Mastering Generative AI: Language Models with Transformers from edX

The demand for transformer-based language models is skyrocketing. AI engineers skilled in using transformer-based models for NLP are essential for developing successful gen AI applications. This course builds the job-ready skills employers need.

During the course, you’ll explore the concepts of transformer-based models for natural language processing (NLP). You’ll look at how to apply transformer-based models for text classification, focusing on the encoder component. Plus, you’ll learn about positional encoding, word embedding, and attention mechanisms in language transformers, and their role in capturing contextual information and dependencies.

You’ll learn about multi-head attention and decoder-based language modeling with generative pre-trained transformers (GPT) for language translation. You’ll consider how to train models and implement models using PyTorch. You’ll explore encoder-based models with bidirectional encoder representations from transformers (BERT) and train them using masked language modeling (MLM) and next sentence prediction (NSP). Plus, you’ll learn to apply transformers for translation using transformer architecture and implement it using PyTorch.

Throughout, you’ll apply your new skills practically in hands-on activities and you’ll complete a final project tackling a real-world scenario.

If you’re looking to build job-ready skills for gen AI applications employers are looking for, ENROLL TODAY and enhance your resume in just 2 weeks!

Prerequisites: To enroll for this course, you need a working knowledge of Python, PyTorch, and machine learning.

What's inside

Learning objectives

Job-ready skills in transformer-based models for nlp employers are looking for in just 2 weeks.
A good understanding of attention mechanisms in transformers, including their role in capturing contextual information.
A good understanding of language modeling with decoder-based gpt and encoder-based bert.

How to implement positional encoding, masking, attention mechanism, document classification, and llms like gpt and bert.
How to use transformer-based models and pytorch functions for text classification, language translation, and modeling.

Job-ready skills in transformer-based models for nlp employers are looking for in just 2 weeks.
A good understanding of attention mechanisms in transformers, including their role in capturing contextual information.
A good understanding of language modeling with decoder-based gpt and encoder-based bert.
How to implement positional encoding, masking, attention mechanism, document classification, and llms like gpt and bert.
How to use transformer-based models and pytorch functions for text classification, language translation, and modeling.

Syllabus

Module 1: Fundamental Concepts of Transformer Architecture

Video: Course Introduction

Reading: Professional Certificate Overview

Reading: General Information

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Develops job-ready skills in transformer-based models for NLP, which are essential for developing successful gen AI applications

Requires a working knowledge of Python, PyTorch, and machine learning, suggesting it is designed for those with some existing experience

Explores positional encoding, word embedding, and attention mechanisms, which are core concepts in understanding language transformers

Teaches how to train models and implement them using PyTorch, a popular framework for building machine learning models

Presented by IBM, a company recognized for its contributions to artificial intelligence and natural language processing

Covers encoder-based models with BERT and decoder-based models with GPT, which are foundational models in the field of NLP

Reviews summary

Practical introduction to transformers and language models

According to learners, this course offers a practical introduction to transformer-based models for Generative AI and NLP. Students particularly highlight the hands-on labs and coding exercises as a major strength, finding them very helpful for implementing concepts using PyTorch. While the course covers key architectures like BERT and GPT, some reviewers mention that the theoretical depth could be improved and that a strong background in Python, PyTorch, and ML is essential due to the fast pace. Overall, the course is seen as highly relevant for job skills in the current AI landscape.

Provides a good intro but not deep dive.

"Gives a good high-level overview of transformers, BERT, and GPT, but I felt it could go deeper into the theoretical mechanics."

"The explanations are clear but sometimes feel a bit superficial for complex topics like the full attention mechanism details."

"Good starting point, but if you need to understand the mathematics or advanced variations, you'll need other resources."

"It covers the 'what' and 'how to implement', but less on the 'why' or nuances."

Skills taught are highly relevant for AI jobs.

"Felt like I gained practical, marketable skills directly applicable to industry needs."

"The skills taught here are exactly what's in demand for AI/ML engineer roles focusing on NLP and GenAI."

"Taking this course directly helped me understand concepts used in my current work projects."

"Highly relevant course for anyone looking to break into or advance in the Generative AI field."

Course shines in practical coding examples.

"The hands-on labs and coding projects are the strongest part of the course for me, they really helped solidify my understanding."

"Loved the practical implementation parts using PyTorch. It's exactly what I needed to start working with these models."

"The course is very practical, giving you the tools and confidence to start implementing transformer models right away."

"I appreciated the emphasis on practical application through coding exercises, making the concepts less abstract."

Assumes strong prior knowledge in ML/PyTorch.

"Be warned, you need a very solid background in PyTorch and general ML. The course moves quickly and doesn't spend much time on basics."

"While prerequisites are listed, the expected level felt higher than stated. Found myself struggling with the PyTorch parts sometimes."

"This course is best if you're already comfortable with neural networks and PyTorch implementation details."

"It assumes you can hit the ground running with PyTorch; it's not a PyTorch tutorial."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Mastering Generative AI: Language Models with Transformers with these activities:

Review PyTorch Fundamentals

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Strengthen your understanding of PyTorch tensors, automatic differentiation, and neural network building blocks before diving into transformer implementations.

Browse courses on PyTorch

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Complete a PyTorch tutorial covering basic tensor operations.
Implement a simple feedforward neural network for image classification.
Practice using autograd to calculate gradients and update model parameters.

Brush Up on Machine Learning Concepts

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Revisit key machine learning concepts like gradient descent, backpropagation, and model evaluation metrics to ensure a solid foundation for understanding transformers.

Browse courses on Machine Learning

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Review the concepts of supervised and unsupervised learning.
Study different optimization algorithms like Adam and SGD.
Understand the importance of regularization techniques.

Read "Attention is All You Need" paper

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Gain a thorough understanding of the original Transformer architecture by reading the foundational paper.

View Putting Knowledge to Work on Amazon

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Download and read the "Attention is All You Need" paper.
Take notes on the key concepts and architecture details.
Identify areas where you need further clarification.

Four other activities

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Implement Attention Mechanisms from Scratch

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Solidify your understanding of attention mechanisms by implementing them from scratch using PyTorch. This hands-on exercise will help you internalize the mathematical operations and code structure.

Browse courses on Attention Mechanism

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Implement the scaled dot-product attention mechanism.
Implement multi-head attention.
Test your implementation with sample input data.

Read "Hugging Face Transformers: Neural Network Models for Natural Language Processing"

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Learn how to effectively use the Hugging Face Transformers library for various NLP tasks.

View Putting Knowledge to Work on Amazon

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Obtain a copy of the "Hugging Face Transformers" book.
Read the chapters relevant to your interests and projects.
Experiment with the code examples provided in the book.

Fine-tune a Pre-trained BERT Model for Sentiment Analysis

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Apply your knowledge of BERT by fine-tuning a pre-trained model for sentiment analysis on a real-world dataset. This project will give you practical experience in using transformers for NLP tasks.

Browse courses on BERT

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Choose a sentiment analysis dataset (e.g., IMDb reviews).
Load a pre-trained BERT model from Hugging Face Transformers.
Fine-tune the model on your chosen dataset.
Evaluate the model's performance on a test set.

Write a Blog Post Explaining Positional Encoding

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Deepen your understanding of positional encoding by writing a blog post explaining its purpose and implementation. Explaining the concept to others will reinforce your own knowledge.

Browse courses on Transformers

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Research different positional encoding techniques.
Write a clear and concise explanation of positional encoding.
Include diagrams and examples to illustrate the concept.

Career center

Learners who complete Mastering Generative AI: Language Models with Transformers will develop knowledge and skills that may be useful to these careers:

Natural Language Processing Engineer

A natural language processing engineer develops systems that allow computers to understand and process human language. This course immerses you in the core concepts of transformer-based models for natural language processing, including positional encoding and specific encoder and decoder architectures. As an aspiring natural language processing engineer, the course's exploration of text classification, language translation, and transformer implementations in PyTorch are vital for solving real-world NLP problems and creating cutting-edge applications. The focus on attention mechanisms and the ability to practically apply learnings in hands-on activities further builds your skillset.

See salaries and explore the career path for Natural Language Processing Engineer

Generative AI Specialist

A generative AI specialist develops and implements models that can generate new content, such as text, code, and images. This course provides core skills in transformer-based language models, a bedrock of many generative AI applications. You'll learn about encoder and decoder models, including GPT and BERT, gaining understanding of how they function and how to implement practical solutions. Understanding attention mechanisms, positional encoding, and transformer architectures learned in this course is crucial for building advanced generative models. The hands-on experience with PyTorch and practical projects solidifies these skills.

See salaries and explore the career path for Generative AI Specialist

Artificial Intelligence Engineer

Artificial intelligence engineers design and build intelligent systems, often specializing in areas like natural language. This course directly equips you with skills in transformer-based models for natural language processing, a crucial competency for any AI engineer working within the generative AI domain. By covering topics like multi-head attention, decoder based models, and encoder based models like BERT, this course provides the foundational knowledge you need. The course also specifically focuses on building job-ready skills for real-world applications. You'll also learn how to implement these models using PyTorch.

See salaries and explore the career path for Artificial Intelligence Engineer

Machine Learning Engineer

A machine learning engineer builds and maintains AI systems, often using transformer-based models to solve complex problems. This course helps you understand the underlying concepts of these models, such as attention mechanisms and positional encoding, which are crucial for effectively training and deploying them. You'll also gain hands-on experience in implementing models using PyTorch, a vital skill for a machine learning engineer. This course also covers both encoder and decoder models. The practical labs using PyTorch and final project directly contribute to the hands-on skillset a machine learning engineer needs.

See salaries and explore the career path for Machine Learning Engineer

AI Software Developer

An AI software developer builds and maintains software solutions that use artificial intelligence. This course helps any AI software developer gain a deeper understanding of the transformer architecture. The course focuses on applying transformer-based models for text classification and language translation. It also covers the implementation of these models using PyTorch. The practical labs, especially the implementations of GPT and BERT Models, are directly relevant to any AI software developer who wants to use language modeling in their projects. The course includes hands-on activities that reinforce learned concepts, which is good for AI developer roles.

See salaries and explore the career path for AI Software Developer

Text Analytics Specialist

Text analytics specialists extract actionable insights from large volumes of text data. This course provides an understanding of the transformer based models that are essential for modern text analytics. The course specifically covers text classification, attention mechanisms and encoder/decoder models. It provides hands-on experience in implementing models using pytorch. This course is suited for someone who wants to work with text data and use modern NLP techniques. The material covered in this course directly contributes to the skills needed for text analytics. This course may help build a foundation for any text analysis role.

See salaries and explore the career path for Text Analytics Specialist

Research Engineer

A research engineer applies engineering principles to research problems, often in the field of machine learning. This course is beneficial for research engineers interested in natural language processing, since it covers foundational concepts in transformer networks. The course's hands on implementation of models using PyTorch helps solidify theoretical knowledge. If you are a research engineer who wants to understand implementation details of models like GPT and BERT, this course is a good starting point. It covers attention mechanisms, positional encoding, and other important concepts. This course may help you develop a research project.

See salaries and explore the career path for Research Engineer

Computational Linguist

A computational linguist uses computer science techniques to analyze and process natural language. This course helps computational linguists understand the specific models and mechanisms driving modern language processing systems. The focus on transformer-based models, including encoder and decoder models like BERT and GPT, is highly relevant to a computational linguist. The course may specifically be helpful if you are a computational linguist looking to understand cutting-edge modeling techniques. The course also provides hands-on experience in using PyTorch for implementing these language models. It may also provide an understanding of the practical challenges of working with such models.

See salaries and explore the career path for Computational Linguist

AI Solutions Architect

AI solutions architects design and oversee the deployment of AI systems within organizations. This course can help solutions architects gain a deeper understanding of transformer-based models, and it can provide important context when designing solutions that rely on natural language processing. An architect might oversee the design and implementation of large language model systems, and this course helps build a fundamental understanding of the mechanisms behind these systems. The course focuses on practical skills such as text classification, language translation, and implementation using PyTorch. This course may aid an AI solutions architect choosing specific architecture needs.

See salaries and explore the career path for AI Solutions Architect

Data Scientist

Data scientists analyze and interpret complex data, often using machine learning techniques. This course may be useful if you're a data scientist who wants to expand into natural language processing. Many data scientists work with unstructured data, and this course helps build a foundation in processing text data. It also provides hands-on experience in applying transformer-based models for tasks like text classification and language translation using PyTorch. Although this course might not encompass all of a data scientist's responsibilities, the coverage of fundamental modeling techniques in AI makes it useful.

See salaries and explore the career path for Data Scientist

Research Scientist

A research scientist working in the field of artificial intelligence often needs to understand the intricacies of language models, and this course may be useful. Research scientists typically require advanced degrees. This course might help with specific projects in NLP involving transformer based models. This course’s focus on encoders like BERT, decoders like GPT, and attention mechanisms provides a good overview of how these models function. It may also be helpful for research scientists who want to understand the practical implementation of these models using PyTorch. The final project can potentially contribute to research work.

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AI Product Manager

An AI product manager defines the strategy and roadmap for AI-powered products. While product managers don't implement models themselves, this course may be useful for them to gain a deeper understanding of the capabilities and limitations of transformer-based AI. The course covers a foundational understanding of transformer networks for natural language processing, including how they handle text classification and language translation. The course may help an AI product manager make more informed decisions about product capabilities and features. The course's focus on practical, real-world applications is helpful for any AI product manager.

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Data Analyst

Data analysts interpret data to make informed decisions for organizations. While they don't typically build NLP models, this course may be helpful if an analyst works with textual data and wants to gain a foundational understanding of how it can be processed and analyzed. The course covers a variety of topics including text classification, and it provides an overview of how attention mechanisms and transformer architectures work. Text analysis may be a component of an analyst's work, so this course provides some contextual help. This course may be helpful to better understand the complexities of processing text data.

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Software Engineer

A software engineer builds and maintains software applications. While they don't always work directly with AI, this course might provide useful context for an engineer working on projects that incorporate natural language processing. This is especially true if you are a software engineer that wants to understand how to use libraries that rely on transformer based models. The course introduces various transformer-based architectures and their implementation in PyTorch. The course specifically provides an understanding of the fundamentals of transformer-based models, including positional encoding and attention mechanisms. This course may help broaden the scope of a software engineer's skillset.

See salaries and explore the career path for Software Engineer

Technical Project Manager

A technical project manager oversees technical projects, including those involving artificial intelligence. While they don't need to implement AI models, this course may provide insights into the underlying technology and techniques, which can be helpful when managing AI-related projects. The course covers transformer-based models for natural language processing, including practical implementation using PyTorch. Gaining familiarity with the terminology and processes involved in training language models is helpful for communicating with technical teams, and this course may broaden the technical knowledge of any project manager.

See salaries and explore the career path for Technical Project Manager

Mastering Generative AI

Language Models with Transformers

What's inside

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Syllabus

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Reviews summary

Practical introduction to transformers and language models

Activities

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