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fileCoursera-Neural-Networks-f8bj

Coursera Neural Networks and chine Learning Geoffrey Hinton University Toronto
  • MP41 - 1 - Why do we need chine learning [13 min].mp415.05MB
  • MP41 - 2 - What are neural networks [8 min].mp49.76MB
  • MP41 - 3 - Some simple models of neurons [8 min].mp49.26MB
  • MP41 - 4 - A simple example of learning [6 min].mp46.57MB
  • MP41 - 5 - Three types of learning [8 min].mp48.96MB
  • MP410 - 1 - Why it helps to combine models [13 min].mp415.12MB
  • MP410 - 2 - Mixtures of Experts [13 min].mp414.98MB
  • MP410 - 3 - The idea of full Bayesian learning [7 min].mp48.39MB
  • MP410 - 4 - king full Bayesian learning practical [7 min].mp48.13MB
  • MP410 - 5 - Dropout [9 min].mp49.69MB
  • MP42 - 1 - Types of neural network architectures [7 min].mp48.78MB
  • MP42 - 2 - Perceptrons The first generation of neural networks [8 min].mp49.39MB
  • MP42 - 3 - A geometrical view of perceptrons [6 min].mp47.32MB
  • MP42 - 4 - Why the learning works [5 min].mp45.90MB
  • MP42 - 5 - What perceptrons cant do [15 min].mp416.57MB
  • MP43 - 1 - Learning the weights of a linear neuron [12 min].mp413.52MB
  • MP43 - 2 - The error surface for a linear neuron [5 min].mp45.89MB
  • MP43 - 3 - Learning the weights of a logistic output neuron [4 min].mp44.37MB
  • MP43 - 4 - The backpropagation algorithm [12 min].mp413.35MB
  • MP43 - 5 - Using the derivatives computed by backpropagation [10 min].mp411.15MB
  • MP44 - 1 - Learning to predict the next word [13 min].mp414.28MB
  • MP44 - 2 - A brief diversion into cognitive science [4 min].mp45.31MB
  • MP44 - 3 - Another diversion The softx output function [7 min].mp48.03MB
  • MP44 - 4 - Neuro-probabilistic language models [8 min].mp48.93MB
  • MP44 - 5 - Ways to deal with the large number of possible outputs [15 min].mp414.26MB
  • MP45 - 1 - Why oect recognition is difficult [5 min].mp45.37MB
  • MP45 - 2 - Achieving viewpoint invariance [6 min].mp46.89MB
  • MP45 - 3 - Convolutional nets for digit recognition [16 min].mp418.46MB
  • MP45 - 4 - Convolutional nets for oect recognition [17min].mp423.03MB
  • MP46 - 1 - Overview of mini-batch gradient descent.mp49.60MB
  • MP46 - 2 - A bag of tricks for mini-batch gradient descent.mp414.90MB
  • MP46 - 3 - The momentum method.mp49.74MB
  • MP46 - 4 - Adaptive learning rates for each connection.mp46.63MB
  • MP46 - 5 - Rmsprop Divide the gradient by a running erage of its recent gnitude.mp415.12MB
  • MP47 - 1 - Modeling sequences A brief overview.mp420.13MB
  • MP47 - 2 - Training RNNs with back propagation.mp47.33MB
  • MP47 - 3 - A toy example of training an RNN.mp47.24MB
  • MP47 - 4 - Why it is difficult to train an RNN.mp48.89MB
  • MP47 - 5 - Long-term Short-term-memory.mp410.23MB
  • MP48 - 1 - A brief overview of Hessian Free optimization.mp416.24MB
  • MP48 - 2 - Modeling character strings with multiplicative connections [14 mins].mp416.56MB
  • MP48 - 3 - Learning to predict the next character using HF [12 mins].mp413.92MB
  • MP48 - 4 - Echo State Networks [9 min].mp411.28MB
  • MP49 - 1 - Overview of ways to improve generalization [12 min].mp413.57MB
  • MP49 - 2 - Limiting the size of the weights [6 min].mp47.36MB
  • MP49 - 3 - Using se as a regularizer [7 min].mp48.48MB
  • MP49 - 4 - Introduction to the full Bayesian approach [12 min].mp412.00MB
  • MP49 - 5 - The Bayesian interpretation of weight decay [11 min].mp412.27MB
  • MP49 - 6 - cKays quick and dirty method of setting weight costs [4 min].mp44.37MB
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