Welcome!
This collection of notebooks documents a series of experiments in machine learning, starting from basic classical machine learning techniques and progressively exploring more complex deep learning approaches.
The primary goal here wasn’t necessarily to achieve state of the art results on the first try, but rather to understand the how and why behind different methodologies.
Each notebook attempts to build upon the last, dissecting concepts, wrestling with implementations, and documenting the often messy, iterative, and occasionally surprising process of trying to make these algorithms actually work. Think of it as a live learning journal, complete with successes, a few spectacular nosedives, and the kind of insights that only come from banging your head against the code until it makes sense.
This is very much a work in progress. The path is ongoing, new topics will be explored, and the spiral into machine learning will undoubtedly continue. Hopefully, sharing this journey – the frustrations, the breakthroughs, and the relentless pursuit of understanding – might be useful, or at least relatable, to others venturing into this complex and captivating field.