Working with Large Language Models Welcome to the Comprehensive Course on Working with Large Language Models. This course is crafted to offer an in-depth understanding and extensive hands-on experience working with a spectrum of generative AI models, including Txt2Txt GenAI, Img2Img GenAI, Multimodal GenAI, and an introduction to PaLM 2. 

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Overview

Large language models are becoming increasingly powerful and more commonly deployed to power Generative AI tools. They can recognize, summarize, translate, predict, and generate any content as they are trained on very large extensive data sets. Cognixia’s Large Language Models course delves into the intricate world of LLMs, discussing a broad array of topics from unimodal mapping to the advanced features of MakerSuite. This large language model online course is designed to offer learners an in-depth understanding & experience of working with a spectrum of Generative AI models including Txt2Txt, Img2Img, multimodal, and PaLM 2.

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What you'll learn

  • Deep understanding of diverse large language models.
  • Mastery in working with Txt2Txt GenAI, including topics like Seq2seq Models and GPT Fundamentals.
  • Hands-on experience with Img2Img GenAI, including training GANs and working with Auto-Encoders in Keras.
  • Practical insights into Multimodal GenAI, including exploring CLIP drop and Stable Diffusion.
  • In-depth knowledge of PaLM 2, including understanding the Pathway Language Model journey and working with PaLM API in Vertex AI.
Modules The course is segmented into 24 exhaustive modules, each delving into specific topics ranging from an introduction to synthetic data to exploring the advanced features of MakerSuite. Lessons Each module is equipped with detailed lessons, offering insights into concepts, theories, and implementations, ensuring a thorough understanding of the topics covered. Labs Hands-on labs accompany each module, allowing participants to practically implement and experiment with the learned concepts, enhancing their practical understanding and skills. After Completing Each Module Upon the completion of each module, participants will emerge with a deeper, well-rounded understanding of the covered topics, fortified with practical experience and the capability to implement the learned concepts in real-world scenarios. Students Will Be Able To
  • Understand and proficiently work with large language models.
  • Implement Txt2Txt GenAI, Img2Img GenAI, and Multimodal GenAI effectively.
  • Gain substantial practical experience in working with PaLM 2.
  • Apply the acquired knowledge seamlessly in real-world scenarios, solving complex problems and enhancing AI applications.
This comprehensive course on Working with Large Language Models is designed to equip participants with the knowledge, skills, and confidence to work effectively with various large language models, ensuring they are well-prepared in the field of AI.

Prerequisites

  • Understanding of Machine Learning Concepts
  • Deep Learning Fundamentals
  • Experience with NLP Techniques
  • Programming Skills
  • Hands-on Experience with Data Handling
  • Mathematics Background
  • Hardware Understanding

Curriculum

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In this module, participants will be introduced to the world of Txt2Txt GenAI, laying the foundation for understanding unimodal mappings and various language models. Lessons:
  • Overview of Txt2Txt GenAI.
  • Introduction to Unimodal Mappings.
  • Understanding the Significance of Txt2Txt GenAI in AI.
Lab:
  • Hands-on session: Exploring the basics of Txt2Txt GenAI.
  • Interactive exercises: Working with unimodal mappings.
After completing this module, students will be able to:
  • Understand the basic concepts of Txt2Txt GenAI.
  • Gain insights into unimodal mappings.
This module will delve into the world of Statistical Language Models, offering participants a comprehensive understanding of their functionality and application. Lessons:
  • Introduction to Statistical Language Models.
  • Exploring the Applications of Statistical Language Models.
  • Hands-on Experience with Statistical Language Models.
Lab:
  • Hands-on workshop: Working with Statistical Language Models.
  • Interactive exercises: Experimenting with various Statistical Language Models.
After completing this module, students will be able to:
  • Understand the concept and application of Statistical Language Models.
  • Gain hands-on experience with Statistical Language Models.
In this module, participants will explore Neural Language Models, understanding their architecture, functionality, and significance in AI. Lessons:
  • Overview of Neural Language Models.
  • Deep Dive into the Architecture of Neural Language Models.
  • Exploring the Applications of Neural Language Models.
Lab:
  • Hands-on session: Working with Neural Language Models.
  • Interactive exercises: Understanding the architecture of Neural Language Models.
After completing this module, students will be able to:
  • Understand the architecture and functionality of Neural Language Models.
  • Explore the applications of Neural Language Models.
This module will guide participants through working with Statistical and Probabilistic Language Models using Python and Keras, offering hands-on experience and insights into their practical application. Lessons:
  • 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.
Lab:
  • Hands-on workshop: Implementing SLM and PLM using Python and Keras.
  • Interactive exercises: Working with SLM and PLM in practical scenarios.
After completing this module, students will be able to:
  • Understand and implement SLM and PLM using Python and Keras.
  • Gain practical experience with SLM and PLM.
In this module, participants will delve deeper into Seq2seq Models, understanding their architecture, functionality, and applications in various AI tasks. Lessons:
  • Comprehensive Overview of Seq2seq Models.
  • Exploring the Architecture and Functionality of Seq2seq Models.
  • Real-World Applications of Seq2seq Models.
Lab:
  • Hands-on session: Working with Seq2seq Models.
  • Interactive exercises: Exploring the applications of Seq2seq Models.
After completing this module, students will be able to:
  • Understand the architecture and functionality of Seq2seq Models.
  • Explore the real-world applications of Seq2seq Models.
This module introduces participants to Hugging Face Transformer Pipelines, offering insights into their functionality, implementation, and application in AI. Lessons:
  • Introduction to Hugging Face Transformer Pipelines.
  • Exploring the Functionality and Implementation of Transformer Pipelines.
  • Hands-on Experience with Hugging Face Transformer Pipelines.
Lab:
  • Hands-on workshop: Implementing Hugging Face Transformer Pipelines.
  • Interactive exercises: Working with Transformer Pipelines in AI tasks.
After completing this module, students will be able to:
  • Understand the concept and functionality of Hugging Face Transformer Pipelines.
  • Gain hands-on experience with Transformer Pipelines.
This module will guide participants through the concept of Transfer Learning in Natural Language Processing, offering insights into its practical application and significance. Lessons:
  • Introduction to Transfer Learning in NLP.
  • Exploring the Applications of Transfer Learning in NLP.
  • Hands-on Experience with Transfer Learning in NLP.
Lab:
  • Hands-on workshop: Implementing Transfer Learning in NLP.
  • Interactive exercises: Working with Transfer Learning in practical scenarios.
After completing this module, students will be able to:
  • Understand the concept and application of Transfer Learning in NLP.
  • Gain practical experience with Transfer Learning in NLP.
In this module, participants will delve deeper into the fundamentals of GPT, understanding the differences and advancements from GPT3.5 to GPT4. Lessons:
  • Comprehensive Overview of GPT Fundamentals.
  • Exploring the Differences between GPT3.5 and GPT4.
  • Understanding the Advancements in GPT4.
Lab:
  • Hands-on session: Working with GPT3.5 and GPT4.
  • Interactive exercises: Exploring the advancements in GPT4.
After completing this module, students will be able to:
  • Understand the fundamentals of GPT.
  • Differentiate between GPT3.5 and GPT4.
  • Explore the advancements and features of GPT4.
This module introduces participants to ChatGPT and the OpenAI API, offering insights into their functionality, implementation, and application in AI. Lessons:
  • Introduction to ChatGPT and OpenAI API.
  • Exploring the Functionality and Implementation of ChatGPT and OpenAI API.
  • Hands-on Experience with ChatGPT and OpenAI API.
Lab:
  • Hands-on workshop: Implementing ChatGPT and OpenAI API.
  • Interactive exercises: Working with ChatGPT and OpenAI API in AI tasks.
After completing this module, students will be able to:
  • Understand the concept and functionality of ChatGPT and OpenAI API.
  • Gain hands-on experience with ChatGPT and OpenAI API.
This module will guide participants through creating a ChatGPT Clone using Google Colab and Streamlit, offering hands-on experience and insights into their practical application. Lessons:
  • Introduction to ChatGPT Clone in Google Colab and Streamlit.
  • Exploring the Implementation of ChatGPT Clone.
  • Hands-on Experience with ChatGPT Clone in Google Colab and Streamlit.
Lab:
  • Hands-on workshop: Implementing ChatGPT Clone using Google Colab and Streamlit.
  • Interactive exercises: Working with ChatGPT Clone in practical scenarios.
After completing this module, students will be able to:
  • Understand and implement ChatGPT Clone using Google Colab and Streamlit.
  • Gain practical experience with ChatGPT Clone.
In this module, participants will be introduced to Img2Img GenAI, laying the foundation for understanding Auto-Encoder visualization and various other concepts. Lessons:
    • Overview of Img2Img GenAI.
    • Introduction to Auto-Encoder Visualization.
    • Understanding the Significance of Img2Img GenAI in AI.
Lab:
        • Hands-on session: Exploring the basics of Img2Img GenAI.
        • Interactive exercises: Working with Auto-Encoder visualization.
After completing this module, students will be able to:
          • Understand the basic concepts of Img2Img GenAI.
          • Gain insights into Auto-Encoder visualization.
This module will delve into the world of Variational Auto-Encoder, offering participants a comprehensive understanding of their functionality and application. Lessons:
  • Introduction to Variational Auto-Encoder.
  • Exploring the Applications of Variational Auto-Encoder.
  • Hands-on Experience with Variational Auto-Encoder.
Lab:
  • Hands-on workshop: Working with Variational Auto-Encoder.
  • Interactive exercises: Experimenting with various Variational Auto-Encoder.
After completing this module, students will be able to:
  • Understand the concept and application of Variational Auto-Encoder.
  • Gain hands-on experience with Variational Auto-Encoder.
This module will guide participants through the process of coding Auto-Encoders in Keras, offering hands-on experience and insights into their practical application. Lessons:
  • Introduction to Coding AE in Keras.
  • Exploring the Implementation of AE in Keras.
  • Hands-on Experience with Coding AE in Keras.
Lab:
  • Hands-on workshop: Implementing AE using Keras.
  • Interactive exercises: Working with AE in practical scenarios.
After completing this module, students will be able to:
  • Understand and implement AE using Keras.
  • Gain practical experience with coding AE in Keras.
In this module, participants will delve deeper into the process of training Generative Adversarial Networks, understanding their architecture, functionality, and applications in various AI tasks. Lessons:
  • Comprehensive Overview of Training GANs.
  • Exploring the Architecture and Functionality of GANs.
  • Real-World Applications of Training GANs.
Lab:
  • Hands-on session: Working with Training GANs.
  • Interactive exercises: Exploring the applications of Training GANs.
After completing this module, students will be able to:
  • Understand the architecture and functionality of Training GANs.
  • Explore the real-world applications of Training GANs.
This module introduces participants to Multimodal GenAI, offering insights into multi-modal Txt2Img generation, Latent Diffusion Models, and other related concepts. Lessons:
  • Introduction to Multimodal GenAI.
  • Exploring Multimodal Txt2Img Generation.
  • Understanding Latent Diffusion Models.
Lab:
  • Hands-on workshop: Implementing Multimodal GenAI.
  • Interactive exercises: Working with Multi-modal Txt2Img Generation and Latent Diffusion Models.
After completing this module, students will be able to:
  • Understand the concept and functionality of Multimodal GenAI.
  • Gain hands-on experience with Multi-modal Txt2Img Generation and Latent Diffusion Models.
This module will guide participants through CLIP drop and Stable Diffusion, offering insights into their practical application and significance. Lessons:
  • 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.
Lab:
  • Hands-on workshop: Implementing CLIP drop and Stable Diffusion.
  • Interactive exercises: Working with CLIP drop and Stable Diffusion in practical scenarios.
After completing this module, students will be able to:
  • Understand the concept and application of CLIP drop and Stable Diffusion.
  • Gain practical experience with CLIP drop and Stable Diffusion.
In this module, participants will delve deeper into LeonardoAI, Midjourney, and OpenAPI - Dall-E3, understanding their architecture, functionality, and applications in various AI tasks. Lessons:
  • 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.
Lab:
  • Hands-on session: Working with LeonardoAI, Midjourney, and OpenAPI - Dall-E3.
  • Interactive exercises: Exploring the applications of LeonardoAI, Midjourney, and OpenAPI - Dall-E3.
After completing this module, students will be able to:
  • Understand the architecture and functionality of LeonardoAI, Midjourney, and OpenAPI - Dall-E3.
  • Explore the real-world applications of LeonardoAI, Midjourney, and OpenAPI - Dall-E3.</li
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This module introduces participants to Txt2Voice Generation with Evenlabs, offering insights into their functionality, implementation, and application in AI. Lessons:
  • Introduction to Txt2Voice Generation - Evenlabs.
  • Exploring the Functionality and Implementation of Txt2Voice Generation - Evenlabs.
  • Hands-on Experience with Txt2Voice Generation - Evenlabs.
Lab:
  • Hands-on workshop: Implementing Txt2Voice Generation - Evenlabs.
  • Interactive exercises: Working with Txt2Voice Generation - Evenlabs in AI tasks.
After completing this module, students will be able to:
  • Understand the concept and functionality of Txt2Voice Generation - Evenlabs.
  • Gain hands-on experience with Txt2Voice Generation - Evenlabs.
In this module, participants will be introduced to PaLM 2, laying the foundation for understanding the Pathway Language Model journey and various other concepts. Lessons:
  • Overview of PaLM 2.
  • Introduction to Pathway Language Model Journey.
  • Understanding the Significance of PaLM 2 in AI.
Lab:
  • Hands-on session: Exploring the basics of PaLM 2.
  • Interactive exercises: Working with Pathway Language Model Journey.
After completing this module, students will be able to:
  • Understand the basic concepts of PaLM 2.
  • Gain insights into Pathway Language Model Journey.
This module will delve into the world of compute optimal scaling and model architecture, offering participants a comprehensive understanding of their functionality and application. Lessons:
  • 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.
Lab:
  • Hands-on workshop: Working with Compute Optimal Scaling and Model Architecture.
  • Interactive exercises: Experimenting with various Compute Optimal Scaling and Model Architecture.
After completing this module, students will be able to:
  • Understand the concept and application of Compute Optimal Scaling and Model Architecture.
  • Gain hands-on experience with Compute Optimal Scaling and Model Architecture.
This module will guide participants through Bard and PaLM API, offering insights into their practical application and significance. Lessons:
  • Introduction to Bard and PaLM API.
  • Exploring the Applications of Bard and PaLM API.
  • Hands-on Experience with Bard and PaLM API.
Lab:
  • Hands-on workshop: Implementing Bard and PaLM API.
  • Interactive exercises: Working with Bard and PaLM API in practical scenarios.
After completing this module, students will be able to:
  • Understand the concept and application of Bard and PaLM API.
  • Gain practical experience with Bard and PaLM API.
This module will guide participants through the use of PaLM API in Vertex AI, offering insights into its practical application and significance. Lessons:
  • 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.
Lab:
  • Hands-on workshop: Implementing PaLM API in Vertex AI.
  • Interactive exercises: Working with PaLM API in Vertex AI in practical scenarios.
After completing this module, students will be able to:
  • Understand the concept and application of PaLM API in Ver
  • Gain practical experience with PaLM API in Vertex AI.
In this module, participants will be introduced to MakerSuite, laying the foundation for understanding its various functionalities and applications. Lessons:
  • Overview of MakerSuite.
  • Introduction to the functionalities of MakerSuite.
  • Understanding the Significance of MakerSuite in AI.
Lab:
  • Hands-on session: Exploring the basics of MakerSuite.
  • Interactive exercises: Working with various functionalities of MakerSuite.
After completing this module, students will be able to:
  • Understand the basic concepts of MakerSuite.
  • Gain insights into the functionalities of MakerSuite.
This module will delve into the advanced features of MakerSuite, offering participants a comprehensive understanding of their functionality and application. Lessons:
  • Introduction to Advanced Features of MakerSuite.
  • Exploring the Applications of Advanced Features in MakerSuite.
  • Hands-on Experience with Advanced Features of MakerSuite.
Lab:
  • Hands-on workshop: Working with Advanced Features in MakerSuite.
  • Interactive exercises: Experimenting with various Advanced Features in MakerSuite.
After completing this module, students will be able to:
  • Understand the concept and application of Advanced Features in MakerSuite.
  • Gain hands-on experience with Advanced Features in MakerSuite.

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FAQs

Generative AI is a subset of AI that focuses on understanding patterns and structure in data and then using that to create more data like it.
Until now, machines couldn’t exhibit behavior that was indistinguishable from human responses. Generative AI has made that possible. In a world that operates on the principle of ‘Survival of the Fittest’, embracing Generative AI could be the edge you need to boost your productivity, improve operations, be more reliable and consistent, and do so much more.
Large Language Models are deep learning algorithms or models that can recognize, summarize, translate, predict, and generate content using very large datasets.
Yes, absolutely! This Large Language Models online certification course is a virtual, instructor-led program so you can enroll and learn from anywhere.
This large language models training course is designed to be highly hands-on for all learners. Roughly 70% of this large language models certification course would be hands-on practical training while 30% would be theoretical studies.
Yes! Every module will be pre- and post-assessed during this large language models online training course.
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