Neural Networks for Machine Learning

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    Info/0304 reading_list-Learning representations by back-propagating errors.pdf 2.95 MB
    Info/0404 reading_list-Neural probabilisic language models.pdf 136.81 KB
    Info/0405 images-Lecture4-turian.png 150.8 KB
    Info/0504 reading_list-Convolutional networks for images, speech, and time series.pdf 122.42 KB
    Info/0504 reading_list-Gradient-based learning applied to document recognition.pdf 932.67 KB
    Info/0705 reading_list-A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks.pdf 313.08 KB
    Info/0803 reading_list-Generating Text with Recurrent Neural Networks.pdf 266.99 KB
    Info/0804 Echo state network - Scholarpedia.htm 311.39 KB
    Info/1002 reading_list-Adaptive mixtures of local experts.pdf 264.76 KB
    Info/1005 reading_list-Improving neural networks by preventing co-adaptation of feature detectors.pdf 1.59 MB
    Info/1105 Boltzmann machine - Scholarpedia.htm 289 KB
    Info/1303 reading_list-Connectionist learning of belief networks.pdf 2.3 MB
    Info/1304 reading_list-- algorithm for unsupervised neural networks.pdf 255.35 KB
    Info/1401 reading_list-A fast learning algorithm for deep belief nets.pdf 769.31 KB
    Info/1401 reading_list-Self-taught learning- transfer learning from unlabeled data.pdf 473.52 KB
    Info/1401 reading_list-To recognize shapes, first learn to generate images.pdf 501.86 KB
    Info/1504 reading_list-Semantic Hashing.pdf 626.56 KB
    Info/1505 reading_list-Using Very Deep Autoencoders for Content-Based Image Retrieval.pdf 741.42 KB
    Slides/lecture_slides-lec1.pdf 3.88 MB
    Slides/lecture_slides-lec10.pdf 827.25 KB
    Slides/lecture_slides-lec11.pdf 694.52 KB
    Slides/lecture_slides-lec12.pdf 1.74 MB
    Slides/lecture_slides-lec13.pdf 307.25 KB
    Slides/lecture_slides-lec14.pdf 1.11 MB
    Slides/lecture_slides-lec15.pdf 2.49 MB
    Slides/lecture_slides-lec16.pdf 338.74 KB
    Slides/lecture_slides-lec2.pdf 492.95 KB
    Slides/lecture_slides-lec3.pdf 535.19 KB
    Slides/lecture_slides-lec4.pdf 941.48 KB
    Slides/lecture_slides-lec5.pdf 1.56 MB
    Slides/lecture_slides-lec6.pdf 534.03 KB
    Slides/lecture_slides-lec7.pdf 953.08 KB
    Slides/lecture_slides-lec8.pdf 642.88 KB
    Slides/lecture_slides-lec9.pdf 702.13 KB
    0101 Why do we need machine learning_.mp4 15.05 MB
    0101 Why do we need machine learning_.srt 18.34 KB
    0102 What are neural networks_.mp4 9.76 MB
    0102 What are neural networks_.srt 11.51 KB
    0103 Some simple models of neurons.mp4 9.26 MB
    0103 Some simple models of neurons.srt 10.7 KB
    0104 A simple example of learning.mp4 6.57 MB
    0104 A simple example of learning.srt 7.02 KB
    0105 Three types of learning.mp4 8.96 MB
    0105 Three types of learning.srt 10.39 KB
    0201 Types of neural network architectures.mp4 8.78 MB
    0201 Types of neural network architectures.srt 9.85 KB
    0202 Perceptrons_ The first generation of neural networks.mp4 9.78 MB
    0202 Perceptrons_ The first generation of neural networks.srt 10.86 KB
    0203 A geometrical view of perceptrons.mp4 7.32 MB
    0203 A geometrical view of perceptrons.srt 8.29 KB
    0204 Why the learning works.mp4 5.9 MB
    0204 Why the learning works.srt 6.4 KB
    0205 What perceptrons can_t do.mp4 16.57 MB
    0205 What perceptrons can_t do.srt 18.5 KB
    0301 Learning the weights of a linear neuron.mp4 13.52 MB
    0301 Learning the weights of a linear neuron.srt 15.09 KB
    0302 The error surface for a linear neuron.mp4 5.89 MB
    0302 The error surface for a linear neuron.srt 6.3 KB
    0303 Learning the weights of a logistic output neuron.mp4 4.37 MB
    0303 Learning the weights of a logistic output neuron.srt 4.46 KB
    0304 The backpropagation algorithm.mp4 13.35 MB
    0304 The backpropagation algorithm.srt 14.87 KB
    0305 Using the derivatives computed by backpropagation.mp4 11.15 MB
    0305 Using the derivatives computed by backpropagation.srt 13.58 KB
    0401 Learning to predict the next word.mp4 14.28 MB
    0401 Learning to predict the next word.srt 16.48 KB
    0402 A brief diversion into cognitive science.mp4 5.31 MB
    0402 A brief diversion into cognitive science.srt 5.76 KB
    0403 Another diversion_ The softmax output function.mp4 8.03 MB
    0403 Another diversion_ The softmax output function.srt 9.07 KB
    0404 Neuro-probabilistic language models.mp4 8.93 MB
    0404 Neuro-probabilistic language models.srt 10.71 KB
    0405 Ways to deal with the large number of possible outputs.mp4 14.26 MB
    0405 Ways to deal with the large number of possible outputs.srt 18.12 KB
    0501 Why object recognition is difficult.mp4 5.37 MB
    0501 Why object recognition is difficult.srt 6.16 KB
    0502 Achieving viewpoint invariance.mp4 6.89 MB
    0502 Achieving viewpoint invariance.srt 8.11 KB
    0503 Convolutional nets for digit recognition.mp4 18.46 MB
    0503 Convolutional nets for digit recognition.srt 21.54 KB
    0504 Convolutional nets for object recognition.mp4 23.03 MB
    0504 Convolutional nets for object recognition.srt 25.63 KB
    0601 Overview of mini-batch gradient descent.mp4 9.6 MB
    0601 Overview of mini-batch gradient descent.srt 11.95 KB
    0602 A bag of tricks for mini-batch gradient descent.mp4 14.9 MB
    0602 A bag of tricks for mini-batch gradient descent.srt 18.77 KB
    0603 The momentum method.mp4 9.74 MB
    0603 The momentum method.srt 11.14 KB
    0604 Adaptive learning rates for each connection.mp4 6.63 MB
    0604 Adaptive learning rates for each connection.srt 7.73 KB
    0605 Rmsprop_ Divide the gradient by a running average of its recent magnitude.mp4 15.12 MB
    0605 Rmsprop_ Divide the gradient by a running average of its recent magnitude.srt 15.69 KB
    0701 Modeling sequences_ A brief overview.mp4 20.13 MB
    0701 Modeling sequences_ A brief overview.srt 22.65 KB
    0702 Training RNNs with back propagation.mp4 7.33 MB
    0702 Training RNNs with back propagation.srt 8.37 KB
    0703 A toy example of training an RNN.mp4 7.24 MB
    0703 A toy example of training an RNN.srt 7.52 KB
    0704 Why it is difficult to train an RNN.mp4 8.89 MB
    0704 Why it is difficult to train an RNN.srt 9.79 KB
    0705 Long-term Short-term-memory.mp4 10.23 MB
    0705 Long-term Short-term-memory.srt 11.62 KB
    0801 A brief overview of Hessian Free optimization.mp4 16.24 MB
    0801 A brief overview of Hessian Free optimization.srt 17.95 KB
    0802 Modeling character strings with multiplicative connections.mp4 16.56 MB
    0802 Modeling character strings with multiplicative connections.srt 17.49 KB
    0803 Learning to predict the next character using HF.mp4 13.92 MB
    0803 Learning to predict the next character using HF.srt 15.74 KB
    0804 Echo State Networks.mp4 11.28 MB
    0804 Echo State Networks.srt 11.98 KB
    0901 Overview of ways to improve generalization.mp4 13.57 MB
    0901 Overview of ways to improve generalization.srt 15.8 KB
    0902 Limiting the size of the weights.mp4 7.36 MB
    0902 Limiting the size of the weights.srt 8.41 KB
    0903 Using noise as a regularizer.mp4 8.48 MB
    0903 Using noise as a regularizer.srt 8.87 KB
    0904 Introduction to the full Bayesian approach.mp4 12 MB
    0904 Introduction to the full Bayesian approach.srt 13.18 KB
    0905 The Bayesian interpretation of weight decay.mp4 12.27 MB
    0905 The Bayesian interpretation of weight decay.srt 13.02 KB
    0906 MacKay_s quick and dirty method of setting weight costs.mp4 4.37 MB
    0906 MacKay_s quick and dirty method of setting weight costs.srt 4.41 KB
    1001 Why it helps to combine models.mp4 15.12 MB
    1001 Why it helps to combine models.srt 17.68 KB
    1002 Mixtures of Experts.mp4 14.98 MB
    1002 Mixtures of Experts.srt 17.06 KB
    1003 The idea of full Bayesian learning.mp4 8.39 MB
    1003 The idea of full Bayesian learning.srt 10.28 KB
    1004 Making full Bayesian learning practical.mp4 8.13 MB
    1004 Making full Bayesian learning practical.srt 8.46 KB
    1005 Dropout.mp4 9.69 MB
    1005 Dropout.srt 11.69 KB
    1101 Hopfield Nets.mp4 14.65 MB
    1101 Hopfield Nets.srt 16.36 KB
    1102 Dealing with spurious minima.mp4 12.77 MB
    1102 Dealing with spurious minima.srt 14.85 KB
    1103 Hopfield nets with hidden units.mp4 11.31 MB
    1103 Hopfield nets with hidden units.srt 12.29 KB
    1104 Using stochastic units to improv search.mp4 11.76 MB
    1104 Using stochastic units to improv search.srt 13.99 KB
    1105 How a Boltzmann machine models data.mp4 13.28 MB
    1105 How a Boltzmann machine models data.srt 15.89 KB
    1201 Boltzmann machine learning.mp4 14.03 MB
    1201 Boltzmann machine learning.srt 16.02 KB
    1202 OPTIONAL VIDEO_ More efficient ways to get the statistics.mp4 16.93 MB
    1202 OPTIONAL VIDEO_ More efficient ways to get the statistics.srt 18.21 KB
    1203 Restricted Boltzmann Machines.mp4 12.68 MB
    1203 Restricted Boltzmann Machines.srt 13.59 KB
    1204 An example of RBM learning.mp4 8.71 MB
    1204 An example of RBM learning.srt 9.87 KB
    1205 RBMs for collaborative filtering.mp4 9.53 MB
    1205 RBMs for collaborative filtering.srt 10.67 KB
    1301 The ups and downs of back propagation.mp4 11.83 MB
    1301 The ups and downs of back propagation.srt 13.64 KB
    1302 Belief Nets.mp4 14.86 MB
    1302 Belief Nets.srt 17.35 KB
    1303 Learning sigmoid belief nets.mp4 14.19 MB
    1303 Learning sigmoid belief nets.srt 14.61 KB
    1304 The wake-sleep algorithm.mp4 15.68 MB
    1304 The wake-sleep algorithm.srt 17.4 KB
    1401 Learning layers of features by stacking RBMs.mp4 20.07 MB
    1401 Learning layers of features by stacking RBMs.srt 22.81 KB
    1402 Discriminative learning for DBNs.mp4 11.29 MB
    1402 Discriminative learning for DBNs.srt 12.74 KB
    1403 What happens during discriminative fine-tuning_.mp4 10.17 MB
    1403 What happens during discriminative fine-tuning_.srt 10.66 KB
    1404 Modeling real-valued data with an RBM.mp4 11.2 MB
    1404 Modeling real-valued data with an RBM.srt 12.15 KB
    1405 OPTIONAL VIDEO_ RBMs are infinite sigmoid belief nets.mp4 19.44 MB
    1405 OPTIONAL VIDEO_ RBMs are infinite sigmoid belief nets.srt 21.63 KB
    1501 From PCA to autoencoders.mp4 9.68 MB
    1501 From PCA to autoencoders.srt 10.26 KB
    1502 Deep auto encoders.mp4 4.92 MB
    1502 Deep auto encoders.srt 5.35 KB
    1503 Deep auto encoders for document retrieval.mp4 10.25 MB
    1503 Deep auto encoders for document retrieval.srt 10.52 KB
    1504 Semantic Hashing.mp4 10.97 MB
    1504 Semantic Hashing.srt 11.32 KB
    1505 Learning binary codes for image retrieval.mp4 11.51 MB
    1505 Learning binary codes for image retrieval.srt 12.89 KB
    1506 Shallow autoencoders for pre-training.mp4 8.25 MB
    1506 Shallow autoencoders for pre-training.srt 10.05 KB
    1601 OPTIONAL_ Learning a joint model of images and captions.mp4 13.83 MB
    1601 OPTIONAL_ Learning a joint model of images and captions.srt 10.31 KB
    1602 OPTIONAL_ Hierarchical Coordinate Frames.mp4 11.16 MB
    1602 OPTIONAL_ Hierarchical Coordinate Frames.srt 13.34 KB
    1603 OPTIONAL_ Bayesian optimization of hyper-parameters.mp4 15.8 MB
    1603 OPTIONAL_ Bayesian optimization of hyper-parameters.srt 18.54 KB
    1604 OPTIONAL_ The fog of progress.mp4 2.78 MB
    1604 OPTIONAL_ The fog of progress.srt 3.49 KB

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