Neural Networks for Machine Learning
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Magnet LinkFile List:
- 0504 Convolutional nets for object recognition.mp4 23.03 MB
- 0701 Modeling sequences_ A brief overview.mp4 20.13 MB
- 1401 Learning layers of features by stacking RBMs.mp4 20.07 MB
- 1405 OPTIONAL VIDEO_ RBMs are infinite sigmoid belief nets.mp4 19.44 MB
- 0503 Convolutional nets for digit recognition.mp4 18.46 MB
- 1202 OPTIONAL VIDEO_ More efficient ways to get the statistics.mp4 16.93 MB
- 0205 What perceptrons can_t do.mp4 16.58 MB
- 0802 Modeling character strings with multiplicative connections.mp4 16.56 MB
- 0801 A brief overview of Hessian Free optimization.mp4 16.24 MB
- 1603 OPTIONAL_ Bayesian optimization of hyper-parameters.mp4 15.80 MB
- 1304 The wake-sleep algorithm.mp4 15.68 MB
- 1001 Why it helps to combine models.mp4 15.12 MB
- 0605 Rmsprop_ Divide the gradient by a running average of its recent magnitude.mp4 15.12 MB
- 0101 Why do we need machine learning_.mp4 15.05 MB
- 1002 Mixtures of Experts.mp4 14.98 MB
- 0602 A bag of tricks for mini-batch gradient descent.mp4 14.90 MB
- 1302 Belief Nets.mp4 14.86 MB
- 1101 Hopfield Nets.mp4 14.65 MB
- 0401 Learning to predict the next word.mp4 14.28 MB
- 0405 Ways to deal with the large number of possible outputs.mp4 14.26 MB
- 1303 Learning sigmoid belief nets.mp4 14.19 MB
- 1201 Boltzmann machine learning.mp4 14.03 MB
- 0803 Learning to predict the next character using HF.mp4 13.92 MB
- 1601 OPTIONAL_ Learning a joint model of images and captions.mp4 13.83 MB
- 0901 Overview of ways to improve generalization.mp4 13.57 MB
- 0301 Learning the weights of a linear neuron.mp4 13.52 MB
- 0304 The backpropagation algorithm.mp4 13.35 MB
- 1105 How a Boltzmann machine models data.mp4 13.29 MB
- 1102 Dealing with spurious minima.mp4 12.77 MB
- 1203 Restricted Boltzmann Machines.mp4 12.68 MB
- 0905 The Bayesian interpretation of weight decay.mp4 12.27 MB
- 0904 Introduction to the full Bayesian approach.mp4 12.00 MB
- 1301 The ups and downs of back propagation.mp4 11.83 MB
- 1104 Using stochastic units to improv search.mp4 11.77 MB
- 1505 Learning binary codes for image retrieval.mp4 11.51 MB
- 1103 Hopfield nets with hidden units.mp4 11.31 MB
- 1402 Discriminative learning for DBNs.mp4 11.29 MB
- 0804 Echo State Networks.mp4 11.28 MB
- 1404 Modeling real-valued data with an RBM.mp4 11.20 MB
- 1602 OPTIONAL_ Hierarchical Coordinate Frames.mp4 11.16 MB
- 0305 Using the derivatives computed by backpropagation.mp4 11.15 MB
- 1504 Semantic Hashing.mp4 10.97 MB
- 1503 Deep auto encoders for document retrieval.mp4 10.25 MB
- 0705 Long-term Short-term-memory.mp4 10.23 MB
- 1403 What happens during discriminative fine-tuning_.mp4 10.18 MB
- 0202 Perceptrons_ The first generation of neural networks.mp4 9.78 MB
- 0102 What are neural networks_.mp4 9.76 MB
- 0603 The momentum method.mp4 9.74 MB
- 1005 Dropout.mp4 9.69 MB
- 1501 From PCA to autoencoders.mp4 9.68 MB
Neural Networks for Machine Learning
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