Neural Networks for Machine Learning

磁力链接/BT种子名称

Neural Networks for Machine Learning

磁力链接/BT种子简介

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文件大小：919.67M
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收录时间：2017-02-08
最近下载：2025-10-01

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文件列表

  • 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 15.4 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 18.6 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
  • Info/0304 reading_list-Learning representations by back-propagating errors.pdf 3.1 MB
  • Info/0404 reading_list-Neural probabilisic language models.pdf 140.1 kB
  • Info/0405 images-Lecture4-turian.png 154.4 kB
  • Info/0504 reading_list-Convolutional networks for images, speech, and time series.pdf 125.4 kB
  • Info/0504 reading_list-Gradient-based learning applied to document recognition.pdf 955.1 kB
  • Info/0705 reading_list-A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks.pdf 320.6 kB
  • Info/0803 reading_list-Generating Text with Recurrent Neural Networks.pdf 273.4 kB
  • Info/0804 Echo state network - Scholarpedia.htm 318.9 kB
  • Info/0804 Echo state network - Scholarpedia_files/1088796616-postmessagerelay.js 5.1 kB
  • Info/0804 Echo state network - Scholarpedia_files/400px-FreqGenTestOverlay.png 39.9 kB
  • Info/0804 Echo state network - Scholarpedia_files/500px-FreqGenSchema.png 73.5 kB
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  • Info/0804 Echo state network - Scholarpedia_files/cb=gapi.loaded_1 51.4 kB
  • Info/0804 Echo state network - Scholarpedia_files/core-rpc-shindig.random-shindig.sha1.js 68.1 kB
  • Info/0804 Echo state network - Scholarpedia_files/facebook.png 540 Bytes
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  • Info/0804 Echo state network - Scholarpedia_files/gplus-16.png 492 Bytes
  • Info/0804 Echo state network - Scholarpedia_files/linkedin.png 636 Bytes
  • Info/0804 Echo state network - Scholarpedia_files/load(1).php 154.2 kB
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  • Info/0804 Echo state network - Scholarpedia_files/load(3).php 428 Bytes
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  • Info/0804 Echo state network - Scholarpedia_files/load(6).php 152.2 kB
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  • Info/0804 Echo state network - Scholarpedia_files/magnify-clip.png 204 Bytes
  • Info/0804 Echo state network - Scholarpedia_files/MathJax.js 58.8 kB
  • Info/0804 Echo state network - Scholarpedia_files/photo.jpg 356 Bytes
  • Info/0804 Echo state network - Scholarpedia_files/plusone.js 33.6 kB
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  • Info/0804 Echo state network - Scholarpedia_files/search-ltr.png 595 Bytes
  • Info/0804 Echo state network - Scholarpedia_files/twitter.png 43.4 kB
  • Info/1002 reading_list-Adaptive mixtures of local experts.pdf 271.1 kB
  • Info/1005 reading_list-Improving neural networks by preventing co-adaptation of feature detectors.pdf 1.7 MB
  • Info/1105 Boltzmann machine - Scholarpedia.htm 295.9 kB
  • Info/1105 Boltzmann machine - Scholarpedia_files/1088796616-postmessagerelay.js 5.1 kB
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  • Info/1105 Boltzmann machine - Scholarpedia_files/facebook.png 540 Bytes
  • Info/1105 Boltzmann machine - Scholarpedia_files/fastbutton.htm 47.4 kB
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  • Info/1105 Boltzmann machine - Scholarpedia_files/gplus-16.png 492 Bytes
  • Info/1105 Boltzmann machine - Scholarpedia_files/linkedin.png 636 Bytes
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  • Info/1105 Boltzmann machine - Scholarpedia_files/twitter.png 43.4 kB
  • Info/1303 reading_list-Connectionist learning of belief networks.pdf 2.4 MB
  • Info/1304 reading_list-- algorithm for unsupervised neural networks.pdf 261.5 kB
  • Info/1401 reading_list-A fast learning algorithm for deep belief nets.pdf 787.8 kB
  • Info/1401 reading_list-Self-taught learning- transfer learning from unlabeled data.pdf 484.9 kB
  • Info/1401 reading_list-To recognize shapes, first learn to generate images.pdf 513.9 kB
  • Info/1504 reading_list-Semantic Hashing.pdf 641.6 kB
  • Info/1505 reading_list-Using Very Deep Autoencoders for Content-Based Image Retrieval.pdf 759.2 kB
  • Slides/lecture_slides-lec1.pdf 4.1 MB
  • Slides/lecture_slides-lec10.pdf 847.1 kB
  • Slides/lecture_slides-lec11.pdf 711.2 kB
  • Slides/lecture_slides-lec12.pdf 1.8 MB
  • Slides/lecture_slides-lec13.pdf 314.6 kB
  • Slides/lecture_slides-lec14.pdf 1.2 MB
  • Slides/lecture_slides-lec15.pdf 2.6 MB
  • Slides/lecture_slides-lec16.pdf 346.9 kB
  • Slides/lecture_slides-lec2.pdf 504.8 kB
  • Slides/lecture_slides-lec3.pdf 548.0 kB
  • Slides/lecture_slides-lec4.pdf 964.1 kB
  • Slides/lecture_slides-lec5.pdf 1.6 MB
  • Slides/lecture_slides-lec6.pdf 546.8 kB
  • Slides/lecture_slides-lec7.pdf 976.0 kB
  • Slides/lecture_slides-lec8.pdf 658.3 kB
  • Slides/lecture_slides-lec9.pdf 719.0 kB

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