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b201
courses
b3b33vir
lectures
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Lectures
date
week
topic
slides+videos+codes
21.09.2020
1
Lec 1:
Regression/Classification as MLE:
derivation of L2-loss, cross-entropy loss, logistic loss
Lec 2:
Neural networks:
Fully-connected layer + computational graph + backpropagation
lec_1_video
(incorrect audio sync!)
vir_outline.pdf
mle_01_regression.pdf
mle_regression.py
lec2_internal_viewer
lec_2_video
mle_02_classification.pdf
mle_linear_classifier.py
28.9.2020
2
State holidays
05.10.2020
3
Lec 3:
ConvNet
: convolutional layer + backpropagation
lec3_internal_viewer
lec3.video
neural_nets.pdf
,
convnets.pdf
convolution_codes.zip
12.10.2020
4
Lec 4:
Training:
SGD, momentum, convergence rate, Adagrad, RMSProp, AdamOptimizer, diminishing/exploding gradient, oscilation
lec4_internal_viewer
lec4_video
training.pdf (corrected slides)
19.10.2020
5
Lec 5:
Layers:
Convolution, Activation functions, Batch-Instance Norm, MaxPooling, Losses + backpropagation
lec5_internal_viewer
,
layers.pdf (corrected)
26.10.2020
6
Lec 6:
Architectures I:
classification (ResNet), segmentation (DeepLab)
Test T1
lec6_internal_viewer
architectures_i.pdf
02.11.2020
7
Lec 7:
Learning from unlabelled data:
Self-supervision: Contrastive learning, rotation, jigsaw, Colorization
Weak-supervision: Multiple-instance learning, physical constraints; guest lecture by Patrik Vacek
lec7_internal_viewer
unlabelled_data.pdf
09.11.2020
8
Lec 8:
Architectures II:
pose regression, detection (Yolo), depth regression, spatial transformer nets, LIFTs
lec8_internal_viewer
architectures_ii.pdf
16.11.2020
9
Lec 9:
Structured inputs: Recurrent neural networks, Convolution in 1,2 and 3D and other structures:
guest lecture by Teymur Azayev
lec9_internal_viewer
lecture_9_structure.pdf
23.11.2020
10
Lec 10:
Reinforcement Learning I:
DQN, GAE+TD(lambda), DDPG
lec10_internal_viewer
reinforcement_learning.pdf
30.11.2020
11
Lec 11:
Reinforcement Learning II:
Policy gradients (REINFORCE), Inverse Reinforcement Learning, Applications,
What did not fit to lectures:
domain transfer, MAML, monodepth, depth from symmetries, Pytorch3D, CvxPyLayer
(it did not fit even here
)
lec11_internal_viewer
reinforcement_learning.pdf
07.12.2020
12
Lec 12:
Generative Adversarial Networks:
guest lecture by David Coufal, ÚI AV ČR)
lec12_internal_viewer
gans_coufal_vir_2019
14.12.2020
13
Exam Test ET
see BRUTE for results
04.01.2021
14
Selected teams present their semestral works (the rest presents during labs)
courses/b3b33vir/lectures/start.txt
· Last modified: 2020/12/08 09:19 by
zimmerk