Google’s Free Learning Courses on Generative AI

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Topic: Google’s Free Learning Courses on Generative AI

Table Of Contents

  • Introduction
  • Google Just Launched Free Learning Courses For Generative AI
  • Summary
  • References

Introduction

In this post we discuss about some Google’s Free Learning Courses on Generative AI, which is just offered by the Google.

Generative Artificial Intelligence (AI) has gained significant attention in recent years due to its ability to create new and original content. To help individuals delve into the fascinating world of Generative AI, Google has introduced a series of free learning courses. This blog post provides a summary of Google’s offerings, highlighting the key concepts and skills covered in these courses.

Google’s Free Learning Courses on Generative AI

This are the free courses offered by the google and all are the related to generative ai.

1. 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 us to develop our own Gen AI apps.This course is estimated to take approximately 45 minutes to complete.

2. 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 we can use prompt tuning to enhance LLM performance. It also covers Google tools to help us develop our own Gen AI apps. This course is estimated to take approximately 45 minutes to complete.

3. Introduction to Responsible AI

  • This is an introductory-level microlearning course aimed at explaining what responsible AI is, why it’s important, and how Google implements responsible AI in their products. It also introduces Google’s 7 AI principles.

4. Generative AI Fundamentals

5. 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 us to the theory behind diffusion models and how to train and deploy them on Vertex AI.

6. 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.
  • We learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, we will code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.

7. Attention Mechanism

  • This course will introduce us to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence.
  • We 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.

8. Transformer Models and BERT Model

  • This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model.
  • We 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. We also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.

9. Create Image Captioning Models

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

10. Introduction to Generative AI Studio

  • This course introduces Generative AI Studio, a product on Vertex AI, that helps us prototype and customize generative AI models so we can use their capabilities in our applications. In this course, we learn what Generative AI Studio is, its features and options, and how to use it by walking through demos of the product. In the end, we will have a hands-on lab to apply what you learned and a quiz to test your knowledge.

Summary

These courses empower learners to gain a solid understanding of Generative AI and its potential applications.

References

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