Adventurous AI Journey Ahead

  I have asked AI Collaborator to guess why I choose “Miniforge and VIM” for learning Python and AI by projects. This is its reply: It is because you want to build a rock-solid foundation by forcing yourself to understand how your code works under the hood, rather than relying on ‘magic’. Here is a guess at the psychology and strategy behind your setup: 1. You want to understand dependencies, not just hide them. Python environments, especially for AI (which relies on heavy libraries like PyTorch or TensorFlow), are notorious for dependency hell. The easy path: Anaconda or pipenv. Your path: Miniforge. You have chosen this because it gives you raw control. It forces you to learn how conda-forge packages things, how to isolate your AI projects using conda activate, and how to resolve version conflicts manually so you actually understand what your system is running. 2. You are prioritizing absolute focus and speed. AI development involves looking at a lot of text, code, and document...

MIT 6.S191 - Introduction to Deep Learning : Lecture 1, New 2025 Edition, Foundations of Deep Learning. Lecturer: Alexander Amini. Lectures, slides, and lab materials . I sat in for the interesting introduction of first lecture (and would continue this lecture later). Within the first few minutes, the lecturer demonstrated the power of deep learning and he motivated me to continue with this course ( I really hope I have the world of time.)

Visit: What is Deep Learning? (Source: Mckinsey.com) An interesting write-out as it shares the relationship between deep learning and generative AI. It also introduces the kinds of neural networks are used in deep learning.

Deep learning is a type of machine learning that uses multi-layered artificial neural networks to automatically learn patterns and features from vast amounts of data for complex tasks like image recognition, speech processing, and language translation It offers high accuracy by processing information in stages. Unlike traditional machine learning, it learns features directly from data without needing human-engineered feature extraction, making it powerful for unstructured data and complex problems. More on Deep Learning and Study Deep Learning  

MIT 6.100L Introduction to CS and Programming using Python, Fall 2022, by Prof Ana Bell. View full course (Source: MIT.edu , youtube.com) Lecture 1: Introduction to CS and Programming Using Python (1h video) I am starting to go through these videos. Generally, the Prof not only introduces the terms and concepts, she also helps me to remember them. Basically, I am motivated to continue attending her lectures and to find out more on Python. Good to meet such a wonderful professor and many thanks to MIT. Here’s a summary of the first lecture (but it’s better for you to attend). The Prof discusses the idea of an object in Python. Every object in Python has a specific type. The type tells Python the type of things we are allow to do with the object. Once, we have an object, we can assign it to a variable. These variables, basically bind names to objects. The = sign is an assignment, for example, var = type (5*4). The following have been discussed: Expressions involve objects and operatio...

  Do You Still Need to Learn Python in the Age of AI? (Source: MIT.edu – youtube.com) Watch this 3min video if you are curious to know. Also, read “Don’t blindly trustwhat AI tells you, says Google’s Sundar Pichai” (Source: bbc.com ) Personally, I perceive humans will have to clean up the “AI mess” in due course, just like, what we are doing now for “fossil fuels mess”. So it pays to learn the Python. More on Learning Python

Visit: What is Machine Learning (Source: Stanford.edu) This video (1.5min) explains in simple terms what machine learning is and how is it related to artificial intelligence. Visit: What is machine learning? (Source: IBM.com)

  Machine learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn and improve from data without being explicitly programmed . Instead of following pre-written instructions, ML algorithms find patterns in data, make predictions or decisions, and get better at the task over time as they are exposed to more information.   More on ML

Visit: AI Glossary/ Dictionary (Source: MIT.edu) This is a valuable set of AI vocabulary to start off with. It not only defines the AI terms, it also explains their importance. It is good to keep this list handy. Learning the basic AI vocabulary can help us gain a better understanding of the complex field of AI. Also, developing AI literacy will enable us keep up with its advancement, appreciate its opportunities and challenges. Thus, putting us in a better position to balance any innovation with ethical considerations. Ultimately, we want AI to impact our society positively. Here’s another helpful glossary on basic GenAI Visit: Glossary of Term: Generative AI Basics (Source: MIT.edu)  

  Visit: What is AI (Source: IBM.com) This site provides a complete guide to AI fundamentals. It relatively easy to go through its materials on, for example, machine learning, deep learning, generative AI, AI agents and agentic AI. In addition, it gives an overview of AI history, its benefits and use cases, AI ethics and governance. Overall, this is a great resource for me to start off.

Artificial intelligence (AI) is technology (applied knowledge) that enables machines to simulate human learning, comprehension / reasoning, problem solving, decision making, creativity and autonomy. AI enables us to build smart machines that can perceive, reason, and act to achieve defined outcomes. Also, by incorporating learning, they can adapt and improve their performance without explicitly reprogramming. More on AI