Neural Networks for Machine Learning | 2D49241CF9A689583FE2352EAB62AD3025A3E42F

Neural Networks for Machine Learning

Name	Neural Networks for Machine Learning	DOWNLOAD Copy Link
Total Size	964.3 MB
Total Files	243
Hash	2D49241CF9A689583FE2352EAB62AD3025A3E42F

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/Info/
0304 reading_list-Learning representations by back-propagating errors.pdf	3.1 MB
0404 reading_list-Neural probabilisic language models.pdf	140.1 KB
0405 images-Lecture4-turian.png	154.4 KB
0504 reading_list-Convolutional networks for images, speech, and time series.pdf	125.4 KB
0504 reading_list-Gradient-based learning applied to document recognition.pdf	955.1 KB
0705 reading_list-A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks.pdf	320.6 KB
0803 reading_list-Generating Text with Recurrent Neural Networks.pdf	273.4 KB
0804 Echo state network - Scholarpedia.htm	318.9 KB
1002 reading_list-Adaptive mixtures of local experts.pdf	271.1 KB
1005 reading_list-Improving neural networks by preventing co-adaptation of feature detectors.pdf	1.7 MB
1105 Boltzmann machine - Scholarpedia.htm	295.9 KB
1303 reading_list-Connectionist learning of belief networks.pdf	2.4 MB
1304 reading_list-- algorithm for unsupervised neural networks.pdf	261.5 KB
1401 reading_list-A fast learning algorithm for deep belief nets.pdf	787.8 KB
1401 reading_list-Self-taught learning- transfer learning from unlabeled data.pdf	484.9 KB
1401 reading_list-To recognize shapes, first learn to generate images.pdf	513.9 KB
1504 reading_list-Semantic Hashing.pdf	641.6 KB
1505 reading_list-Using Very Deep Autoencoders for Content-Based Image Retrieval.pdf	759.2 KB
/Slides/
lecture_slides-lec1.pdf	4.1 MB
lecture_slides-lec10.pdf	847.1 KB
lecture_slides-lec11.pdf	711.2 KB
lecture_slides-lec12.pdf	1.8 MB
lecture_slides-lec13.pdf	314.6 KB
lecture_slides-lec14.pdf	1.2 MB
lecture_slides-lec15.pdf	2.6 MB
lecture_slides-lec16.pdf	346.9 KB
lecture_slides-lec2.pdf	504.8 KB
lecture_slides-lec3.pdf	548.0 KB
lecture_slides-lec4.pdf	964.1 KB
lecture_slides-lec5.pdf	1.6 MB
lecture_slides-lec6.pdf	546.8 KB
lecture_slides-lec7.pdf	976.0 KB
lecture_slides-lec8.pdf	658.3 KB
lecture_slides-lec9.pdf	719.0 KB
/
0101 Why do we need machine learning_.mp4	15.8 MB
0101 Why do we need machine learning_.srt	18.8 KB
0102 What are neural networks_.mp4	10.2 MB
0102 What are neural networks_.srt	11.8 KB
0103 Some simple models of neurons.mp4	9.7 MB
0103 Some simple models of neurons.srt	11.0 KB
0104 A simple example of learning.mp4	6.9 MB
0104 A simple example of learning.srt	7.2 KB
0105 Three types of learning.mp4	9.4 MB
0105 Three types of learning.srt	10.6 KB
0201 Types of neural network architectures.mp4	9.2 MB
0201 Types of neural network architectures.srt	10.1 KB
0202 Perceptrons_ The first generation of neural networks.mp4	10.3 MB
0202 Perceptrons_ The first generation of neural networks.srt	11.1 KB
0203 A geometrical view of perceptrons.mp4	7.7 MB
0203 A geometrical view of perceptrons.srt	8.5 KB
0204 Why the learning works.mp4	6.2 MB
0204 Why the learning works.srt	6.6 KB
0205 What perceptrons can_t do.mp4	17.4 MB
0205 What perceptrons can_t do.srt	18.9 KB
0301 Learning the weights of a linear neuron.mp4	14.2 MB
0301 Learning the weights of a linear neuron.srt	15.5 KB
0302 The error surface for a linear neuron.mp4	6.2 MB
0302 The error surface for a linear neuron.srt	6.5 KB
0303 Learning the weights of a logistic output neuron.mp4	4.6 MB
0303 Learning the weights of a logistic output neuron.srt	4.6 KB
0304 The backpropagation algorithm.mp4	14.0 MB
0304 The backpropagation algorithm.srt	15.2 KB
0305 Using the derivatives computed by backpropagation.mp4	11.7 MB
0305 Using the derivatives computed by backpropagation.srt	13.9 KB
0401 Learning to predict the next word.mp4	15.0 MB
0401 Learning to predict the next word.srt	16.9 KB
0402 A brief diversion into cognitive science.mp4	5.6 MB
0402 A brief diversion into cognitive science.srt	5.9 KB
0403 Another diversion_ The softmax output function.mp4	8.4 MB
0403 Another diversion_ The softmax output function.srt	9.3 KB
0404 Neuro-probabilistic language models.mp4	9.4 MB
0404 Neuro-probabilistic language models.srt	11.0 KB
0405 Ways to deal with the large number of possible outputs.mp4	14.9 MB
0405 Ways to deal with the large number of possible outputs.srt	18.6 KB
0501 Why object recognition is difficult.mp4	5.6 MB
0501 Why object recognition is difficult.srt	6.3 KB
0502 Achieving viewpoint invariance.mp4	7.2 MB
0502 Achieving viewpoint invariance.srt	8.3 KB
0503 Convolutional nets for digit recognition.mp4	19.4 MB
0503 Convolutional nets for digit recognition.srt	22.1 KB
0504 Convolutional nets for object recognition.mp4	24.1 MB
0504 Convolutional nets for object recognition.srt	26.2 KB
0601 Overview of mini-batch gradient descent.mp4	10.1 MB
0601 Overview of mini-batch gradient descent.srt	12.2 KB
0602 A bag of tricks for mini-batch gradient descent.mp4	15.6 MB
0602 A bag of tricks for mini-batch gradient descent.srt	19.2 KB
0603 The momentum method.mp4	10.2 MB
0603 The momentum method.srt	11.4 KB
0604 Adaptive learning rates for each connection.mp4	7.0 MB
0604 Adaptive learning rates for each connection.srt	7.9 KB
0605 Rmsprop_ Divide the gradient by a running average of its recent magnitude.mp4	15.9 MB
0605 Rmsprop_ Divide the gradient by a running average of its recent magnitude.srt	16.1 KB
0701 Modeling sequences_ A brief overview.mp4	21.1 MB
0701 Modeling sequences_ A brief overview.srt	23.2 KB
0702 Training RNNs with back propagation.mp4	7.7 MB
0702 Training RNNs with back propagation.srt	8.6 KB
0703 A toy example of training an RNN.mp4	7.6 MB
0703 A toy example of training an RNN.srt	7.7 KB
0704 Why it is difficult to train an RNN.mp4	9.3 MB
0704 Why it is difficult to train an RNN.srt	10.0 KB
0705 Long-term Short-term-memory.mp4	10.7 MB
0705 Long-term Short-term-memory.srt	11.9 KB
0801 A brief overview of Hessian Free optimization.mp4	17.0 MB
0801 A brief overview of Hessian Free optimization.srt	18.4 KB
0802 Modeling character strings with multiplicative connections.mp4	17.4 MB
0802 Modeling character strings with multiplicative connections.srt	17.9 KB
0803 Learning to predict the next character using HF.mp4	14.6 MB
0803 Learning to predict the next character using HF.srt	16.1 KB
0804 Echo State Networks.mp4	11.8 MB
0804 Echo State Networks.srt	12.3 KB
0901 Overview of ways to improve generalization.mp4	14.2 MB
0901 Overview of ways to improve generalization.srt	16.2 KB
0902 Limiting the size of the weights.mp4	7.7 MB
0902 Limiting the size of the weights.srt	8.6 KB
0903 Using noise as a regularizer.mp4	8.9 MB
0903 Using noise as a regularizer.srt	9.1 KB
0904 Introduction to the full Bayesian approach.mp4	12.6 MB
0904 Introduction to the full Bayesian approach.srt	13.5 KB
0905 The Bayesian interpretation of weight decay.mp4	12.9 MB
0905 The Bayesian interpretation of weight decay.srt	13.3 KB
0906 MacKay_s quick and dirty method of setting weight costs.mp4	4.6 MB
0906 MacKay_s quick and dirty method of setting weight costs.srt	4.5 KB
1001 Why it helps to combine models.mp4	15.9 MB
1001 Why it helps to combine models.srt	18.1 KB
1002 Mixtures of Experts.mp4	15.7 MB
1002 Mixtures of Experts.srt	17.5 KB
1003 The idea of full Bayesian learning.mp4	8.8 MB
1003 The idea of full Bayesian learning.srt	10.5 KB
1004 Making full Bayesian learning practical.mp4	8.5 MB
1004 Making full Bayesian learning practical.srt	8.7 KB
1005 Dropout.mp4	10.2 MB
1005 Dropout.srt	12.0 KB
1101 Hopfield Nets.mp4	15.4 MB
1101 Hopfield Nets.srt	16.8 KB
1102 Dealing with spurious minima.mp4	13.4 MB
1102 Dealing with spurious minima.srt	15.2 KB
1103 Hopfield nets with hidden units.mp4	11.9 MB
1103 Hopfield nets with hidden units.srt	12.6 KB
1104 Using stochastic units to improv search.mp4	12.3 MB
1104 Using stochastic units to improv search.srt	14.3 KB
1105 How a Boltzmann machine models data.mp4	13.9 MB
1105 How a Boltzmann machine models data.srt	16.3 KB
1201 Boltzmann machine learning.mp4	14.7 MB
1201 Boltzmann machine learning.srt	16.4 KB
1202 OPTIONAL VIDEO_ More efficient ways to get the statistics.mp4	17.8 MB
1202 OPTIONAL VIDEO_ More efficient ways to get the statistics.srt	18.7 KB
1203 Restricted Boltzmann Machines.mp4	13.3 MB
1203 Restricted Boltzmann Machines.srt	13.9 KB
1204 An example of RBM learning.mp4	9.1 MB
1204 An example of RBM learning.srt	10.1 KB
1205 RBMs for collaborative filtering.mp4	10.0 MB
1205 RBMs for collaborative filtering.srt	10.9 KB
1301 The ups and downs of back propagation.mp4	12.4 MB
1301 The ups and downs of back propagation.srt	14.0 KB
1302 Belief Nets.mp4	15.6 MB
1302 Belief Nets.srt	17.8 KB
1303 Learning sigmoid belief nets.mp4	14.9 MB
1303 Learning sigmoid belief nets.srt	15.0 KB
1304 The wake-sleep algorithm.mp4	16.4 MB
1304 The wake-sleep algorithm.srt	17.8 KB
1401 Learning layers of features by stacking RBMs.mp4	21.0 MB
1401 Learning layers of features by stacking RBMs.srt	23.4 KB
1402 Discriminative learning for DBNs.mp4	11.8 MB
1402 Discriminative learning for DBNs.srt	13.0 KB
1403 What happens during discriminative fine-tuning_.mp4	10.7 MB
1403 What happens during discriminative fine-tuning_.srt	10.9 KB
1404 Modeling real-valued data with an RBM.mp4	11.7 MB
1404 Modeling real-valued data with an RBM.srt	12.4 KB
1405 OPTIONAL VIDEO_ RBMs are infinite sigmoid belief nets.mp4	20.4 MB
1405 OPTIONAL VIDEO_ RBMs are infinite sigmoid belief nets.srt	22.2 KB
1501 From PCA to autoencoders.mp4	10.2 MB
1501 From PCA to autoencoders.srt	10.5 KB
1502 Deep auto encoders.mp4	5.2 MB
1502 Deep auto encoders.srt	5.5 KB
1503 Deep auto encoders for document retrieval.mp4	10.7 MB
1503 Deep auto encoders for document retrieval.srt	10.8 KB
1504 Semantic Hashing.mp4	11.5 MB
1504 Semantic Hashing.srt	11.6 KB
1505 Learning binary codes for image retrieval.mp4	12.1 MB
1505 Learning binary codes for image retrieval.srt	13.2 KB
1506 Shallow autoencoders for pre-training.mp4	8.7 MB
1506 Shallow autoencoders for pre-training.srt	10.3 KB
1601 OPTIONAL_ Learning a joint model of images and captions.mp4	14.5 MB
1601 OPTIONAL_ Learning a joint model of images and captions.srt	10.6 KB
1602 OPTIONAL_ Hierarchical Coordinate Frames.mp4	11.7 MB
1602 OPTIONAL_ Hierarchical Coordinate Frames.srt	13.7 KB
1603 OPTIONAL_ Bayesian optimization of hyper-parameters.mp4	16.6 MB
1603 OPTIONAL_ Bayesian optimization of hyper-parameters.srt	19.0 KB
1604 OPTIONAL_ The fog of progress.mp4	2.9 MB
1604 OPTIONAL_ The fog of progress.srt	3.6 KB
Total files 243