The Artificial Intelligence (AI) ecosystem has evolved rapidly within the last five years, with Generative AI (GAI) leading this evolution. In reality, the Generative AI market is predicted to succeed in $36 billion by 2028, in comparison with $3.7 billion in 2023.
Today, Generative AI is affecting many industries, akin to healthcare, marketing, fashion, and entertainment because AI generators like AI image generators and AI video generators have shown us the potential to substitute manual human tasks. Nevertheless, advancing on this field requires a specialized AI skillset.
So, to make learning easier for AI enthusiasts, Google has launched 10 free courses for Generative AI. Before we discuss them, let’s see briefly what generative AI is.
What’s Generative AI & Why is Learning Generative AI Necessary?
Generative AI is a specialized AI domain that focuses on constructing models that may generate recent realistic content, like images, text, audio, or videos, using existing data samples.
For example, models like ChatGPT and DALL-E are distinguished examples of Generative AI as we at the moment are observing their real-world applications. ChatGPT is integrated into Bing’s search engine, whereas the Edge browser now incorporates DALL-E.
As Generative AI evolves, staying up-to-date with this technology has turn into crucial for several reasons:
- Ensures business productivity, cost-effectiveness, and increased efficiency.
- Encourages experimentation and creativity.
- Supports human-AI collaboration and augments human capabilities.
- Allows progressive problem-solving strategies.
Now, let’s have a look at how Google helps learners study Generative AI.
Google’s 10-Course Generative AI Learning Path
1. Introduction To Generative AI
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Beginner-level
~ 45 minutes
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- What’s Generative Artificial Intelligence, how it really works, what its applications are, and the way it differs from standard machine learning (ML) techniques.
- Covers Google tools for creating your individual Generative AI apps.
- You’ll also learn in regards to the Generative AI model types: unimodal or multimodal, on this course. Unimodal systems take just one input type, whereas multimodal systems can take a couple of input type.
2. Introduction to Large Language Models
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Beginner-level
~ 45 minutes
No
- This course explores LLMs (Large Language Models) – AI models trained on large amounts of textual data. “Google’s Bard AI” is a superb example of an LLM that makes advanced human-machine interaction possible.
- Understand how LLMs are used for sentiment evaluation.
- Find out about prompt tuning, through which the prompts given to a language model are refined to attain the specified output.
- Cover the tools that Google provides for the event of Gen AI.
3. Introduction to Responsible AI
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Beginner-level
~ 1 day (Complete the quiz/lab in your individual time)
No
- What’s Responsible Artificial Intelligence? Why it’s necessary, and the way Google implements this technology in its products.
- An introduction to the 7 Responsible AI principles of Google.
4. Generative AI Fundamentals
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Beginner-level
~ 1 day (Complete the quiz/lab in your individual time)
No
- Accommodates all of the content from the previous three courses.
- Features a final quiz through which you’ll show your understanding of the basic concepts of Generative AI.
5. Introduction To Image Generation
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Beginner-level
~ 1 day (Complete the quiz/lab in your individual time)
Knowledge of ML, Deep Learning (DL), Convolutional Neural Nets (CNNs), and Python programming.
- On this course, you’ll discover diffusion models, their working, and implementation.
- Understand what unconditioned diffusion models are.
- Improvements in text-to-image diffusion models.
- Training and deploying these models on Vertex AI – a totally managed ML platform by Google.
6. Encoder-Decoder Architecture
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Intermediate-level
~ 1 day (Complete the quiz/lab in your individual time)
Knowledge of Python programming and TensorFlow.
- Discover the important thing components of the encoder-decoder architecture.
- Understand how one can use the encoder-decoder architecture to coach a model and produce text from it.
- Features a lab walkthrough where you’ll code in TensorFlow, a preferred ML development platform to construct production-grade models.
7. Attention Mechanism
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Intermediate-level
~ 45 minutes
Knowledge of ML, DL, Natural Language Processing (NLP), Computer Vision (CV), and Python programming.
- Discover the concept of attention mechanism – a strong approach that allows language models to focus on particular input sequence segments with the intention to understand contextual information.
- Find out how it operates and its uses.
- Understand how the eye mechanism is applied to ML models.
8. Transformer Models & BERT Models
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Beginner-level
~ 45 minutes
Intermediate knowledge of ML, understanding of word embeddings and a focus mechanism, and experience with Python and TensorFlow.
- Learn in regards to the Transformer architecture and explore how a Bidirectional Encoder Representation from the Transformer (BERT) model is built using Transformers.
- Covers different NLP tasks for which a BERT model is used.
9. Create Image Captioning Models
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Intermediate-level
~ 1 day (Complete the quiz/lab in your individual time)
Knowledge of ML, DL, NLP, CV, and Python programming.
- Easy methods to discover the weather of a picture captioning model.
- Easy methods to construct and assess a model for image captioning.
- Easy methods to create your individual captioning models for photos and use them to create captions.
10. Introduction To Generative AI Studio
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Introductory-level
~ 1 day (Complete the quiz/lab in your individual time)
No
- Recognize the aim of Generative AI Studio, a Vertex AI product.
- The choices and properties of Generative AI Studio are also covered on this course.
- Accommodates a hands-on lab where you possibly can utilize this tool.
After completing these ten free courses, learners can have a comprehensive understanding of Generative AI and its practical applications. Learners can utilize their newly acquired knowledge to advance the sphere of Generative AI, constructing progressive products that may positively impact our society.
“” ― Hendrith Vanlon Smith Jr, CEO of Mayflower-Plymouth, in his book Business Essentials.
To maintain yourself updated about AI advancements, visit unite.ai.