Implementing SLM and PLM using Python and Keras
  • Interactive exercises: Working with SLM and PLM in practical scenarios
  • Comprehensive Overview of Seq2seq Models
  • Exploring the Architecture and Functionality of Seq2seq Models
  • Real-World Applications of Seq2seq Models
  • Hands-on session: Working with Seq2seq Models
  • Interactive exercises: Exploring the applications of Seq2seq Models
  • Introduction to Hugging Face Transformer Pipelines
  • Exploring the Functionality and Implementation of Transformer Pipelines
  • Hands-on Experience with Hugging Face Transformer Pipelines
  • Hands-on workshop: Implementing Hugging Face Transformer Pipelines
  • Interactive exercises: Working with Transformer Pipelines in AI tasks
  • Introduction to Transfer Learning in NLP
  • Exploring the Applications of Transfer Learning in NLP
  • Hands-on Experience with Transfer Learning in NLP
  • Hands-on workshop: Implementing Transfer Learning in NLP
  • Interactive exercises: Working with Transfer Learning in practical scenarios
  • Comprehensive overview of PaLM fundamentals
  • Exploring the Differences between PaLM and Google Gemini
  • Understanding the Advancements in Google Gemini
  • Using Gemma, Llama models with Vertex AI
  • Hands-on session: Working with Gemini and PaLM
  • Interactive exercises: Exploring the advancements in Gemini
  • Introduction to Chat Apps using LLMs, Claude and and Local AI
  • Exploring the Functionality and Implementation of Llama 2 models and Local AI
  • Hands-on Experience with Chat Apps and Local AI API
  • Hands-on workshop: Implementing ChatGPT and OpenAI API
  • Interactive exercises: Working with ChatGPT and OpenAI API in AI tasks
  • Introduction to ChatGPT Clone in Google Colab and Streamlit
  • Exploring the Implementation of a ChatGPT Clone
  • Hands-on Experience with ChatGPT Clone in Google Colab and Streamlit
  • Hands-on workshop: Implementing ChatGPT Clone using Google Colab and Streamlit
  • Interactive exercises: Working with ChatGPT Clone in practical scenarios
  • Overview of Img2Img GenAI
  • Introduction to Auto-Encoder Visualization
  • Understanding the Significance of Img2Img GenAI in AI
  • Hands-on session: Exploring the basics of Img2Img GenAI
  • Interactive exercises: Working with Auto-Encoder visualization
  • Introduction to Variational Auto-Encoder
  • Exploring the Applications of Variational Auto-Encoder
  • Hands-on Experience with Variational Auto-Encoder
  • Hands-on workshop: Working with Variational Auto-Encoder
  • Interactive exercises: Experimenting with various Variational Auto-Encoder
  • Introduction to Coding AE in Keras
  • Exploring the Implementation of AE in Keras
  • Hands-on Experience with Coding AE in Keras
  • Hands-on workshop: Implementing AE using Keras
  • Interactive exercises: Working with AE in practical scenarios
  • Comprehensive Overview of Training GANs
  • Exploring the Architecture and Functionality of GANs
  • Real-world applications of Training GANs
  • Hands-on session: Working with Training GANs
  • Interactive exercises: Exploring the applications of Training GANs
  • Introduction to Multimodal GenAI
  • Exploring Multimodal Txt2Img Generation
  • Understanding Latent Diffusion Models
  • Hands-on workshop: Implementing Multimodal GenAI
  • Interactive exercises: Working with Multi-modal Txt2Img Generation and Latent Diffusion Models
  • Introduction to CLIP drop and Stable Diffusion
  • Exploring the Applications of CLIP drop and Stable Diffusion
  • Hands-on Experience with CLIP drop and Stable Diffusion
  • Hands-on workshop: Implementing CLIP drop and Stable Diffusion
  • Interactive exercises: Working with CLIP drop and Stable Diffusion in practical scenarios
  • Comprehensive Overview of LeonardoAI, Midjourney, and OpenAPI - Dall-E3
  • Exploring the Architecture and Functionality of LeonardoAI, Midjourney, and OpenAPI - Dall-E3
  • Real-World Applications of LeonardoAI, Midjourney, and OpenAPI - Dall-E3
  • Hands-on session: Working with LeonardoAI, Midjourney, and OpenAPI - Dall-E3
  • Interactive exercises: Exploring the applications of LeonardoAI, Midjourney, and OpenAPI - Dall-E3
  • Introduction to Txt2Voice Generation - Evenlabs
  • Exploring the Functionality and Implementation of Txt2Voice Generation - Evenlabs
  • Hands-on Experience with Txt2Voice Generation - Evenlabs
  • Hands-on workshop: Implementing Txt2Voice Generation - Evenlabs
  • Interactive exercises: Working with Txt2Voice Generation - Evenlabs in AI tasks
  • Overview of PaLM 2
  • Introduction to Pathway Language Model Journey
  • Understanding the Significance of PaLM 2 in AI
  • Hands-on session: Exploring the basics of PaLM 2
  • Interactive exercises: Working with Pathway Language Model Journey
  • Introduction to Compute Optimal Scaling and Model Architecture
  • Exploring the Applications of Compute Optimal Scaling and Model Architecture
  • Hands-on Experience with Compute Optimal Scaling and Model Architecture
  • Hands-on workshop: Working with Compute Optimal Scaling and Model Architecture
  • Interactive exercises: Experimenting with various Compute Optimal Scaling and Model Architecture
  • Introduction to Gemini and PaLM API
  • Exploring the Applications of Bard and PaLM API
  • Hands-on Experience with Bard and PaLM API
  • Hands-on workshop: Implementing Gemini and PaLM API
  • Interactive exercises: Working with Gemini and PaLM API in practical scenarios
  • Introduction to PaLM API in Vertex AI
  • Exploring the Applications of PaLM API in Vertex AI
  • Hands-on Experience with PaLM API in Vertex AI
  • Hands-on workshop: Implementing PaLM API in Vertex AI
  • Interactive exercises: Working with PaLM API in Vertex AI in practical scenarios
  • Overview of MakerSuite
  • Introduction to the functionalities of MakerSuite
  • Understanding the Significance of MakerSuite in AI
  • Hands-on session: Exploring the basics of MakerSuite
  • Interactive exercises: Work with various functionalities of MakerSuite
  • Introduction to Advanced Features of MakerSuite
  • Exploring the Applications of Advanced Features in MakerSuite
  • Hands-on Experience with Advanced Features of MakerSuite
  • Hands-on workshop: Working with Advanced Features in MakerSuite
  • Interactive exercises: Experimenting with various Advanced Features in MakerSuite

Interested in this course?

Reach out to us for more information

Course Feature

Course Duration
Learning Support
Tailor-made Training Plan
Customized Quotes

FAQs

Large Language Models (LLMs) are advanced AI systems trained on vast amounts of text data to understand and generate human-like language. These models use complex neural network architectures—particularly transformer-based approaches—to process, interpret, and generate text across various tasks, including content creation, summarization, translation, coding, and conversational AI applications.
The production deployment of LLMs requires addressing numerous challenges, including latency optimization, cost management, and resource allocation. The course provides practical guidance on quantization techniques, model distillation, efficient inference strategies, and API integration. You will learn to implement caching mechanisms, batch processing, and other optimization approaches that make LLM deployment viable in real-world business environments.
LLM hallucinations present significant challenges in production environments. The course covers comprehensive strategies for improving factual reliability, including retrieval-augmented generation, grounding techniques, fact-checking mechanisms, and output verification. You will learn to implement knowledge retrieval systems, design effective prompting strategies, and create validation workflows that substantially improve output quality for enterprise applications.
Participants embarking on this GenAI course should possess a foundational understanding of machine learning concepts and be adept with programming languages, particularly Python.