Embark on a journey into Generative AI with Google’s 10 free courses. From fundamentals to advanced models, discover the secrets of large language models, responsible AI, image generation, encoder-decoder architecture, attention mechanisms, and transformer models. Cap it off with hands-on experience in Generative AI Studio. Elevate your skills with these comprehensive resources.
10 Free Generative AI Courses by Google for Your Learning
Dive into the world of Generative AI with Google’s 10 Free Courses, offering an opportunity for learning and exploration. Uncover the principles and techniques behind AI creation at no cost, allowing enthusiasts and learners alike to enhance their skills. From foundational concepts to advanced applications, these courses provide an invaluable resource for those seeking to understand and harness the power of Generative AI in today’s dynamic technological landscape.
Generative AI has the ability to create text, images, or various media forms in response to user inputs. It excels in generating fresh content, automating repetitive tasks, handling personalised data, and performing a range of diverse functions.
List of 10 Free Generative AI Courses by Google
1. Generative AI Basics
This course gives you a starting point for understanding Generative AI. If you’re new to it, this is where you should begin. You’ll discover what sets Generative AI apart from other machine learning methods.
2. Understanding Large Language Model
Explore the world of large language models like ChatGPT and Bard. Learn how they’re constructed, their purpose, and how to fine-tune them for better performance.
3. Responsible AI Overview
Dive into the realm of responsible AI amid recent concerns. This course explains how Google implements responsible AI in its products, covering the 7 AI Principles, social responsibility, accountability, and privacy design.
4. Mastering Generative AI Fundamentals
After completing the first three courses, this quiz-based course tests your knowledge. It’s suitable for both beginners and those looking to solidify their understanding.
5. Creating Images with Generative AI
Delve into the art of generating images using stable diffusion. Learn about diffusion models, machine learning, deep learning, and convolutional neural networks.
6. Demystifying Encoder-Decoder Architecture
Understand the powerful encoder-decoder architecture used in sequence-to-sequence tasks. The course includes a hands-on lab where you code a basic implementation for a specific task.
7. Exploring Attention Mechanism
Uncover the secrets of the attention mechanism, a technique allowing neural networks to focus on specific parts of input sequences. Prior knowledge in machine learning, deep learning, natural language processing, or Python is recommended.
8. Decoding Transformer Models and BERT
Dive deeper into advanced terminology. Explore transformer models and Bidirectional Encoder Representations from Transformers (BERT). Learn about self-attention mechanisms and their use in the BERT model, along with tasks like text classification.
9. Building Image Captioning Models
Learn to craft image captioning models through deep learning. Break down components like the encoder and decoder, proceed to train and evaluate the model, and create your own models capable of generating image captions.
10. Introduction to Generative AI Studio
Explore the Generative AI Studio through walk-through demos. This tool helps prototype and customise generative AI models for application use. The course includes a hands-on lab and a quiz to reinforce your understanding.
![[2401.09074] Code Simulation Challenges for Large Language Models](https://aigumbo.com/wp-content/uploads/2023/12/arxiv-logo-fb-235x190.png "[2401.09074] Code Simulation Challenges for Large Language Models"){kind=logo}
