02 – Neural nets: rotation and squashing
Course website: http://bit.ly/DLSP21-web
Playlist: http://bit.ly/DLSP21-YouTube
Speaker: Alfredo Canziani
Chapters
00:00 – Welcome!
02:30 – Affine transformations and non-linearities
03:42 – Affine transformation: intuition
13:47 – Summary slide
14:06 – Jupyter and PyTorch
18:47 – Input data
24:39 – Coding a 2×2 linear transformation & Gilbert Strang
30:15 – Coding a 2×2 linear transformation w/ PyTorch
33:03 – Hyperbolic tangent
47:02 – Rotation + squashing + rotation: ooooh, a neural net
49:28 – Rectifying linear unit (ReLU)
51:10 – Shoutout to @vcubingx and his animation
52:09 – Spiky transformation: what happen here?
54:23 – A *very deep* neural net
56:30 – A deep net with tanh
56:43 – Summary of today lesson
Видео 02 – Neural nets: rotation and squashing канала Alfredo Canziani
Playlist: http://bit.ly/DLSP21-YouTube
Speaker: Alfredo Canziani
Chapters
00:00 – Welcome!
02:30 – Affine transformations and non-linearities
03:42 – Affine transformation: intuition
13:47 – Summary slide
14:06 – Jupyter and PyTorch
18:47 – Input data
24:39 – Coding a 2×2 linear transformation & Gilbert Strang
30:15 – Coding a 2×2 linear transformation w/ PyTorch
33:03 – Hyperbolic tangent
47:02 – Rotation + squashing + rotation: ooooh, a neural net
49:28 – Rectifying linear unit (ReLU)
51:10 – Shoutout to @vcubingx and his animation
52:09 – Spiky transformation: what happen here?
54:23 – A *very deep* neural net
56:30 – A deep net with tanh
56:43 – Summary of today lesson
Видео 02 – Neural nets: rotation and squashing канала Alfredo Canziani
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
14L – Lagrangian backpropagation, final project winners, and Q&A session12 – Planning and controlPractical 4.1 – RNN forward and backwardWeek 15 – Practicum part B: Training latent variable energy based models (EBMs)Behind the scenesTeraDeep Image Parser02 – Supervised learning / ClassificationPerson detectorPractical 3.2 – CNN modelsWeek 9 – Practicum: (Energy-based) Generative adversarial networks[LIVE] Free energy gentle introductionPurdue theme08 – From LV-EBM to target prop to (vanilla, denoising, contractive, variational) autoencoder06L – Latent variable EBMs for structured predictionWhy not?Matrix multiplication, signals, and convolutionsPractical 3.3 – CNN trainingGoodbye to DL20,21,22,23 apartmentModel-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic4 febbraio 2012 Molo AudaceStreaming test