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College notes Deep Learning deel 1 (XM_0083), Master VU AI $4.74   Add to cart
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College notes Deep Learning deel 1 (XM_0083), Master VU AI

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  • Course
  • Deep learning (XM_0083)
  • Institution
  • Vrije Universiteit Amsterdam (VU)

Those are the notes I made during the whole course, it is not a summary! As long as you understand this document you do not need to watch any lecture. With those notes I have passed the course with a 7.5.

Last document update: 2 year ago

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Document information

  • February 23, 2022
  • February 23, 2022
  • 98
  • 2021/2022
  • Class notes
  • Jakub tomczak
  • 1-8

Subjects

  • deeplearning
  • deep learning
  • logistic regression
  • fnn
  • cnn
  • backpropagation
  • artificial intelligence
  • kunstmatige intelligentie
  • neural networks
  • rnn
  • lstm
  • autoregressive models
  • ai
  • convolutional neural networks

Written for

  • Vrije Universiteit Amsterdam (VU)
  • Artificial Intelligence
  • Deep learning (XM_0083)
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1  review

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By: meessoek • 1 year ago

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Deep Learning
Created @October 30, 2021 4:35 PM

Class S2

Type

Materials




Lecture 1
Introduction
Foundations of Deep Learning

Probabilistic Learning

e.g. Toss a coin




Bernoulli distribution




Deep Learning 1

, likelihood function in this case: product, because independent and identical from same coin, one probability




Objective function: .. all identically distributed so all follow Bernoulli




finding best x:




formulating it as general approach, probabilistic learning (likelihood-based) —> always done like this
p(y|x) is probability distribution



Deep Learning 2

, probabilistic values bernoulli can be used, real or continues values can be done with gaussian or
others

identically and independently —> iid, else if sequential you are dependent of past so a method with
sequential (generative regressive models, rnn)

Logistic Regression

= model that allows us to classify objects
linear dependency: when the result of coin depends on the hand velocity while coining, or the mail calling
spam depends on the words. Its probabilistic so we need sigmoid




Deep Learning 3

, gradient = vector of derivatives




note for image: derivative with respect to theta applying the chain rule

we get the difference between our prediction (sigmoid, y?) and the label x which comes into the gradient. If
our model is correct, the weights θ will not change, else we will follow the direction of x (-1)

next image not important to know in very detail:




Deep Learning 4

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