Digital sailing

NodeJS, Flask, Angular, React are not new to me. Given the researchers' recommendation to train students on modern industrial processes, and my aim to develop a more user-friendly UI for AI testing, it's prudent for me to refresh my skills in certain familiar areas that I haven't utilized recently. Setting up a development environment is a key step in this process.  One more step to run TypeScript without automation: Initialize: tsc -init  Compile .ts to .js: tsc source_file.ts Reference: TypeScript:  https://www.typescriptlang.org/   JavaScript:  https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference

 course: Generative AI with Large Language Models This course is presented by Andrew Ng as the introductory speaker and mainly consists of lectures by AWS engineers who explain various current theories and three practical courses. Week 1: introduction of LLMs and project lifecycle  The course introduces the architecture of Large Language Models (LLMs), their conceptual framework, and methods of expansion. It includes practical sessions demonstrating how to load AI and use extension datasets. The lectures cover the initial pre-training phase of AI and discuss the impact of domains on both the overall system and specialized aspects. Week 2: Fine-Tuning The course explains how AI, after initial training, can be further expanded to enhance areas required by users. It introduces examples of Fine-Tuning and includes a practical session demonstrating the extension training and integration of AI. Week 3: Reinforcement learning from human feedback (RLHF)  The course discusses the social respons

Conclusion:  Step 1:  find the model path: ls ~/.cache  Step 2:  Copy the entire folder to Google Drive:  Step 3:  Set model path to the subfolder under snapshot: My Story: I initially began exploring Generative AI (GAI) and Google Colab through Stable Diffusion. In the past, as I mainly wrote server services and console applications, I was less familiar with data science modes like R and Jupyter that can maintain a paused state. I didn't quite understand the heavy burden on Colab of creating a temporary Stable Diffusion WebUI with .ipynb, as suggested by popular guides. I just found it troublesome that connections often took a long time and then dropped, requiring a restart. Recently, while testing new versions of the Stable Diffusion model, and facing challenges due to Colab's policies making various versions of WebUI difficult to run successfully, I started researching how to write my own test programs in Colab. Eventually, I understood that Colab is essentially a VM, capabl

Course: Generative AI for Everyone The course "Generative AI for Everyone" discusses the design principles, practical applications, and potential societal impacts of Generative AI (GAI). It shares a similar structure with the "AI for Everyone" course, highlighting the differences between GAI and traditional AI, as well as between general-purpose AI and Artificial General Intelligence (AGI). Week 1: What is Generative AI? Generative AI (GAI) is a type of AI that predicts the next step based on existing content and uses the results as a reference for future iterations. In the realm of imagery, it involves identifying and clarifying images from noise. A key characteristic of GAI is that smaller models have limited growth potential and don't improve significantly with more data, whereas larger models have greater potential for development. In terms of accuracy, GAI is not as effective as Web Search. GAI is better suited for simple interactions, summarizing long arti