[{"data":1,"prerenderedAt":193},["ShallowReactive",2],{"lang-switch-post-\u002Fen\u002Fplaylists":3,"playlists-index-en":4},null,[5,40,96,165],{"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",1787338982605]