Machine Learning Lecture 5 Bayesian Learning G53MLE | Machine Learning | Dr Guoping Qiu1.
Mathematics for Machine Learning - Henrik Bachmann · educba.com Supervised Learning Game Al Skill...
Transcript of Mathematics for Machine Learning - Henrik Bachmann · educba.com Supervised Learning Game Al Skill...
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Mathematics for Machine LearningSpecial Mathematics LectureNagoya University, Fall 2020
https://www.henrikbachmann.com/mml_2020.html
Lecture 11: Neural Networks I
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Machine learning overview
Remaining plan:• Introduce (simple) Neural Networks (Today)• Understand how to train them (Next time)• Consider more complicated NN.• Use finished implementations, e.g. Tensorflow.
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Nice to watch
https://www.3blue1brown.com/neural-networks
https://nnfs.io/
3blue1brown
Neural Networks from Scratch
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Neural Networks
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Recall: Logistic regression
Logistic regression
Hypothesis:
Example: Binary classifier (Pass an exam Yes/No, Spam email Yes/no)
We learned the correct parameters by maximizing the log-likelihood (by using gradient ascent)
Or mizimizing the negative of the log-likelihood (= cost function) (by using gradient descent)
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Rewriting logistic regression as a neural network
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Motivation: Image classification
Goal: Check if a picture contains a cat
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Motivation: Image classification
Goal: Check if a picture contains a cat or dog
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Motivation: Image classification. More layers
Goal: Check if a picture contains a cat or dog
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(rough) Notation
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Content of a layer
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Content of a layer
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Example of activation functionsThere are several common activiation functions.
https://en.wikipedia.org/wiki/Activation_function
Question: Why not use the identity map as an activation function?
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Example of NNLet us consider a NN with 4 layers (2 hidden):