LLMs, or Large Language Models, are a type of generative AI that has been gaining attention in recent years. They're designed to process and generate human-like language, and can be used for a wide range of applications, from chatbots to text summarization.
One of the key benefits of LLMs is their ability to learn from large datasets, allowing them to improve their performance over time. This is made possible by their complex neural network architecture, which enables them to identify patterns and relationships in the data.
LLMs have been used in various industries, including customer service, where they can be used to power chatbots that can respond to customer inquiries. By leveraging LLMs, businesses can provide 24/7 support to their customers, improving their overall experience.
Their ability to generate human-like language makes them a valuable tool for content creation, such as writing articles or generating product descriptions.
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What to Expect
You're about to embark on a journey to learn about LLMs and Generative AI, and I'm excited to guide you through what to expect.
You'll gain foundational knowledge, practical skills, and a functional understanding of how generative AI works. This means you'll be able to grasp the basics and apply them in real-world scenarios.
You'll dive into the latest research on Gen AI to understand how companies are creating value with cutting-edge technology. This is where you'll get to see the latest trends and innovations in the field.
You'll be learning from expert AWS AI practitioners who actively build and deploy AI in business use-cases today. That's like getting hands-on experience from someone who's already doing it in the real world!
Course Details
This course provides a comprehensive understanding of generative AI, covering foundational knowledge, practical skills, and a functional understanding of how it works.
You'll learn from expert AWS AI practitioners who actively build and deploy AI in business use-cases today.
The course includes instruction on the latest research on generative AI, helping you understand how companies are creating value with cutting-edge technology.
Worth a look: Generative Ai Certification Microsoft
What's Included
In this course, you'll have access to a wealth of educational materials to help you learn about Generative AI and LLMs.
There are 17 videos in total, covering topics such as Course Introduction, Generative AI & LLMs, and Pre-training large language models.
You'll also have 7 readings to work through, which will provide you with a deeper understanding of the subject matter.
To put your knowledge into practice, you'll complete 1 assignment, which will help you apply what you've learned.
Additionally, you'll have 2 app items to explore, which will provide you with hands-on experience with the concepts learned in the course.
Here's a breakdown of the course materials:
- Course videos: 17
- Readings: 7
- Assignment: 1
- App items: 2
Learner Reviews
The Generative AI project lifecycle course has received overwhelmingly positive reviews from learners.
82.76% of learners have given the course a 5-star rating, indicating a high level of satisfaction with the course content and teaching.
Many learners have found the course to be comprehensive and informative, with one learner noting that it has been an "extra addition in enhancing my understanding of the Generative AI project lifecycle."
A total of 96.12% of learners have given the course a rating of 4 stars or higher, suggesting that the course has met or exceeded their expectations.
Here's a breakdown of the learner reviews:
- 5 stars: 82.76%
- 4 stars: 13.99%
- 3 stars: 2.35%
- 2 stars: 0.37%
- 1 star: 0.51%
Generative AI Overview
Generative AI is built on top of models that are trained with large sets of data, such as natural language, images, audio, and code. These models use patterns identified in the training data to produce new, statistically similar content.
Generative AI models are designed to produce unique content, so they won't generate the same output for identical inputs. They work by parsing user input into a form of data the model can understand, identifying matching patterns from its training, and combining them to build the final output.
Some generative AI applications produce code from natural language inputs, providing quick implementations of common operations or translating code from one language to another. For example, given the input "Show me how to code a game of tic-tac-toe with C#", a generative AI application might produce the code to create a simple console-based tic-tac-toe game in C#.
Here are some key tasks you can perform with generative AI:
- Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle
- Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases
- Use empirical scaling laws to optimize the model’s objective function across dataset size, compute budget, and inference requirements
- Apply state-of-the art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of your project
Natural Generation
Natural Generation is a powerful application of Generative AI, allowing computers to create new, unique content that resembles human language. This can include text, audio, and even images.
Some examples of natural language generation include professionally worded summaries for a LinkedIn profile, rich descriptions of items in an online storefront, and ideas for healthy meals. For instance, given the user input "Give me three ideas for a healthy breakfast including peppers", a generative AI application might produce a list of three breakfast ideas.
Professionally worded summaries can be generated for various purposes, such as LinkedIn profiles or online storefronts. Rich descriptions can be created for products, making them more appealing to potential customers.
Generative AI applications can produce audio outputs from natural language inputs, such as synthesizing natural sounding voices or creating music in a specific style. This can be useful for digital voice assistants, music production, or reproducing a user's original voice from reference recordings.
Here are some examples of natural language generation applications:
- Professionally worded summaries for a LinkedIn profile
- Rich descriptions of items in an online storefront
- Ideas for healthy meals
- Synthesizing natural sounding voices
- Creating music in a specific style
- Modifying input audio files
Examples of Popular Foundation Models
Gemini is a multimodal model from Google DeepMind that can understand virtually any input and generate almost any output. It's a powerful tool that can be used with text, images, video, or code in Vertex AI.
Gemini's advanced reasoning capabilities make it ideal for tasks like extracting text from images.
Developers can try sample prompts for extracting text from images, converting image text to JSON, and even generating answers about uploaded images.
Expand your knowledge: Generative Ai by Getty
Frequently Asked Questions
Is ChatGPT LLM or generative AI?
ChatGPT is a generative AI tool, not a Large Language Model (LLM), as it creates new content like text, images, and music. This makes it a specialized form of AI designed to produce original human-like responses.
What type of AI is LLM?
LLMs are a type of large deep learning model, specifically a type of neural network known as a transformer. This architecture enables LLMs to process and understand vast amounts of data with high accuracy.
What is the difference between AI ML DL and generative AI?
AI is the overarching field, while ML and DL are subsets that focus on machine learning and complex data modeling. Generative AI is a specific application within AI that creates new, original content from learned data
What is the role of large language models in generative AI?
Large language models (LLMs) generate human-like text by analyzing extensive text data and employing patterns to produce novel outputs. They are a key component of generative AI, often paired with technologies like GANs and VAEs to create sophisticated language models.
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