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Working with Large Language Models Training

The Working with Large Language Models training by AgileFever is tailored to deliver a thorough understanding and practical expertise in using diverse generative AI models, including Txt2Txt GenAI, Img2Img GenAI, Multimodal GenAI, and an overview of PaLM 2.

  • 40 hours of live, instructor-led training
  • Learn from experts and leaders in the Generative AI industry
  • Access 24/7 dedicated support
  • Enhance your skills through hands-on exercises and interactive lab activities
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Working with Large Language Models Course Overview

The Working with Large Language Models course by AgileFever is a 40-hour live training program led by top experts in the Generative AI field. Tailored for advanced learners, the course features a cutting-edge curriculum packed with expert insights, advanced strategies, interactive exercises, and lab activities, enabling participants to apply their skills right away.

On the completion of this course, learners will develop a deep understanding of large language models and, master their use effectively implement Txt2Txt GenAI, Img2Img GenAI, and Multimodal GenAI. This journey from fundamental concepts to advanced skills provides learners with a thorough understanding, enabling them to effectively utilize generative AI tools in their business environments.

Working with Large Language Models Course Highlights

  • Comprehensive understanding of various large language models.
  • Expertise in Txt2Txt GenAI, covering topics like Seq2seq Models and GPT Fundamentals.
  • Practical experience with Img2Img GenAI, including training GANs and using Auto-Encoders in Keras.
  • Insights into Multimodal GenAI, featuring CLIP Drop and Stable Diffusion techniques.
  • Advanced knowledge of PaLM 2, focusing on the Pathway Language Model journey and using the PaLM API in Vertex AI.

Working with Large Language Models Curriculum

  • 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.
  • Introduction to Statistical Language Models.
  • Exploring the Applications of Statistical Language Models.
  • Hands-on Experience with Statistical Language Models.
  • Overview of Neural Language Models.
  • Deep Dive into the Architecture of Neural Language Models.
  • Exploring the Applications of Neural Language Models.

Lab:

  • Hands-on workshop: Working with Statistical Language Models.
  • Interactive exercises: Experimenting with various Statistical 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.

Lab:

  • 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.

Lab:

  • 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.

Lab:

  • 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.

Lab:

  • Hands-on workshop: Implementing Transfer Learning in NLP.
  • Interactive exercises: Working with Transfer Learning in practical scenarios.
  • 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.
  • Introduction to ChatGPT and OpenAI API.
  • Exploring the Functionality and Implementation of ChatGPT and OpenAI API.
  • Hands-on Experience with ChatGPT and OpenAI API.
  • 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 and OpenAI API.
  • Interactive exercises: Working with ChatGPT and OpenAI API in AI tasks.
  • 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.

Lab:

  • 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.
  • 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: Working with Variational Auto-Encoder.
  • Interactive exercises: Experiment with various Variational Auto-Encoder.
  • 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.
  • Introduction to Multimodal GenAI.
  • Exploring Multimodal Txt2Img Generation.
  • Understanding Latent Diffusion Models.

Lab:

  • Hands-on session: Working with Training GANs.
  • Interactive exercises: Exploring the applications of Training GANs.
  • 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.
  • 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 workshop: Implementing CLIP drop and Stable Diffusion.
  • Interactive exercises: Working with CLIP drop and Stable Diffusion in practical scenarios.
  • 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.
  • Overview of PaLM 2.
  • Introduction to Pathway Language Model Journey.
  • Understanding the Significance of PaLM 2 in AI.

Lab:

  • Hands-on workshop: Implementing Txt2Voice Generation – Evenlabs.
  • Interactive exercises: Working with Txt2Voice Generation – Evenlabs in AI tasks.
  • Hands-on session: Exploring the basics of PaLM 2.
  • Interactive exercises: Working with Pathway Language Model Journey.
  • Introduction to Bard and PaLM API.
  • Exploring the Applications of Bard and PaLM API.
  • Hands-on Experience with Bard and PaLM API.
  • 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 Bard and PaLM API.
  • Interactive exercises: Working with Bard and PaLM API in practical scenarios.
  • 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.
  • 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.

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Who can attend the Working with Large Language Models Course?

The Working with Large Language Model Course from AgileFever is perfect for:

  • Data Scientists
  • Software Developers
  • Project Leads
  • AI Enthusiasts
  • Machine Learning Engineers
  • Entrepreneurs
  • Data Scientists
  • Software Developers
  • Project Leads
  • AI Enthusiasts
  • Machine Learning Engineers
  • Entrepreneurs
  • Data Scientists
  • Software Developers
  • Project Leads
  • AI Enthusiasts
  • Machine Learning Engineers
  • Entrepreneurs

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Working with Large Language Models Exam Details

Name of Exam – AgileFever Gen AI Working with Large Language Models Exam

Exam details are as follows:

  • Practical Exams, Lab Assessments, and Projects at the end of every completed module.
  • Knowledge of Machine Learning concepts
  • Fundamentals of Deep Learning
  • Experience with Natural Language Processing (NLP) techniques
  • Proficiency in programming
  • Practical experience with data handling
  • Strong foundation in mathematics
  • Understanding of hardware requirements

Benefits

Corporate Benefits

  • Ace GenAI projects  
  • Enhance productivity 
  • Improve project quality 
  • Faster implementation

Individual Benefits

  • Become proficient in prompt engineering and apply in MAANG companies 
  • Gain Gen AI skills that will rule the next decades
  • Gain expertise to be in demand across multiple industries – Technology, Healthcare, Education, Hospitality, Finance, Entertainment, Sports, and more

Certification Process

Step 1: Register for the Working with Large Language Models course with AgileFever

Step 2: Attend AgileFever’s 40 hours of live training

Step 3: Upon successful completion of the course, you will receive certification from AgileFever

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Testimonials

FAQs

The Gen AI – Working with Large Language Models course by AgileFever will help you by:

  • Deepening your understanding of large language models and generative AI technologies.
  • Equipping you with practical skills in implementing Txt2Txt GenAI, Img2Img GenAI, and Multimodal GenAI.
  • Providing hands-on experience with cutting-edge tools like PaLM 2, GPT, and GANs, which you can apply directly to real-world problems.
  • Enhancing your problem-solving abilities with advanced techniques, enabling you to innovate and optimize AI applications.
  • Expanding your career opportunities by mastering in-demand AI skills that are highly valued across industries

After completing the Gen AI – Working with Large Language Models course, you can apply for various job roles, including:

  • AI/ML Engineer
  • Data Scientist
  • Generative AI Specialist
  • Natural Language Processing (NLP) Engineer
  • Machine Learning Researcher
  • AI Solutions Architect
  • AI Application Developer
  • Deep Learning Engineer
  • Data Engineer
  • AI Product Manager
  • AI Consultant

These roles are highly sought after in industries such as tech, finance, healthcare, and e-commerce, where advanced AI technologies are being integrated into products and services.

Professionals who specialize in Large Language Models experience a huge demand across a variety of industries such as – IT, Retail, Manufacturing, Energy, Aerospace, Sports, Hospitality, eCommerce, and more.

AgileFever’s 40-hour Working with Large Language Models course, led by industry experts, offers a comprehensive curriculum with hands-on exercises and case studies. This immersive learning experience equips you with the skills to excel in the field of AI.

The instructors of this course are globally renowned Gen AI experts.

To attend the Working with Large Language Models course by AgileFever, you need to:


  • Knowledge of Machine Learning concepts
  • Fundamentals of Deep Learning
  • Experience with Natural Language Processing (NLP) techniques
  • Proficiency in programming
  • Practical experience with data handling
  • Strong foundation in mathematics
  • Understanding of hardware requirements


This course is powered by an industry-best curriculum and interactive lab activities to strengthen your fundamentals and skills.


Here are some of the topics covered in this course:

  • Txt2Txt Gen AI
  • Statistical and Neural Language Models
  • SLM and PLM in Python and Keras
  • Seq2seq Models
  • Hugging Face Transformer Pipelines
  • Transfer Learning in NLP
  • GPT Fundamentals and ChatGPT clones
  • Img2img GenAI
  • Variational Auto-Encoder, Coding AE in Keras
  • GANs and Multimodal GenAI
  • Txt2Voice Generation
  • PaLM2 and PaLM API
  • Advanced MakerSuite

The Working with Large Language Model training by AgileFever is ideal for:


  • AI Enthusiasts looking to enhance their knowledge of generative AI technologies.
  • Data Scientists and Machine Learning Engineers aiming to expand their expertise in advanced AI models.
  • Software Developers interested in integrating generative AI capabilities into applications.
  • Tech Leaders and Innovators seeking to leverage AI for business transformation.
  • Students and Researchers exploring cutting-edge advancements in generative AI.
  • Business Professionals wanting to apply generative AI solutions in real-world scenarios.

Corporates use Large Language Models (LLMs) for a variety of purposes, including:

  • Customer Support: Automating responses and chatbots for 24/7 customer service.
  • Content Creation: Generating blog posts, marketing copy, social media content, and more.
  • Data Analysis: Analyzing large amounts of unstructured data like customer feedback, emails, and reviews.
  • Automation: Streamlining repetitive tasks such as data entry, document processing, and report generation.
  • Personalization: Offering personalized recommendations and improving user experiences through tailored content.
  • Business Intelligence: Extracting insights from vast data sources to aid decision-making.
  • Translation and Localization: Assisting in real-time language translation and content localization for global audiences.
  • Internal Communication: Enhancing internal knowledge sharing through automated documentation, FAQs, and summarization tools.
  • Sentiment Analysis: Gauging customer sentiment and opinions from social media or product reviews.

By leveraging LLMs, businesses can improve efficiency, enhance customer experience, and drive innovation.

The future of NLP experts is incredibly promising. As NLP technology continues to advance, skilled professionals in this field will be in high demand. Here’s a glimpse into what the future holds for NLP experts:

1. Expanding Applications:

  • Healthcare: NLP can analyze medical records, research papers, and clinical trials to accelerate drug discovery and improve patient care.
  • Finance: NLP can analyze financial news, reports, and social media sentiment to inform investment decisions and risk assessment.
  • Education: NLP can personalize learning experiences, automate grading, and provide intelligent tutoring systems.
  • Customer Service: NLP-powered chatbots and virtual assistants can enhance customer support, automate routine tasks, and improve customer satisfaction.

2. Evolving Role:

  • Ethical Considerations: NLP experts will need to address ethical concerns related to bias, privacy, and the potential misuse of AI.
  • Interdisciplinary Collaboration: Successful NLP projects will require collaboration with experts from various fields, such as linguistics, computer science, and domain-specific knowledge.
  • Lifelong Learning: The rapid pace of technological advancement necessitates continuous learning and upskilling to stay ahead of the curve.

3. High Demand and Lucrative Careers:

As NLP becomes increasingly integrated into various industries, the demand for skilled NLP professionals will continue to grow. This translates into lucrative career opportunities and the potential for significant impact.

In conclusion, the future of NLP experts is bright. By staying updated with the latest advancements, embracing interdisciplinary collaboration, and addressing ethical considerations, NLP professionals can shape the future of technology and make a lasting impact on society.

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