From: 2weiEmu Date: Mon, 9 Feb 2026 09:47:56 +0000 (+0100) Subject: more ml notes X-Git-Url: https://git.saalbach.dev/?a=commitdiff_plain;h=42a6d687c9dc85e3a42ab9eb523aeffed15b40c1;p=research-obsidian.git more ml notes --- diff --git a/.obsidian/workspace.json b/.obsidian/workspace.json index 5c3631d..afb540b 100644 --- a/.obsidian/workspace.json +++ b/.obsidian/workspace.json @@ -4,16 +4,16 @@ "type": "split", "children": [ { - "id": "63418e15dc850084", + "id": "226611ae14e85609", "type": "tabs", "children": [ { - "id": "04094233c3d02ad2", + "id": "3e78d839de43b5f0", "type": "leaf", "state": { "type": "markdown", "state": { - "file": "List of things to do.md", + "file": "4th and 5th Gen Fighters/Grippen and F-35 Deep Dive.md", "mode": "source", "source": false, "backlinks": true, @@ -28,10 +28,23 @@ } }, "icon": "lucide-file", - "title": "List of things to do" + "title": "Grippen and F-35 Deep Dive" + } + }, + { + "id": "c7759456ba31ed51", + "type": "leaf", + "state": { + "type": "release-notes", + "state": { + "currentVersion": "1.11.4" + }, + "icon": "lucide-book-up", + "title": "Release Notes 1.11.4" } } - ] + ], + "currentTab": 1 } ], "direction": "vertical" @@ -88,8 +101,7 @@ } ], "direction": "horizontal", - "width": 298, - "collapsed": true + "width": 300 }, "right": { "id": "52c8cd2985704b8e", @@ -175,52 +187,53 @@ "pdf-plus:PDF++: Toggle auto-paste": false } }, - "active": "04094233c3d02ad2", + "active": "c7759456ba31ed51", "lastOpenFiles": [ - "Nebulous Command/missiles-ranges.py~", - "Nebulous Command/4913", - "Nebulous Command/missile-simulator.py~", - "Nebulous Command/data.py~", - "List of things to do.md", - "Nebulous Command/__pycache__/data.cpython-314.pyc", + "University/Algorithm Design/Full Notes - Run 2.md", + "Robert's Opsec Policy/Robert's Opsec Policy.md", + "Robert's Opsec Policy", + "Poems that I like/Poems that I like.md", + "Poems that I like", + "Pasted image 20251207164533.png", "Nebulous Command/missiles-ranges.py", - "Nebulous Command/calc.py~", + "Nebulous Command/missile-simulator.py", "Nebulous Command/data.py", "Nebulous Command/calc.py", - "Nebulous Command/Notes on Mechanics.md~", + "Nebulous Command/__pycache__/data.cpython-314.pyc", + "Nebulous Command/__pycache__/calc.cpython-314.pyc", + "Nebulous Command/__pycache__", "Nebulous Command/Notes on Mechanics.md", - 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"Daily/27-01-2025.md", - "University/Algorithm Design/Full Notes - Run 2.md", - "Pasted image 20251207164533.png", - "Daily/16-01-2025.md", "Pasted image 20251106230015.png", "Pasted image 20251106225651.png", "Pasted image 20251106225645.png", - "Pasted image 20251106225012.png", - "Pasted image 20251106224648.png", - "Pasted image 20251106224445.png", - "Pasted image 20251106224411.png", - "Pasted image 20251106224329.png", - "Pasted image 20251106223635.png" + "Pasted image 20251106225012.png" ] } \ No newline at end of file diff --git a/University/Machine Learning/Full Notes.md b/University/Machine Learning/Full Notes.md index 3a867ca..2019385 100644 --- a/University/Machine Learning/Full Notes.md +++ b/University/Machine Learning/Full Notes.md @@ -1095,6 +1095,8 @@ for the points on hte margin we have thus know that their distance to the decisi So the margin is: $$\frac{2}{||\mathbf{w}||}$$ TODO: please interpret this for my monkey brain because right now it's just math in my head +> ok so here is what I seem to understand so far the SVM itself is a classifer based on the nearest points, and at the classifier the linear formula is zero, and on one side it moves in the + class and on the other side ot the - class (ok sounds fair enough so far) -> and that means the the "w" vector always points in the direction of the boundary. + we wanted to maximise the margin so:$$\max_{\mathbf{w},w_0}\frac{2}{||\mathbf{w}||}$$ which is the same as minimising: $$\min_{\mathbf{w},w_0}\frac{1}{2}||\mathbf{w}||^2$$ ![[Pasted image 20251105165950.png]] @@ -1447,7 +1449,7 @@ Missclassification: $$1-\max_c p_c$$ Entropy: -$$-\sum_c p_c\log(p_c)$$ +$$-\sum_c p_c\log_2(p_c)$$ Gini Index: $$\sum_c p_c(1-p_c)$$ @@ -2092,7 +2094,7 @@ typically: data sets are *high-dimensional*: each instance is described by many - Constriant: $w^Tw=1\rightarrow||w||^2=1$ - requires w (direction of projection) to be unit vector (length is 1); solves scaling problem and guarantees unique solution -## zero-mean data +## zero-mean (and unit variance?) data - recall the definition of variance ![[Pasted image 20251106004843.png]] - for PCA shift your data to zero-mean - for each feature, calcualte the mean, subtract the mean from each data point in that feature @@ -2105,7 +2107,7 @@ typically: data sets are *high-dimensional*: each instance is described by many - our objective is to max. variance $$\max_{||w||^2=1}\text{var}(w^TX)$$ - assuming zero-mean data: $$\text{var}(w^TX)=\frac{1}{n}(w^TX)(w^TX)^T=\frac{1}{n}w^TXX^Tw=w^TMw$$ ## lagrange multiplier -- introduce a language multiplier $\lambda$ to incorporate the constraint into our optimization $$L(w,\lambda)=w^TMw-\lambda(w^Tw-1)$$ +- introduce a lagrange multiplier $\lambda$ to incorporate the constraint into our optimization $$L(w,\lambda)=w^TMw-\lambda(w^Tw-1)$$ ![[Pasted image 20251106005249.png]] - it penalizes any deviation from the constraint - if the constraint is violated (i.e. $w^Tw\neq 1$)it will either increase or decrease the value of the Lagrangian