Welcome to "LLMs Mastery: Complete Guide to Generative AI & Transformers".
This comprehensive guide is designed to equip you with the knowledge and skills to build efficient, production-ready AI models using cutting-edge technologies.
Read more
Welcome to "LLMs Mastery: Complete Guide to Generative AI & Transformers".
This comprehensive guide is designed to equip you with the knowledge and skills to build efficient, production-ready AI models using cutting-edge technologies.
Welcome to "LLMs Mastery: Complete Guide to Generative AI & Transformers".
This comprehensive guide is designed to equip you with the knowledge and skills to build efficient, production-ready AI models using cutting-edge technologies.
Key Topics Covered:
Generative AI: Understand the principles and applications of Generative AI in creating new data instances.
ChatGPT & GPT4: Dive into the workings of advanced AI models like ChatGPT and GPT4.
LLMs: Learn about Language Models (LLMs) and their role in understanding and generating human-like text.
Encoder-Decoders: Master the concept of encoder-decoder models in the context of Transformers.
Machine Learning & Data: Understand the role of machine learning and data in training robust AI models.
What You Will Learn:
Natural Language Processing Basics
️ Journey through the evolution of NLP, from the Rule-Based Systems Era to the Embeddings Era.
️ Lay a solid foundation in NLP for more advanced topics.
Introduction to Transformers
Learn about the architecture of Transformer models, including attention mechanisms, encoders, and decoders.
️ Understand pre-training and fine-tuning strategies.
Explore tokenization and embeddings, critical components of Transformer models.
Popular Transformer Models
Dive into popular Transformer models: BERT (encoder-only), GPT (decoder-only), and T5 (encoder-decoder).
Gain deeper insights into the capabilities and potential of Transformer technology.
Using Transformers (Practical)
Get hands-on experience with Transformer models in real-world applications.
Master tokenization, embeddings, and masked language models (MLMs).
Build a Semantic Search Index project.
NLP Tasks and Applications (Practical)
Learn how to use BERT for extractive question answering, GPT for building a personal assistant, and T5 for writing product reviews.
Experience the practical applications of NLP tasks.
Who This Course Is For:
Perfect for anyone interested in AI, machine learning, and data science.
Ideal for both seasoned professionals and curious beginners.
Ready to dive into the world of Generative AI and Transformers
Enroll today and start your journey to mastery.
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What's inside
Learning objectives
- Grasp nlp fundamentals: understand the evolution and key concepts of natural language processing, from rule-based systems to the deep learning era.
- Master transformers: learn the architecture and application of transformers in depth. including tokenization, embeddings, pre-training & fine-tunning.
- Understand the principles behind generative ai: get familiar with building and tweaking generative models for any real-world use case.
- Utilize transformer models: overview llms and encoder-decoder models like bert, gpt, and t5 in various nlp tasks.
- Develop practical, useful and production ready nlp tasks: build personal assistants, model for writing product reviews and answering questions..
- Grasp nlp fundamentals: understand the evolution and key concepts of natural language processing, from rule-based systems to the deep learning era.
- Master transformers: learn the architecture and application of transformers in depth. including tokenization, embeddings, pre-training & fine-tunning.
- Understand the principles behind generative ai: get familiar with building and tweaking generative models for any real-world use case.
- Utilize transformer models: overview llms and encoder-decoder models like bert, gpt, and t5 in various nlp tasks.
- Develop practical, useful and production ready nlp tasks: build personal assistants, model for writing product reviews and answering questions..
Syllabus
Using Transformers: Building Blocks and Hidden Gems (Practical)
Introduction
Building Blocks
Getting Started: How to Make the Best Use of this Course
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Syllabus
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Activities
Review Python for machine learning
Show steps
Strengthening your Python programming skills will improve your ability to code generative AI and transformer models effectively and efficiently.
Browse courses on
Python
Show steps
-
Review basic Python syntax and data types.
-
Practice working with Python libraries such as numpy and pandas.
-
Complete coding exercises and tutorials to reinforce your understanding.
Read 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
This book provides a comprehensive foundation in deep learning, which is essential for understanding and applying generative AI and transformer models.
View
Deep Learning
on Amazon
Show steps
-
Read the introductory chapters to gain an overview of deep learning.
-
Work through the chapters on neural networks, backpropagation, and optimization.
-
Review the sections on convolutional neural networks and recurrent neural networks.
Participate in online discussion forums or study groups on generative AI and transformers
Show steps
Engaging with peers will provide opportunities to share knowledge, ask questions, and gain new perspectives on the course material.
Browse courses on
Generative AI
Show steps
-
Join an online discussion forum or study group.
-
Participate in discussions by asking questions, sharing insights, and providing feedback to others.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Build a simple generative AI model using a pre-trained transformer
Show steps
Creating a practical generative AI model will provide hands-on experience in applying the concepts and techniques learned in the course.
Browse courses on
Generative AI
Show steps
-
Choose a pre-trained transformer model, such as GPT-3 or T5.
-
Load the model and fine-tune it on a specific dataset.
-
Generate text or images using your fine-tuned model.
Follow tutorials on advanced transformer models, such as BERT and RoBERTa
Show steps
Exploring advanced transformer models will deepen your understanding of their capabilities and limitations.
Browse courses on
Transformers
Show steps
-
Search for tutorials on BERT or RoBERTa.
-
Follow the tutorials to learn about the architecture and applications of these models.
-
Complete any coding exercises or assignments included in the tutorials.
Compile a list of resources on generative AI and transformers
Show steps
Curating a collection of resources will help you organize and retain information about the course material.
Browse courses on
Generative AI
Show steps
-
Search for articles, tutorials, and documentation on generative AI and transformers.
-
Organize the resources into a structured list or database.
Offer to mentor other students in the course or in a related field
Show steps
Mentoring others will reinforce your understanding of the course material and improve your communication skills.
Show steps
-
Reach out to other students in the course or in a related field.
-
Offer to provide guidance and support on course-related topics.
Review Python for machine learning
Show steps
Strengthening your Python programming skills will improve your ability to code generative AI and transformer models effectively and efficiently.
Browse courses on
Python
Show steps
- Review basic Python syntax and data types.
- Practice working with Python libraries such as numpy and pandas.
- Complete coding exercises and tutorials to reinforce your understanding.
Read 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
This book provides a comprehensive foundation in deep learning, which is essential for understanding and applying generative AI and transformer models.
View
Deep Learning
on Amazon
Show steps
- Read the introductory chapters to gain an overview of deep learning.
- Work through the chapters on neural networks, backpropagation, and optimization.
- Review the sections on convolutional neural networks and recurrent neural networks.
Participate in online discussion forums or study groups on generative AI and transformers
Show steps
Engaging with peers will provide opportunities to share knowledge, ask questions, and gain new perspectives on the course material.
Browse courses on
Generative AI
Show steps
- Join an online discussion forum or study group.
- Participate in discussions by asking questions, sharing insights, and providing feedback to others.
Build a simple generative AI model using a pre-trained transformer
Show steps
Creating a practical generative AI model will provide hands-on experience in applying the concepts and techniques learned in the course.
Browse courses on
Generative AI
Show steps
- Choose a pre-trained transformer model, such as GPT-3 or T5.
- Load the model and fine-tune it on a specific dataset.
- Generate text or images using your fine-tuned model.
Follow tutorials on advanced transformer models, such as BERT and RoBERTa
Show steps
Exploring advanced transformer models will deepen your understanding of their capabilities and limitations.
Browse courses on
Transformers
Show steps
- Search for tutorials on BERT or RoBERTa.
- Follow the tutorials to learn about the architecture and applications of these models.
- Complete any coding exercises or assignments included in the tutorials.
Compile a list of resources on generative AI and transformers
Show steps
Curating a collection of resources will help you organize and retain information about the course material.
Browse courses on
Generative AI
Show steps
- Search for articles, tutorials, and documentation on generative AI and transformers.
- Organize the resources into a structured list or database.
Offer to mentor other students in the course or in a related field
Show steps
Mentoring others will reinforce your understanding of the course material and improve your communication skills.
Show steps
- Reach out to other students in the course or in a related field.
- Offer to provide guidance and support on course-related topics.
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