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b221
courses
b3b33vir
lectures
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Table of Contents
Lectures
Lectures 2021
Lectures 2020
Lectures
date
week
topic
slides + videos
19.09.2022
1
Lec 1:
Machine learning 101:
model, loss, learning, issues, regression, classification
01_intro_learning_regression_classification.pdf
lec1_recording
26.09.2022
2
Lec 2:
Under the hood of a linear classifier:
two-class and multi-class linear classifier on RGB images
02_classification.pdf
lec2_recording
03.10.2022
3
Lec 3:
Where the hell does the loss come from?
MAP and ML estimate, KL divergence and losses.
03_mle.pdf
lec3_recording
10.10.2022
4
Lec 4:
The story of the cat's brain surgery:
fully-connected NN + fast backpropagation via Vector-Jacobian-Product (VJP), cortex + convolutional layer
04b_convnets.pdf
lec4_recording
17.10.2022
5
Lec 5:
Under the hood of auto-differentiation:
Vector-Jacobian-Product (VJP) vs chainrule and multiplication of Jacobians, convolutional layer and its VJP
04a_fully_connected_neural_nets.pdf
lec5_recording
24.10.2022
6
Midterm test
vir_2022_training_questions_midterm_test.pdf
(“pure SGD” = “SGD”, i.e. momentum = 0)
31.10.2022
7
Lec 6:
Why is learning prone to fail? - Structural issues:
layers + issues, batch-norm, drop-out
layers.pdf
lec6_recording
07.11.2022
8
Lec 7:
Why is learning prone to fail? - Optim. issues:
optimization vs learning, KL divergence, SGD, momentum, convergence rate, Adagrad, RMSProp, AdamOptimizer, diminishing/exploding gradient, oscillation, double descent
training.pdf
lec7_recording
14.11.2022
9
Lec 8:
What can('t) we do with a deep net?:
Classification (ResNet, Squeeze and Excitation Nets), Segmentation (DeepLab), Detection (Yolo, fast-RCNN), Regression (OpenPose), Spatial Transformer Nets, Memory and attention (recurrent nets, Image transformers with attention module)
08a_architectures_class_segm_simplified.pdf
08b_architectures_simple_reg_det_stn.pdf
lec8_recording
21.11.2022
10
Lec 9:
Reinforcement learning:
Approximated Q-learning, DQN, DDPG, Derivation of the policy gradient (REINFORCE), A2C, Inverse RL, Applications,
Lec 9 3/4:
Learning to optimize:
Backpropagation through unconstrained and constrained optimization problems, application (end-to-end differentiable modules cvxpy, gradSLAM, gradMPC, gradODE, pytorch3d)
Lec 9 9/10:
Self-supervision
: (pseudo-labeling, privileged information, monodepth)
09_reinforcement_learning.pdf
lec9_recording
28.11.2022
11
Lec 10:
Generative Adversial Networks:
Guest lecture by David Coufal, ÚI AV ČR
lec10_recording
vir2022_a.pdf
05.12.2022
12
Lec 11:
Normalizing Flows:
Guest lecture by David Coufal, ÚI AV ČR
lec11_recording
vir2022_b.pdf
12.12.2022
13
Exam test
vir_2022_training_questions_exam_test.pdf
09.01.2023
15
Lec 12:
Deep learning for satellite imagery:
Guest lecture of
Michal Reinstein
(chief scientist in
Spaceknow
)
The playlist of all lecture recordings is
here
.
Lectures 2021
date
week
topic
slides + videos
20.09.2021
1
Lec 1:
MLE regression:
derivation of L2 loss, prior
lec_01_MLE_regression
27.09.2021
2
Lec 2:
MLE classifier:
derivation of cross-entropy loss and logistic loss and linear classifier
lec_02_MLE_classifiction(pdf)
lec_02_recording
codes
04.10.2021
3
Lec 3:
Fully-connected network:
computational graph + backpropagation
lec_03_NN(pdf)
lec_03_recording
11.10.2021
4
Lec 4:
ConvNet:
convolutional layer + backpropagation
lec_04_ConvNets(pdf)
lec_04_recording
18.10.2021
5
Lec 5:
Training:
SGD, momentum, convergence rate, Adagrad, RMSProp, AdamOptimizer, diminishing/exploding gradient, oscilation
lec_05_training(pdf)
lec_05_recording
25.10.2021
6
Lec 6:
Layers:
Convolution, Activation functions, Batch-Instance Norm, MaxPooling, Losses + backpropagation
Test T1
lec_06_ConvNets
training_examples.zip
01.11.2021
7
Lec 7:
Architectures I:
classification (ResNet, Squeeze and `excitation Nets)
lec_07_classification_segmentation(pdf)
lec_07_recording
08.11.2021
8
Lec 8:
Architectures II:
segmentation (DeepLab), pose regression (OpenPose),
lec_08_classification_segmentation(pdf)
lec_08_recording
15.11.2021
9
Lec 9:
Architectures III:
detection (Yolo), depth regression, spatial transformer nets
lec_09_regression_detection_stn(pdf)
SVTI streams and recordings
22.11.2021
10
Lec 10:
Reinforcement Learning I:
Approximated Q-learning, DQN, DDPG, Policy gradient (REINFORCE),
lec_10_DQN_DDPG_REINFORCE(pdf)
lec_10_recording
29.11.2021
11
Lec 11:
Generative Adversarial Networks:
guest lecture by David Coufal, ÚI AV ČR)
lect_11_GANs(pdf)
lec_11_recording
06.12.2021
12
Lec 12:
Reinforcement Learning II:
Derivation of the policy gradient, A2C, Inverse Reinforcement Learning, Applications, end-to-end differentiable modules (cvxpy, gradSLAM, pytorch3d)
lec_12_A2C_IRL(pdf)
lec_12_recording
13.12.2021
13
Lec 13:
Memory and attention:
Recurrent nets, Image transformers with attention module,
Depth completition (monodepth), Graph convolution,
lec_13_memory_attention(pdf)
lec_13_recording
03.01.2022
15
Lec 14:
Exam Test ET
All SVTI streams
Lectures 2020
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: 2022/12/11 12:42 by
zimmerk