14.11.2024
Intro to LLMs trending topic
with
Lauren Newbould
Start your AI Engineer career journey: Master Transformer Architecture and the Essentials of Modern AI
3 hours of content
2739 students
Start for free
What you get:
- 3 hours of content
- 19 Interactive exercises
- 3 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
Intro to LLMs trending topic
A course by
Lauren Newbould
Start for free
What you get:
- 3 hours of content
- 19 Interactive exercises
- 3 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
Start for free
What you get:
- 3 hours of content
- 19 Interactive exercises
- 3 Downloadable resources
- World-class instructor
- Closed captions
- Q&A support
- Future course updates
- Course exam
- Certificate of achievement
What You Learn
- Create your own AI-driven applications tailored to solve specific business problems
- Boost your career prospects by mastering AI Engineering skills
- Gain a solid understanding of key LLM concepts such as attention and self-attention, crucial for building intuitive AI systems
- Learn how to integrate Open AI’s API and create a bridge between your products and powerful AI foundation models
- Get an introduction to LangChain, the platform that streamlines the creation of AI-driven apps
- Explore HuggingFace to access the cutting-edge AI Engineering tools it offers
Top Choice of Leading Companies Worldwide
Industry leaders and professionals globally rely on this top-rated course to enhance their skills.
Course Description
In recent years, large language models (LLMs) have dominated the tech news for their incredible ability to write poetry, essays, social media content, code, and more. They’re the hot new topic in natural language processing. This Intro to Large Language Models course teaches you the knowledge and skills required to experiment and create your own language model solutions. Through a combination of video lessons and practical coding exercises, we’ll cover what these language models are, their functions, and ways to implement them into your own projects. Whether you want to generate content, create a chatbot, or train these models on your own custom data and NLP tasks, this course equips you with the fundamental tools and concepts to fine-tune LLM models and tackle these challenges.
Learn for Free
1.1 Introduction to the course
1.2 Course Materials and Notebooks
1.3 What are LLMs?
1.4 How large is an LLM?
1.5 General purpose models
1.6 Pre-training and fine tuning
Interactive Exercises
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.
Practice what you've learned with coding tasks, flashcards, fill in the blanks, multiple choice, and other fun exercises.
Curriculum
- 1. Introduction to Large Language Models7 Lessons 16 Min
We’ll begin our journey with an introduction into large language models., We’ll study the world of LLMs, their applications, training processes, and what datasets they’ve been trained on.
Introduction to the course2 minCourse Materials and Notebooks Read now1 minWhat are LLMs?3 minHow large is an LLM?3 minGeneral purpose models1 minPre-training and fine tuning3 minWhat can LLMs be used for?3 min - 2. The Transformer Architecture9 Lessons 24 Min
In this segment of the LLM course, we’ll break down the transformers' architecture and explain the mechanics behind encoders and decoders, embeddings, multi-headed attention, and the significance of a feed-forward layer. You’ll learn the advantages of transformers over RNNs.
Deep learning recap3 minThe problem with RNNs4 minThe solution: attention is all you need3 minThe transformer architecture1 minInput embeddings3 minMulti-headed attention4 minFeed-forward layer3 minMasked multihead attention1 minPredicting the final outputs2 min - 3. Getting started with GPT models10 Lessons 31 Min
We’ll examine GPT models closely and begin our practical part of the LLM tutorial. We’ll connect to OpenAI’s API and implement a simple chatbot with a personality: a poetic chatbot. I’ll also show you how to use LangChain to work with your own custom data, feeding information from the 365 web pages to our model.
What does GPT mean?1 minThe development of ChatGPT2 minOpenAI API3 minGenerating text2 minCustomizing GPT Output4 minKey word text summarization4 minCoding a simple chatbot6 minIntroduction to Langchain in Python1 minLangchain3 minAdding custom data to our chatbot5 min - 4. Hugging Face Transformers6 Lessons 27 Min
The Hugging Face package is an open-source package, which allows us an alternative way to interact with LLMs. We’ll learn about pre-trained and customized tokenizers and how to integrate Hugging Face into Pytorch and Tensorflow deep learning workflows.
Hugging Face package3 minThe transformer pipeline6 minPre-trained tokenizers9 minSpecial tokens3 minHugging Face and PyTorch, TensorFlow5 minSaving and loading models1 min - 5. Question and answer models with BERT7 Lessons 32 Min
This section of our Intro to Large Language Models course will explore BERT's architecture and contrast it with GPT models. It will delve into the workings of question-answering systems both theoretically and practically and examine variations of BERT—including the optimized RoBERTa and the smaller lightweight version DistilBERT.
GPT vs BERT3 minBERT architecture5 minLoading the model and tokenizer2 minBERT embeddings4 minCalculating the response6 minCreating a QA bot9 minBERT, RoBERTa, DistilBERT3 min - 6. Text classification with XLNet5 Lessons 25 Min
In the final Intro to Large Language Models course section, we’ll look under the hood of XLNET (a novel LLM), that uses permutations of data sets to train a model. We’ll also compare XLNet and our previously discussed models, BERT and GPT.
GPT vs BERT vs XLNET4 minPreprocessing our data10 minXLNet Embeddings4 minFine tuning XLNet4 minEvaluating our model3 min
Topics
Course Requirements
- Highly recommended to take the Intro to Python course first
Who Should Take This Course?
Level of difficulty: Intermediate
- Aspiring data analysts, data scientists, data engineers, machine learning engineers, and AI engineers
Exams and Certification
A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.
Meet Your Instructor
Guided by a comprehensive social science and statistics background, Lauren's data science career has taken her through several pivotal roles—from creating custom NLP solutions for non-profits in Nepal to providing insights for BBC Sport and the 2020 Olympics. Lauren has spoken at several conferences on how NLP can benefit those in developing countries and advocates for ethical and open data science. She aims to empower individuals and organizations to make confident, data-driven decisions and to ensure AI is fair and accessible for all.
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