Neural Networks for Machine Learning

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最近下载：2024-07-12
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文档列表

5 - 4 - Convolutional nets for object recognition [17min].mp4 24.1 MB
7 - 1 - Modeling sequences A brief overview.mp4 21.1 MB
14 - 1 - Learning layers of features by stacking RBMs [17 min].mp4 21.0 MB
14 - 5 - OPTIONAL VIDEO RBMs are infinite sigmoid belief nets [17 mins].mp4 20.4 MB
5 - 3 - Convolutional nets for digit recognition [16 min].mp4 19.4 MB
12 - 2 - OPTIONAL VIDEO More efficient ways to get the statistics [15 mins].mp4 17.8 MB
2 - 5 - What perceptrons cant do [15 min].mp4 17.4 MB
8 - 2 - Modeling character strings with multiplicative connections [14 mins].mp4 17.4 MB
8 - 1 - A brief overview of Hessian Free optimization.mp4 17.0 MB
16 - 3 - OPTIONAL Bayesian optimization of hyper-parameters [13 min].mp4 16.6 MB
13 - 4 - The wake-sleep algorithm [13 min].mp4 16.4 MB
10 - 1 - Why it helps to combine models [13 min].mp4 15.9 MB
6 - 5 - Rmsprop Divide the gradient by a running average of its recent magnitude.mp4 15.9 MB
1 - 1 - Why do we need machine learning [13 min].mp4 15.8 MB
10 - 2 - Mixtures of Experts [13 min].mp4 15.7 MB
6 - 2 - A bag of tricks for mini-batch gradient descent.mp4 15.6 MB
13 - 2 - Belief Nets [13 min].mp4 15.6 MB
11 - 1 - Hopfield Nets [13 min].mp4 15.4 MB
4 - 1 - Learning to predict the next word [13 min].mp4 15.0 MB
4 - 5 - Ways to deal with the large number of possible outputs [15 min].mp4 14.9 MB
12 - 1 - Boltzmann machine learning [12 min].mp4 14.7 MB
8 - 3 - Learning to predict the next character using HF [12 mins].mp4 14.6 MB
16 - 1 - OPTIONAL Learning a joint model of images and captions [10 min].mp4 14.5 MB
13 - 3 - Learning sigmoid belief nets [12 min].mp4 14.3 MB
9 - 1 - Overview of ways to improve generalization [12 min].mp4 14.2 MB
3 - 1 - Learning the weights of a linear neuron [12 min].mp4 14.2 MB
3 - 4 - The backpropagation algorithm [12 min].mp4 14.0 MB
11 - 5 - How a Boltzmann machine models data [12 min].mp4 13.9 MB
11 - 2 - Dealing with spurious minima [11 min].mp4 13.4 MB
12 - 3 - Restricted Boltzmann Machines [11 min].mp4 13.3 MB
9 - 5 - The Bayesian interpretation of weight decay [11 min].mp4 12.9 MB
9 - 4 - Introduction to the full Bayesian approach [12 min].mp4 12.6 MB
13 - 1 - The ups and downs of back propagation [10 min].mp4 12.4 MB
11 - 4 - Using stochastic units to improv search [11 min].mp4 12.3 MB
15 - 5 - Learning binary codes for image retrieval [9 mins].mp4 12.1 MB
11 - 3 - Hopfield nets with hidden units [10 min].mp4 11.9 MB
14 - 2 - Discriminative learning for DBNs [9 mins].mp4 11.8 MB
8 - 4 - Echo State Networks [9 min].mp4 11.8 MB
14 - 4 - Modeling real-valued data with an RBM [10 mins].mp4 11.7 MB
16 - 2 - OPTIONAL Hierarchical Coordinate Frames [10 mins].mp4 11.7 MB
3 - 5 - Using the derivatives computed by backpropagation [10 min].mp4 11.7 MB
15 - 3 - Deep auto encoders for document retrieval [8 mins].mp4 10.7 MB
7 - 5 - Long-term Short-term-memory.mp4 10.7 MB
14 - 3 - What happens during discriminative fine-tuning [8 mins].mp4 10.7 MB
15 - 4 - Semantic Hashing [9 mins].mp4 10.5 MB
1 - 2 - What are neural networks [8 min].mp4 10.2 MB
6 - 3 - The momentum method.mp4 10.2 MB
10 - 5 - Dropout [9 min].mp4 10.2 MB
15 - 1 - From PCA to autoencoders [5 mins].mp4 10.2 MB
6 - 1 - Overview of mini-batch gradient descent.mp4 10.1 MB
12 - 5 - RBMs for collaborative filtering [8 mins].mp4 10.0 MB
2 - 2 - Perceptrons The first generation of neural networks [8 min].mp4 9.8 MB
1 - 3 - Some simple models of neurons [8 min].mp4 9.7 MB
1 - 5 - Three types of learning [8 min].mp4 9.4 MB
4 - 4 - Neuro-probabilistic language models [8 min].mp4 9.4 MB
7 - 4 - Why it is difficult to train an RNN.mp4 9.3 MB
2 - 1 - Types of neural network architectures [7 min].mp4 9.2 MB
12 - 4 - An example of RBM learning [7 mins].mp4 9.1 MB
9 - 3 - Using noise as a regularizer [7 min].mp4 8.9 MB
10 - 3 - The idea of full Bayesian learning [7 min].mp4 8.8 MB
15 - 6 - Shallow autoencoders for pre-training [7 mins].mp4 8.7 MB
10 - 4 - Making full Bayesian learning practical [7 min].mp4 8.5 MB
4 - 3 - Another diversion The softmax output function [7 min].mp4 8.4 MB
9 - 2 - Limiting the size of the weights [6 min].mp4 7.7 MB
7 - 2 - Training RNNs with back propagation.mp4 7.7 MB
2 - 3 - A geometrical view of perceptrons [6 min].mp4 7.7 MB
7 - 3 - A toy example of training an RNN.mp4 7.6 MB
5 - 2 - Achieving viewpoint invariance [6 min].mp4 7.2 MB
6 - 4 - Adaptive learning rates for each connection.mp4 7.0 MB
1 - 4 - A simple example of learning [6 min].mp4 6.9 MB
2 - 4 - Why the learning works [5 min].mp4 6.2 MB
3 - 2 - The error surface for a linear neuron [5 min].mp4 6.2 MB
5 - 1 - Why object recognition is difficult [5 min].mp4 5.6 MB
4 - 2 - A brief diversion into cognitive science [4 min].mp4 5.6 MB
15 - 2 - Deep auto encoders [4 mins].mp4 5.2 MB
9 - 6 - MacKays quick and dirty method of setting weight costs [4 min].mp4 4.6 MB
3 - 3 - Learning the weights of a logistic output neuron [4 min].mp4 4.6 MB
16 - 4 - OPTIONAL The fog of progress [3 min].mp4 2.9 MB
Neural Networks for Machine Learning.torrent 25.3 kB

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