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

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 1.4 — A simple example of learning [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 1.4 — A simple example of learning [Neural Networks for Machine Learning]

9 years ago - 5:39

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

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

Neural Networks for Machine Learning | What is Neural Networks? | Deep Learning Tutorial | CNN

REGex Software

Neural Networks for Machine Learning | What is Neural Networks? | Deep Learning Tutorial | CNN

Streamed 3 years ago - 3:51:59

Understanding how training neural networks for machine learning works

ZunairaMunir

Understanding how training neural networks for machine learning works

2 years ago - 0:38

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 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 14.2 — Discriminative learning for DBNs [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 14.2 — Discriminative learning for DBNs [Neural Networks for Machine Learning]

9 years ago - 9:41

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

Neural Networks for Machine Learning From Scratch - learn Python

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Neural Networks for Machine Learning From Scratch - learn Python

4 years ago - 3:03

Neural Networks for Machine Learning 3 4 Ways to deal with the large number of possible outputs

Amir plus

Neural Networks for Machine Learning 3 4 Ways to deal with the large number of possible outputs

8 years ago - 12:17

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 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

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

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 1.2 — What are neural networks [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 1.2 — What are neural networks [Neural Networks for Machine Learning]

9 years ago - 8:31

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

Lecture 9.3 — Using noise as a regularizer [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 9.3 — Using noise as a regularizer [Neural Networks for Machine Learning]

9 years ago - 7:32

Neural Networks for Machine Learning (3 Minutes)

Microlearning Daily

Neural Networks for Machine Learning (3 Minutes)

1 month ago - 2:25

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

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 14.4 — Modeling real valued data with an RBM [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 14.4 — Modeling real valued data with an RBM [Neural Networks for Machine Learning]

9 years ago - 9:57

Neural Networks for Machine Learning 4 1 Achieving viewpoint invariance

Amir plus

Neural Networks for Machine Learning 4 1 Achieving viewpoint invariance

8 years ago - 5:59

Lecture 14.1 — Learning layers of features by stacking RBMs [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 14.1 — Learning layers of features by stacking RBMs [Neural Networks for Machine Learning]

9 years ago - 17:35

Lecture 8.3 — Predicting the next character using HF [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 8.3 — Predicting the next character using HF [Neural Networks for Machine Learning]

9 years ago - 12:25

Neural Networks for Machine Learning 12 0 The ups and downs of back propagation

Amir plus

Neural Networks for Machine Learning 12 0 The ups and downs of back propagation

8 years ago - 9:54

Neural Networks for Machine Learning with Geoffrey Hinton

Olga Smith

Neural Networks for Machine Learning with Geoffrey Hinton

10 years ago - 4:51

Lecture 11.4 — Using stochastic units to improve search [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 11.4 — Using stochastic units to improve search [Neural Networks for Machine Learning]

9 years ago - 10:25

Lecture 5.2 — Achieving viewpoint invariance [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 5.2 — Achieving viewpoint invariance [Neural Networks for Machine Learning]

9 years ago - 5:59

Neural Networks for Machine Learning 4 3 Convolutional nets for object recognition

Amir plus

Neural Networks for Machine Learning 4 3 Convolutional nets for object recognition

8 years ago - 17:45

Neural Networks for Machine Learning 12 3 The wake sleep algorithm

Amir plus

Neural Networks for Machine Learning 12 3 The wake sleep algorithm

8 years ago - 13:15

Neural Networks for Machine Learning 12 1 Belief Nets

Amir plus

Neural Networks for Machine Learning 12 1 Belief Nets

8 years ago - 12:36

Lecture 10.4 — Making full Bayesian learning practical [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 10.4 — Making full Bayesian learning practical [Neural Networks for Machine Learning]

9 years ago - 6:45

Lecture 13.4 — The wake sleep algorithm [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 13.4 — The wake sleep algorithm [Neural Networks for Machine Learning]

9 years ago - 13:15

Lecture 6.2 — A bag of tricks for mini batch gradient descent [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 6.2 — A bag of tricks for mini batch gradient descent [Neural Networks for Machine Learning]

9 years ago - 13:16

Neural Networks for Machine Learning. Why do we need machine learning?

Professor tutorials

Neural Networks for Machine Learning. Why do we need machine learning?

5 years ago - 13:15

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

Neural Networks for Machine Learning 8 5 MacKay's quick and dirty method of setting weight costs

Amir plus

Neural Networks for Machine Learning 8 5 MacKay's quick and dirty method of setting weight costs

8 years ago - 3:32

Neural Networks for Machine Learning 2 3 The backpropagation algorithm

Amir plus

Neural Networks for Machine Learning 2 3 The backpropagation algorithm

8 years ago - 11:52

Lecture 10.5 — Dropout [Neural Networks for Machine Learning]

Colin McDonnell

Lecture 10.5 — Dropout [Neural Networks for Machine Learning]

9 years ago - 8:36

Insights into Neural Networks for Machine Learning

NMIMS CDOE

Insights into Neural Networks for Machine Learning

7 years ago - 1:01:04