From: 2weiEmu Date: Thu, 6 Nov 2025 23:46:18 +0000 (+0100) Subject: raw ml notes are here X-Git-Url: https://git.saalbach.dev/?a=commitdiff_plain;h=401e2a9f912dac39ad5896d11bad548da727f249;p=research-obsidian.git raw ml notes are here --- diff --git a/.obsidian/workspace.json b/.obsidian/workspace.json index 3df61f1..a49961d 100644 --- a/.obsidian/workspace.json +++ b/.obsidian/workspace.json @@ -177,17 +177,17 @@ }, "active": "3e78d839de43b5f0", "lastOpenFiles": [ - "Pasted image 20251106010304.png", - "Pasted image 20251106010018.png", - "Pasted image 20251106010007.png", - "Pasted image 20251106005956.png", - "Pasted image 20251106005919.png", - "Pasted image 20251106005851.png", - "Pasted image 20251106005803.png", - "Pasted image 20251106005742.png", - "Pasted image 20251106005719.png", - "Pasted image 20251106005655.png", - "Pasted image 20251106005628.png", + "Pasted image 20251106230015.png", + "Pasted image 20251106225651.png", + "Pasted image 20251106225645.png", + "Pasted 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image 20251106230015.png b/Pasted image 20251106230015.png new file mode 100644 index 0000000..5eeb64b Binary files /dev/null and b/Pasted image 20251106230015.png differ diff --git a/University/Machine Learning/Full Notes.md b/University/Machine Learning/Full Notes.md index 62f85b2..3a867ca 100644 --- a/University/Machine Learning/Full Notes.md +++ b/University/Machine Learning/Full Notes.md @@ -2186,4 +2186,133 @@ http://setosa.io/ev/principal-component-analysis/ http://peterbloem.nl/blog/pca -(there is something about Convolutional neural networks but it seems to be optional - I think I went to this last time actually but hey) \ No newline at end of file +(there is something about Convolutional neural networks but it seems to be optional - I think I went to this last time actually but hey) + +## Convolutional Neural Networks +introduction to deep learning + +feature extraction vs deep leearning +![[Pasted image 20251106130821.png]] + +end-to-end learning: end goal (output) used to learn feature extraction (input) + +![[Pasted image 20251106130852.png]] + +![[Pasted image 20251106130914.png]] + +![[Pasted image 20251106130935.png]] +![[Pasted image 20251106130951.png]] + + +![[Pasted image 20251106130959.png]] + +![[Pasted image 20251106131009.png]] + +![[Pasted image 20251106131033.png]] + +![[Pasted image 20251106131119.png]] +![[Pasted image 20251106131137.png]] + +![[Pasted image 20251106131147.png]] + + +![[Pasted image 20251106131201.png]] + +![[Pasted image 20251106131249.png]] +non-linearity in this case things like ReLU or such + +![[Pasted image 20251106131327.png]] + +## advanced techniques for CNNs + +![[Pasted image 20251106131351.png]] + + +![[Pasted image 20251106131407.png]] + +![[Pasted image 20251106131415.png]] +![[Pasted image 20251106131447.png]] +![[Pasted image 20251106131501.png]] + + + +![[Pasted image 20251106131514.png]] +![[Pasted image 20251106131535.png]] +![[Pasted image 20251106131549.png]] + +![[Pasted image 20251106131559.png]] + +Use a pre-trained model on a large dataset and 'adapt'iit to a task with a smaller dataset + +![[Pasted image 20251106131652.png]] +3. freeze initial convolutional layers and fine-tune the last layers +![[Pasted image 20251106131724.png]] + +## finetuning +![[Pasted image 20251106131735.png]] + +conclusion: +- images as input data +- CNN architecture: ResNet +- How to transfer learning to train the REsNet + +https://cs231n.github.io/transfer-learning/ + + +## things found during exercises: +![[Pasted image 20251106132145.png]] + + + +![[Pasted image 20251106142417.png]] + + +![[Pasted image 20251106142639.png]] + + + +Naive Bayes: missing feature values are: ignored! + + +![[Pasted image 20251106145927.png]] + +![[Pasted image 20251106150042.png]] +![[Pasted image 20251106150218.png]] + +![[Pasted image 20251106150412.png]] + + +![[Pasted image 20251106150522.png]] +(logistic regreession is linear, don't forget that - but you already kinda thought that right, after all the thing that goes in the sigmoid is linear) + + +TODO: what does it mean for something to be separable +![[Pasted image 20251106220110.png]] + + +logistic regression simply has the linear thing in hte sigmoid function +Model the class posterior probability $P(Y=1|X)$ using $\sigma(h(X))$ +![[Pasted image 20251106222245.png]] + +![[Pasted image 20251106222907.png]] + + +![[Pasted image 20251106223635.png]] + + +![[Pasted image 20251106224329.png]] +parametric = you make an assumption on the distribution of the data + +![[Pasted image 20251106224411.png]] + +![[Pasted image 20251106224445.png]] +![[Pasted image 20251106224648.png]] + +![[Pasted image 20251106225012.png]] + + +![[Pasted image 20251105154154.png]] +(don't forget kids - this is a logistic function, can be used as an activation function) + +![[Pasted image 20251106230015.png]] +