Machine Learning is to me the transcendentalization of my combined passion toward Software Engineering and Data Analytics. Machine Learning (ML) is a specific subset of AI focused on enabling machines to learn from data.

My first ASR model fine-tuning hit really hard at an error by gradient checkpoint which draw my attention to this topic. Gradient Checkpointing (also known as activation checkpointing) is a memory-optimization strategy used during the training of deep neural networks. It allows you to train significantly larger models (or use larger batch sizes) on limited GPU memory by trading computation time for memory space.

Building AI Agent from first principles with a durable understanding that frameworks alone cannot, This post structures a learning path that separates the core theory from the "from scratch" implementation, using resources that are independent of major agent frameworks, such as LlamaIndex.

I was originally studying LLM on Hugging Face (https://huggingface.co/learn/llm-course) and notice that transformer occupied significant portion of their learning materials so I decided to look into it deeply

Why would AI work? Certainly technological advancements and market conditions have their contributions. But it is the philosophical justification presented by one of the greatest philosophers, David Hume, that has made it inevitable which this post shows by examining and linking his most phenomenon work - "An Enquiry Concerning Human Understanding"

A fascination with things from the past, like ancient languages, is a common and deeply rooted human interest. There are a number of reasons for this, ranging from the intellectual and practical to the emotional and psychological.

If the Universal Approximation Theorem expresses the unprecedented power of AI, the Halting Problem poses the opposite - its computational limit.