Coursera - Neural Networks and Machine Learning, Geoffrey Hinton University of Toronto

Coursera Neural Networks Machine Learning Geoffrey Hinton University Toronto

种子大小：532.59 MB

收录时间：2014-01-15

链接操作：点赞1 dmca/举报

磁力链接：

资源下载：磁力链接磁力资源蜘蛛资源磁力引擎网盘资源影视资源云盘资源免费小说美女图片

文件列表：49File

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