Free Generative AI courses launched by Google in 2023

What is generative AI?

It is a type of artificial intelligence system that can generate something from scratch without any prior knowledge or experience. The resulting product can be anything from an artwork to a new set of algorithms, and it usually has some degree of novelty compared to existing solutions.

Google's free Generative AI courses will focus on developing these technologies as well as teaching students

Generative AI has the potential to push the boundaries of creativity and enable new ways of content creation.?

Generative AI refers to a class of artificial intelligence techniques and models that are designed to generate new content, such as images, text, music, or even video, that is similar to some existing data it has been trained on. It involves training a model on a large dataset and then using that model to generate new data that is original and coherent.

Applications of generative AI are wide-ranging. For example, in the field of computer vision, generative AI can be used to create new and realistic images or to enhance and modify existing images. In natural language processing, generative AI can be used to generate coherent and contextually relevant text or to create conversational agents. It has also found applications in music composition, video synthesis, and even in generating new drug molecules in the field of pharmaceuticals.

How and When Generative AI was developed?

Joseph Weizenbaum (8 January, 1923 to 5 March, 2008) was German-American computer scientist and a professor at MIT. He was known as father of modern artificial intelligence.?

In 1966, he published a comparatively simple program called ELIZA at MIT which performed Natural Language Processing(NLP)?

ELIZA was specifically designed to deceive users into perceiving it as a real human conversation partner by simulating a therapist's role. It would pose open-ended questions and provide follow-up responses, all with the intention of creating the illusion of engaging with a human.

After 1966 it was Ian goodfellow who developed the concept of generative adversarial networks a type of machine learning algorithm -- that generative AI could create convincingly authentic images, videos and audio of real people.

And on 30 Nov 2022, World was introduced to ChatGPT.

Google Launches Free Generative AI Courses?

The field of generative AI is generating significant buzz and is emerging as an exciting and promising path for those interested in building their AI/ML careers.

It offers a brand new avenue to consider and explore!

Google is offering seven free courses in generative AI domain?-

Introduction to Generative AI

This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.

Objectives of this course?

• Define Generative AI
• Explain how Generative AI works
• Describe Generative AI Model Types
• Describe Generative AI Applications

Introduction to large language models

This is an introductory level microlearning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance.

Objectives of this course

• Define Large Language Models (LLMs)
• Describe LLM Use Cases
• Explain Prompt Tuning
• Describe Google’s Gen AI Development tools

Attention Mechanism

This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering.

Objectives of this course

• Understand the concept of attention and how it works
• Learn how attention mechanism is applied to machine translation

Transformer Models and BERT Model

This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.

Objectives of this course

• Understand the main components of the Transformer architecture.
• Learn how a BERT model is built using Transformers.
• Use BERT to solve different natural language processing (NLP) tasks.

Introduction to Image Generation

This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.

Objectives of this course

• How diffusion models work
• Real use-cases for diffusion models
• Unconditioned diffusion models
• Advancements in diffusion models (text-to-image)

Create Image Captioning models

This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images

Objectives of this course?

• Understand the different components of an image captioning model.
• Learn how to train and evaluate an image captioning model.
• Create your own image captioning models.
• Use your image captioning models to generate captions for images.

Encoder- decoder Architecture

This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.

Objectives of this course

• Understand the main components of the encoder-decoder architecture.
• Learn how to train and generate text from a model by using the encoder-decoder architecture.
• Learn how to write your own encoder-decoder model in Keras.

Conclusion

Google recognizes that the world of AI can be complex and bewildering to many people. To bridge this knowledge gap, Google has introduced new courses and tools aimed at demystifying AI for the public. This effort not only helps in educating and empowering individuals but also serves as a means for Google to identify and attract skilled engineers and scientists who can contribute to their future AI initiatives and advancements.

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