_    ___ ___      _                               
   / \  |_ _/ _ \ ___| |_ _ __ ___  __ _ _ __ ___  ___
  / _ \  | | | | / __| __| '__/ _ \/ _` | '_ ` _ \/ __|
 / ___ \ | | |_| \__ \ |_| | |  __/ (_| | | | | | \__ \
/_/   \_\___\___/|___/\__|_|  \___|\__,_|_| |_| |_|___/

Invidious > Channel > Colin Reckons

Colin Reckons	12200 subscribers    RSS
View channel on YouTube Videos Playlists

YT 14 minutes 35 seconds 19K views Shared 8 years ago Colin Reckons Lecture 2.5 — What perceptrons can't do [Neural Networks for Machine Learning] YT 5 minutes 39 seconds 41K views Shared 8 years ago Colin Reckons Lecture 1.4 — A simple example of learning [Neural Networks for Machine Learning] YT 8 minutes 24 seconds 46K views Shared 8 years ago Colin Reckons Lecture 1.3 — Some simple models of neurons [Neural Networks for Machine Learning]

YT 8 minutes 31 seconds 57K views Shared 8 years ago Colin Reckons Lecture 1.2 — What are neural networks [Neural Networks for Machine Learning] YT 13 minutes 15 seconds 216K views Shared 8 years ago Colin Reckons Lecture 1.1 — Why do we need machine learning [Neural Networks for Machine Learning] YT 7 minutes 29 seconds 45K views Shared 8 years ago Colin Reckons Lecture 2.1 — Types of neural network architectures [Neural Networks for Machine Learning]

YT 7 minutes 38 seconds 38K views Shared 8 years ago Colin Reckons Lecture 1.5 — Three types of learning [Neural Networks for Machine Learning] YT 8 minutes 17 seconds 31K views Shared 8 years ago Colin Reckons Lecture 2.2 — Perceptrons: first-generation neural networks [Neural Networks for Machine Learning] YT 6 minutes 25 seconds 24K views Shared 8 years ago Colin Reckons Lecture 2.3 — A geometrical view of perceptrons [Neural Networks for Machine Learning]

YT 5 minutes 4 seconds 15K views Shared 8 years ago Colin Reckons Lecture 3.2 — The error surface for a linear neuron [Neural Networks for Machine Learning] YT 11 minutes 56 seconds 23K views Shared 8 years ago Colin Reckons Lecture 3.1 — Learning the weights of a linear neuron [Neural Networks for Machine Learning] YT 5 minutes 10 seconds 19K views Shared 8 years ago Colin Reckons Lecture 2.4 — Why the learning works [Neural Networks for Machine Learning]

YT 3 minutes 57 seconds 14K views Shared 8 years ago Colin Reckons Lecture 3.3 — Learning weights of logistic output neuron [Neural Networks for Machine Learning] YT 11 minutes 52 seconds 35K views Shared 8 years ago Colin Reckons Lecture 3.4 — The backpropagation algorithm [Neural Networks for Machine Learning] YT 9 minutes 50 seconds 15K views Shared 8 years ago Colin Reckons Lecture 3.5 — Using the derivatives from backpropagation [Neural Networks for Machine Learning]

YT 12 minutes 34 seconds 15K views Shared 8 years ago Colin Reckons Lecture 4.1 — Learning to predict the next word [Neural Networks for Machine Learning] YT 4 minutes 27 seconds 9.5K views Shared 8 years ago Colin Reckons Lecture 4.2 — A brief diversion into cognitive science [Neural Networks for Machine Learning] YT 7 minutes 21 seconds 63K views Shared 8 years ago Colin Reckons Lecture 4.3 — The softmax output function [Neural Networks for Machine Learning]

YT 7 minutes 53 seconds 11K views Shared 8 years ago Colin Reckons Lecture 4.4 — Neuro-probabilistic language models [Neural Networks for Machine Learning] YT 12 minutes 17 seconds 9.6K views Shared 8 years ago Colin Reckons Lecture 4.5 — Dealing with many possible outputs [Neural Networks for Machine Learning] YT 4 minutes 41 seconds 8.8K views Shared 8 years ago Colin Reckons Lecture 5.1 — Why object recognition is difficult [Neural Networks for Machine Learning]

YT 5 minutes 59 seconds 8.4K views Shared 8 years ago Colin Reckons Lecture 5.2 — Achieving viewpoint invariance [Neural Networks for Machine Learning] YT 16 minutes 2 seconds 10K views Shared 8 years ago Colin Reckons Lecture 5.3 — Convolutional nets for digit recognition [Neural Networks for Machine Learning] YT 8 minutes 23 seconds 13K views Shared 8 years ago Colin Reckons Lecture 6.1 — Overview of mini batch gradient descent [Neural Networks for Machine Learning]

YT 17 minutes 45 seconds 7.9K views Shared 8 years ago Colin Reckons Lecture 5.4 — Convolutional nets for object recognition [Neural Networks for Machine Learning] YT 11 minutes 39 seconds 14K views Shared 8 years ago Colin Reckons Lecture 6.5 — Rmsprop: normalize the gradient [Neural Networks for Machine Learning] YT 13 minutes 16 seconds 16K views Shared 8 years ago Colin Reckons Lecture 6.2 — A bag of tricks for mini batch gradient descent [Neural Networks for Machine Learning]

YT 6 minutes 24 seconds 9.1K views Shared 8 years ago Colin Reckons Lecture 7.2 — Training RNNs with back propagation [Neural Networks for Machine Learning] YT 17 minutes 24 seconds 10K views Shared 8 years ago Colin Reckons Lecture 7.1 — Modeling sequences: a brief overview [Neural Networks for Machine Learning] YT 6 minutes 15 seconds 7.9K views Shared 8 years ago Colin Reckons Lecture 7.3 — A toy example of training an RNN [Neural Networks for Machine Learning]

YT 14 minutes 36 seconds 4.6K views Shared 8 years ago Colin Reckons Lecture 8.2 — Modeling character strings [Neural Networks for Machine Learning] YT 12 minutes 25 seconds 3.4K views Shared 8 years ago Colin Reckons Lecture 8.3 — Predicting the next character using HF [Neural Networks for Machine Learning] YT 9 minutes 38 seconds 14K views Shared 8 years ago Colin Reckons Lecture 8.4 — Echo State Networks [Neural Networks for Machine Learning]

YT 14 minutes 25 seconds 8K views Shared 8 years ago Colin Reckons Lecture 8.1 — A brief overview of Hessian-free optimization [Neural Networks for Machine Learning] YT 11 minutes 45 seconds 6.6K views Shared 8 years ago Colin Reckons Lecture 9.1 — Overview of ways to improve generalization [Neural Networks for Machine Learning] YT 6 minutes 23 seconds 4.2K views Shared 8 years ago Colin Reckons Lecture 9.2 — Limiting the size of the weights [Neural Networks for Machine Learning]

YT 7 minutes 32 seconds 6.6K views Shared 8 years ago Colin Reckons Lecture 9.3 — Using noise as a regularizer [Neural Networks for Machine Learning] YT 10 minutes 50 seconds 33K views Shared 8 years ago Colin Reckons Lecture 9.4 — Introduction to the full Bayesian approach [Neural Networks for Machine Learning] YT 3 minutes 32 seconds 4.7K views Shared 8 years ago Colin Reckons Lecture 9.6 — MacKay 's quick and dirty method [Neural Networks for Machine Learning]

YT 10 minutes 53 seconds 10K views Shared 8 years ago Colin Reckons Lecture 9.5 — The Bayesian interpretation of weight decay [Neural Networks for Machine Learning] YT 7 minutes 28 seconds 5.5K views Shared 8 years ago Colin Reckons Lecture 10.3 — The idea of full Bayesian learning [Neural Networks for Machine Learning] YT 8 minutes 36 seconds 8.2K views Shared 8 years ago Colin Reckons Lecture 10.5 — Dropout [Neural Networks for Machine Learning]

YT 6 minutes 45 seconds 4.6K views Shared 8 years ago Colin Reckons Lecture 10.4 — Making full Bayesian learning practical [Neural Networks for Machine Learning] YT 13 minutes 16 seconds 8.6K views Shared 8 years ago Colin Reckons Lecture 10.2 — Mixtures of Experts [Neural Networks for Machine Learning] YT 13 minutes 2 seconds 34K views Shared 8 years ago Colin Reckons Lecture 11.1 — Hopfield Nets [Neural Networks for Machine Learning]

YT 9 minutes 40 seconds 7K views Shared 8 years ago Colin Reckons Lecture 11.3 — Hopfield nets with hidden units [Neural Networks for Machine Learning] YT 11 minutes 3 seconds 6.8K views Shared 8 years ago Colin Reckons Lecture 11.2 — Dealing with spurious minima [Neural Networks for Machine Learning] YT 13 minutes 11 seconds 5.3K views Shared 8 years ago Colin Reckons 10.1 — Why it helps to combine models [Neural Networks for Machine Learning]

YT 10 minutes 25 seconds 6.4K views Shared 8 years ago Colin Reckons Lecture 11.4 — Using stochastic units to improve search [Neural Networks for Machine Learning] YT 11 minutes 45 seconds 18K views Shared 8 years ago Colin Reckons Lecture 11.5 — How a Boltzmann machine models data [Neural Networks for Machine Learning] YT 10 minutes 55 seconds 7.5K views Shared 8 years ago Colin Reckons Lecture 12.3 — Restricted Boltzmann Machines [Neural Networks for Machine Learning]

YT 12 minutes 16 seconds 22K views Shared 8 years ago Colin Reckons Lecture 12.1 — Boltzmann machine learning [Neural Networks for Machine Learning] YT 7 minutes 15 seconds 6K views Shared 8 years ago Colin Reckons Lecture 12.4 — An example of RBM learning [Neural Networks for Machine Learning] YT 14 minutes 49 seconds 4.6K views Shared 8 years ago Colin Reckons Lecture 12.2 — More efficient ways to get the statistics [Neural Networks for Machine Learning]

YT 8 minutes 17 seconds 4.8K views Shared 8 years ago Colin Reckons Lecture 12.5 — RBMs for collaborative filtering [Neural Networks for Machine Learning] YT 9 minutes 54 seconds 3.5K views Shared 8 years ago Colin Reckons Lecture 13.1 — The ups and downs of backpropagation [Neural Networks for Machine Learning] YT 12 minutes 36 seconds 4.6K views Shared 8 years ago Colin Reckons Lecture 13.2 — Belief Nets [Neural Networks for Machine Learning]

YT 9 minutes 41 seconds 2.6K views Shared 8 years ago Colin Reckons Lecture 14.2 — Discriminative learning for DBNs [Neural Networks for Machine Learning] YT 11 minutes 26 seconds 6.1K views Shared 8 years ago Colin Reckons Lecture 13.3 — Learning sigmoid belief nets [Neural Networks for Machine Learning] YT 13 minutes 15 seconds 6.1K views Shared 8 years ago Colin Reckons Lecture 13.4 — The wake sleep algorithm [Neural Networks for Machine Learning]