06:16
How to Shape Model - EXTRA - MODEL VISUALIZATION
08:17
How to Shape Model - EXTRA - MODEL EVALUATION
08:31
How to Shape Model - Part5 - FITTING
09:17
How to Shape Model - Part4 - KERNELS
05:53
How to Shape Model - Part3 - TEMPLATE DESIGN
07:19
How to Shape Model - Part2 - RIGID ALIGNMENT
08:18
How to Shape Model - Part1 - POINT-CORRESPONDENCE
28:53
Dennis Madsen - PhD Defense presentation
06:09
Scalismo getting started with IntelliJ IDEA - Setup tutorial
13:20
Scalismo demo Applications - a Statistical Shape Modelling Library in Scala
11:52
PyTorch - The Basics of Transfer Learning with TorchVision and AlexNet
07:42
PyTorch - Torch vision for pretrained models (AlexNet)
10:25
PyTorch - Linear Regression implementation
16:13
PyTorch - The Basics
21:00
Deep Learning: Part6 - Generative Models - Beyond the Basics
39:09
Deep Learning: Part5 - Sequence Models (RNN, LSTM, GRU, Transformers)
46:08
Deep Learning: Part4 - CNN Architectures
34:17
Deep Learning: Part3 - Convolutional Neural Networks (CNNs)
01:13:26
Deep Learning: Part1 - Introduction to the basics
52:25
Deep Learning: Part2 - Hyperparameter tuning
09:56
Learning Shape Priors from Pieces (ShapeMi @ MICCAI 2020)
22:49
Slicer 3D for Volumetric Image Segmentation/labelling - Basic segmentation usage
10:00
A Closest Point Proposal for MCMC-based Probabilistic Surface Registration (ECCV2020)
00:59
A Closest Point Proposal for MCMC-based Probabilistic Surface Registration (ECCV2020 - Poster)
27:00
Probability Theory Refresher/Tutorial for ML/PR students
11:53
3D Image Segmentation (CT/MRI) with a 2D UNET - Part3: Volume Segmentation
22:52
3D Image Segmentation (CT/MRI) with a 2D UNET - Part2: Model training with TensorFlow 2
11:50
3D Image Segmentation (CT/MRI) with a 2D UNET - Part1: Data preparation
02:53
Setup PyCharm to use a Conda environment
36:48
Python Basics with Jupyter Notebook
10:47
Conda tutorial - Creating Virtual Python Environments
03:22
Hackathon rewind - gaming framework, gamEmotion@LauzHack2016
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Videos exploring topics within machine learning. A mix of lecture-style and coding tutorials.
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