Lecture 9.1 — Overview of ways to improve generalization [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 9.1 — Overview of ways to improve generalization [Neural Networks for Machine Learning]

9 years ago - 11:45

Lecture 12.4 — An example of RBM learning [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 12.4 — An example of RBM learning [Neural Networks for Machine Learning]

9 years ago - 7:15

Lecture 14.5 — RBMs are infinite sigmoid belief nets [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 14.5 — RBMs are infinite sigmoid belief nets [Neural Networks for Machine Learning]

9 years ago - 17:12

Building Accurate Neural Networks for Machine Learning

Wattage Wisdom

Building Accurate Neural Networks for Machine Learning

1 year ago - 20:28

Lecture 2.4 — Why the learning works [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 2.4 — Why the learning works [Neural Networks for Machine Learning]

9 years ago - 5:10

Neural Networks for Machine Learning (3 Minutes)

Microlearning Daily

Neural Networks for Machine Learning (3 Minutes)

3 weeks ago - 2:25

Lecture 15.1 — From PCA to autoencoders [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 15.1 — From PCA to autoencoders [Neural Networks for Machine Learning]

9 years ago - 7:58

Lecture 14.3 — Discriminative fine tuning [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 14.3 — Discriminative fine tuning [Neural Networks for Machine Learning]

9 years ago - 8:40

Lecture 12.3 — Restricted Boltzmann Machines [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 12.3 — Restricted Boltzmann Machines [Neural Networks for Machine Learning]

9 years ago - 10:55

Lecture 12.5 — RBMs for collaborative filtering [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 12.5 — RBMs for collaborative filtering [Neural Networks for Machine Learning]

9 years ago - 8:17

Lecture 4.2 — A brief diversion into cognitive science [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 4.2 — A brief diversion into cognitive science [Neural Networks for Machine Learning]

9 years ago - 4:27

Neural Networks for Machine Learning From Scratch - learn Python

Duong Ngoc Que

Neural Networks for Machine Learning From Scratch - learn Python

4 years ago - 3:03

Lecture 15.2 — Deep autoencoders [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 15.2 — Deep autoencoders [Neural Networks for Machine Learning]

9 years ago - 4:11

10.1 — Why it helps to combine models [Neural Networks for Machine Learning]

Colin McDonnell

10.1 — Why it helps to combine models [Neural Networks for Machine Learning]

9 years ago - 13:11

Lecture 4.5 — Dealing with many possible outputs [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 4.5 — Dealing with many possible outputs [Neural Networks for Machine Learning]

9 years ago - 12:17

Lecture 16.2 — Hierarchical Coordinate Frames [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 16.2 — Hierarchical Coordinate Frames [Neural Networks for Machine Learning]

9 years ago - 9:41

Lecture 13.2 — Belief Nets [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 13.2 — Belief Nets [Neural Networks for Machine Learning]

9 years ago - 12:36

Weight and Bias Matrices, Neural Networks for Machine Learning

Tetra Elements LLC

Weight and Bias Matrices, Neural Networks for Machine Learning

5 years ago - 15:26

Lecture 15.3 — Deep autoencoders for document retrieval [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 15.3 — Deep autoencoders for document retrieval [Neural Networks for Machine Learning]

9 years ago - 8:19

Lecture 12.2 — More efficient ways to get the statistics [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 12.2 — More efficient ways to get the statistics [Neural Networks for Machine Learning]

9 years ago - 14:49

Lecture 8.2 — Modeling character strings [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 8.2 — Modeling character strings [Neural Networks for Machine Learning]

9 years ago - 14:36

Lecture 7.3 — A toy example of training an RNN [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 7.3 — A toy example of training an RNN [Neural Networks for Machine Learning]

9 years ago - 6:15

Intel (Altera) Demonstration of Power-Efficient Neural Networks for Machine Learning Acceleration

Edge AI and Vision Alliance

Intel (Altera) Demonstration of Power-Efficient Neural Networks for Machine Learning Acceleration

9 years ago - 1:21

Why Neural Networks for Machine Learning? [Lecture 5.1]

AMILE - Machine Learning with Christian Nabert

Why Neural Networks for Machine Learning? [Lecture 5.1]

4 years ago - 4:57

Lecture 11.2 — Dealing with spurious minima [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 11.2 — Dealing with spurious minima [Neural Networks for Machine Learning]

9 years ago - 11:03

Lecture 1.1 — Why do we need machine learning [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 1.1 — Why do we need machine learning [Neural Networks for Machine Learning]

9 years ago - 13:15

Lecture 7.5 — Long term Short term memory [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 7.5 — Long term Short term memory [Neural Networks for Machine Learning]

9 years ago - 9:16

What is a Neural Network? | Neural Networks for Machine Learning (Simply Explained)

TutorialsPoint

What is a Neural Network? | Neural Networks for Machine Learning (Simply Explained)

8 months ago - 5:21

Lecture 5.3 — Convolutional nets for digit recognition [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 5.3 — Convolutional nets for digit recognition [Neural Networks for Machine Learning]

9 years ago - 16:02