[{"data":1,"prerenderedAt":234},["ShallowReactive",2],{"lang-switch-post-\u002Fen\u002Fplaylists":3,"playlists-index-en":4},null,[5,40,96,165,193],{"id":6,"title":7,"body":8,"cover":3,"description":30,"extension":31,"meta":32,"navigation":33,"order":34,"path":35,"seo":36,"status":37,"stem":38,"__hash__":39},"playlists\u002Fen\u002Fplaylists\u002Fmachine-learning-specialization\u002Findex.md","Machine Learning Specialization (Andrew Ng)",{"type":9,"value":10,"toc":26},"minimark",[11,20,23],[12,13,14,15,19],"p",{},"This playlist is my public study notebook while going through the ",[16,17,18],"strong",{},"Machine Learning Specialization",", by Andrew Ng (DeepLearning.AI \u002F Stanford), probably the most recommended course for anyone getting started in ML.",[12,21,22],{},"The idea here isn't just \"solve the notebook and move on.\" Every lab in the course becomes a post where I retell what I understood, with an everyday-life metaphor, an example, and of course, the correct technical term, because you'll need it when you go looking for more on the topic later.",[12,24,25],{},"We start at the start: representing the simplest model there is, linear regression with one variable.",{"title":27,"searchDepth":28,"depth":28,"links":29},"",2,[],"My step-by-step notes going through Andrew Ng's Machine Learning Specialization (DeepLearning.AI \u002F Stanford), course by course, lab by lab.","md",{},true,1,"\u002Fen\u002Fplaylists\u002Fmachine-learning-specialization",{"title":7,"description":30},"published","en\u002Fplaylists\u002Fmachine-learning-specialization\u002Findex","doASMuy-kKTfF67w2hQII9iS08pq0gbnf-FwE2AmImQ",{"id":41,"title":42,"body":43,"cover":3,"description":90,"extension":31,"meta":91,"navigation":33,"order":28,"path":92,"seo":93,"status":37,"stem":94,"__hash__":95},"playlists\u002Fen\u002Fplaylists\u002Fpattern-recognition\u002Findex.md","Pattern Recognition",{"type":9,"value":44,"toc":88},[45,58,70,80],[12,46,47,48,51,52,57],{},"This playlist is my study notebook from the Pattern Recognition course I took, taught by ",[16,49,50],{},"Dr. Francisco Boldt",". The guy is excellent, he genuinely codes the models by hand, live, in class, no pre-baked formula slides, and he's teaching me ",[53,54,56],"a",{"href":55},"\u002Fen\u002Fplaylists\u002Fneural-networks","neural networks now too",". If you landed here coming from one of his classes, you already know what I mean.",[12,59,60,61,64,65,69],{},"Unlike the ",[53,62,63],{"href":35},"Andrew Ng specialization playlist",", the lecture notebooks here are a lot leaner: barely any markdown cells, it's the professor live-coding and everyone following along. So the work of digging into the \"why\" behind each line of code is heavier here, and for that I lean on ",[66,67,68],"em",{},"Pattern Recognition and Machine Learning",", by Christopher Bishop (2006), pretty much a bible in the field, as the theoretical reference.",[12,71,72,73,79],{},"The notebooks come from the course repository, ",[53,74,78],{"href":75,"rel":76},"https:\u002F\u002Fgithub.com\u002Fpablobelmiro\u002Faulasml\u002Ftree\u002F2026-1",[77],"nofollow","pablobelmiro\u002Faulasml",", a fork of Dr. Boldt's own repository, where he publishes each lecture's code. In this playlist's posts, whoever \"wrote\" the code is always him, the professor; the foundational explanation, with the metaphor, the slightly-off analogy, and the buddy-sitting-next-to-you tone, that part is mine.",[12,81,82,83,87],{},"Every lecture becomes a post here. We start at the start: the same ",[53,84,86],{"href":85},"\u002Fen\u002Fplaylists\u002Fmachine-learning-specialization\u002Flab02-model-representation","linear regression problem from the other playlist",", except this time the code is being written live on the whiteboard.",{"title":27,"searchDepth":28,"depth":28,"links":89},[],"My notes from my Pattern Recognition course, lecture by lecture, with Bishop's book as the theoretical backbone.",{},"\u002Fen\u002Fplaylists\u002Fpattern-recognition",{"title":42,"description":90},"en\u002Fplaylists\u002Fpattern-recognition\u002Findex","y_rvvoQ-pc7yj5eUqMw_dVQddrlu3I5aEN4sROdZOdk",{"id":97,"title":98,"body":99,"cover":3,"description":159,"extension":31,"meta":160,"navigation":33,"order":161,"path":55,"seo":162,"status":37,"stem":163,"__hash__":164},"playlists\u002Fen\u002Fplaylists\u002Fneural-networks\u002Findex.md","Neural Networks",{"type":9,"value":100,"toc":157},[101,110,127,141],[12,102,103,104,106,107,109],{},"Second playlist with ",[16,105,50],{}," (the first was ",[53,108,42],{"href":92},"), now in his Neural Networks course. Same style as always: code by hand, live, in class, no pre-baked formula slides. If you've already read the previous playlist, you know exactly what to expect.",[12,111,112,113,116,117,121,122,126],{},"The reference book changes: here I use ",[66,114,115],{},"Neural Networks and Deep Learning: A Textbook",", by Charu Aggarwal (2018), to play the role Bishop played in the previous playlist, filling in the foundation the notebook only shows in code. A lot of the material also connects straight back to what I've already covered: ",[53,118,120],{"href":119},"\u002Fen\u002Fplaylists\u002Fpattern-recognition\u002Flinear-regression-estimator","the delta rule and linear regression already showed up in Pattern Recognition",", and ",[53,123,125],{"href":124},"\u002Fen\u002Fplaylists\u002Fmachine-learning-specialization\u002Floss-functions","loss functions already showed up in Andrew Ng's specialization",", so whenever it fits I'll pull those threads instead of reteaching from scratch.",[12,128,72,129,134,135,140],{},[53,130,133],{"href":131,"rel":132},"https:\u002F\u002Fgithub.com\u002Fpablobelmiro\u002Faulasann",[77],"pablobelmiro\u002Faulasann",", a fork of Dr. Boldt's own repository, ",[53,136,139],{"href":137,"rel":138},"https:\u002F\u002Fgithub.com\u002Ffboldt\u002Faulasann",[77],"fboldt\u002Faulasann",", where he publishes each lecture's code. Same convention as the previous playlist: whoever \"wrote\" the code is always him, the professor; the foundational explanation is mine.",[12,142,143,144,147,148,151,152,156],{},"One important detail this time: this course is being taught ",[16,145,146],{},"right now",", live, and the repository only has the beginning of the course as of this moment (perceptron, Adaline, cost functions, and a cliffhanger right at the edge of what a single neuron can solve). Unlike the Pattern Recognition playlist, which I only started once the whole course had already ended, this one is a ",[16,149,150],{},"living playlist",": it grows every time the professor publishes a new lecture, and I come back to keep going. We start at the very beginning: ",[53,153,155],{"href":154},"\u002Fen\u002Fplaylists\u002Fneural-networks\u002Fmcculloch-pitts-perceptron","the simplest neuron there is",".",{"title":27,"searchDepth":28,"depth":28,"links":158},[],"My notes from my Neural Networks course, lecture by lecture, with Aggarwal's book as the theoretical backbone. A living playlist, growing along with the course.",{},3,{"title":98,"description":159},"en\u002Fplaylists\u002Fneural-networks\u002Findex","fD14SmK9Haa_ztrp627bgiWBVDETZgSpdrWnwlC_LnU",{"id":166,"title":167,"body":168,"cover":3,"description":186,"extension":31,"meta":187,"navigation":33,"order":188,"path":189,"seo":190,"status":37,"stem":191,"__hash__":192},"playlists\u002Fen\u002Fplaylists\u002Fpapers\u002Findex.md","Papers",{"type":9,"value":169,"toc":184},[170,173,176],[12,171,172],{},"The other three playlists here are real study: lecture notebook, textbook on the side, every line of code picked apart until it hurts. This one is a different vibe. Every once in a while I read a paper or survey that gets me excited, usually skimmed diagonally, not read page by page with a magnifying glass, and I wanted a place to talk about it without all the ceremony of a lecture.",[12,174,175],{},"That's what this playlist is: I tell you what I found coolest about the paper, with a crooked metaphor, some joking around here and there, and whenever it fits, some simple Python code and a chart just to give you that view. Don't expect notebook-level rigor or section-by-section coverage, the goal here is a straight conversation about an interesting idea, not an academic summary.",[12,177,178,179,183],{},"First in the series is about a topic there's no escaping these days: ",[53,180,182],{"href":181},"\u002Fen\u002Fplaylists\u002Fpapers\u002Fagentic-reasoning-for-large-language-models","agentic reasoning in language models",", a giant survey trying to organize everything that became trendy to call an \"AI agent\".",{"title":27,"searchDepth":28,"depth":28,"links":185},[],"A diagonal read of articles and surveys I found cool, with no pretense of turning into a lecture. Simple code, a chart to give you a view, and the same conversation as always with you.",{},4,"\u002Fen\u002Fplaylists\u002Fpapers",{"title":167,"description":186},"en\u002Fplaylists\u002Fpapers\u002Findex","TwtxQvLC6nBRqD_S4n46i-MnkOFuDQKRx5oRX-hKPTo",{"id":194,"title":195,"body":196,"cover":3,"description":227,"extension":31,"meta":228,"navigation":33,"order":229,"path":230,"seo":231,"status":37,"stem":232,"__hash__":233},"playlists\u002Fen\u002Fplaylists\u002Fhow-to-build-and-finetune-slm\u002Findex.md","Small Language Models",{"type":9,"value":197,"toc":225},[198,209,222],[12,199,200,201,208],{},"This playlist comes from a book, not a live class or a course notebook: ",[53,202,205],{"href":203,"rel":204},"https:\u002F\u002Fleanpub.com\u002Fhowtobuildandfine-tuneasmalllanguagemodel",[77],[66,206,207],{},"How to Build and Fine-Tune a Small Language Model",", by J. Paul Liu. It's a lean book, more of a practical project script with code than a theory brick, and that's a compliment: every chapter comes with a dollar cost estimate, a training time, a minimum hardware spec attached. Seeing an ML book tell you \"this costs 50 dollars and takes an hour\" before throwing you into a sea of math is rare, and I wanted to pull that thread with you.",[12,210,211,212,216,217,221],{},"The book's full plan is ambitious, 12 chapters going from \"why train your own model\" all the way to production deployment with ethics and everything. For now I'm covering the first two: ",[53,213,215],{"href":214},"\u002Fen\u002Fplaylists\u002Fhow-to-build-and-finetune-slm\u002Fsmall-vs-giant-language-models","Chapter 1",", the case for why a small specialized model beats a generalist giant in plenty of real scenarios, and ",[53,218,220],{"href":219},"\u002Fen\u002Fplaylists\u002Fhow-to-build-and-finetune-slm\u002Fbuilding-gpt-from-scratch-dom-casmurro","Chapter 2",", which builds a character-level GPT from scratch, the same classic exercise from Andrej Karpathy's \"Let's Build GPT\" video (the book credits this openly). If more posts on the following chapters happen, I'll come back here, but for now that's it.",[12,223,224],{},"A funny detail before we start: the book's table of contents opens with a section called \"AI Use Disclaimer\". So the author used AI to help write a book teaching you to build AI, and I use uncle Claude to write this blog about the book. It's AI all the way down, but at least nobody here is pretending otherwise.",{"title":27,"searchDepth":28,"depth":28,"links":226},[],"Chapter by chapter through \"How to Build and Fine-Tune a Small Language Model\", by J. Paul Liu. Why train your own model instead of outsourcing everything to an API, and how to build a GPT from scratch until it writes like a 19th-century Brazilian novelist.",{},5,"\u002Fen\u002Fplaylists\u002Fhow-to-build-and-finetune-slm",{"title":195,"description":227},"en\u002Fplaylists\u002Fhow-to-build-and-finetune-slm\u002Findex","eTa95D4OCbBPJ0M1Phqi3IbxK0pTLR9Kxy507jmCikc",1787605213199]