Neural Networks Video Lectures - Hugo Larochelle
Hugo Larochelle



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neural_nets_larochelle (92 files)
Neural networks [1.1] - Feedforward neural network - artificial neuron-SGZ6BttHMPw.mp458.87MB
Neural networks [1.2] - Feedforward neural network - activation function-tCHIkgWZLOQ.mp443.92MB
Neural networks [1.3] - Feedforward neural network - capacity of single neuron-iT5P4z6Fzj8.mp423.71MB
Neural networks [1.4] - Feedforward neural network - multilayer neural network-apPiZd-qnZ8.mp448.13MB
Neural networks [1.5] - Feedforward neural network - capacity of neural network-O4I7dQC4VtU.mp428.19MB
Neural networks [1.6] - Feedforward neural network - biological inspiration-cuJ4IC5_pGs.mp4116.10MB
Neural networks [2.1] - Training neural networks - empirical risk minimization-5adNQvSlF50.mp468.82MB
Neural networks [2.2] - Training neural networks - loss function-PpFTODTztsU.mp429.90MB
Neural networks [2.3] - Training neural networks - output layer gradient-1N837i4s1T8.mp446.51MB
Neural networks [2.4] - Training neural networks - hidden layer gradient-xFhM_Kwqw48.mp4104.17MB
Neural networks [2.5] - Training neural networks - activation function derivative-tf9p1xQbWNM.mp424.51MB
Neural networks [2.6] - Training neural networks - parameter gradient-p5tL2JqCRDo.mp432.73MB
Neural networks [2.7] - Training neural networks - backpropagation-_KoWTD8T45Q.mp496.36MB
Neural networks [2.8] - Training neural networks - regularization-JfkbyODyujw.mp482.17MB
Neural networks [2.9] - Training neural networks - parameter initialization-sLfogkzFNfc.mp450.95MB
Neural networks [2.10] - Training neural networks - model selection-Fs-raHUnF2M.mp469.13MB
Neural networks [2.11] - Training neural networks - optimization-Bver7Ttgb9M.mp4125.69MB
Neural networks [3.1] - Conditional random fields - motivation-GF3iSJkgPbA.mp428.44MB
Neural networks [3.2] - Conditional random fields - linear chain CRF-PGBlyKtfB74.mp462.18MB
Neural networks [3.3] - Conditional random fields - context window-G4lnHc2M1CA.mp477.40MB
Neural networks [3.4] - Conditional random fields - computing the partition function-fGdXkVv1qNQ.mp4142.98MB
Neural networks [3.5] - Conditional random fields - computing marginals-hjkwp-eDwt8.mp458.01MB
Neural networks [3.6] - Conditional random fields - performing classification-pQJvX9U-MyE.mp4102.82MB
Neural networks [3.7] - Conditional random fields - factors, sufficient statistics and linear CRF-uXV2an9TdJY.mp473.27MB
Neural networks [3.8] - Conditional random fields - Markov network-ZYUnyyVgtyA.mp471.66MB
Neural networks [3.9] - Conditional random fields - factor graph-Q5GTCHVsHXY.mp442.89MB
Neural networks [3.10] - Conditional random fields - belief propagation--z5lKPHcumo.mp4209.38MB
Neural networks [4.1] - Training CRFs - loss function-6dpGB60Q1Ts.mp435.50MB
Neural networks [4.2] - Training CRFs - unary log-factor gradient-fU2W7KRoS2U.mp475.03MB
Neural networks [4.3] - Training CRFs - pairwise log-factor gradient-1W2lkcGV2Zo.mp449.58MB
Neural networks [4.4] - Training CRFs - discriminative vs. generative learning-MD4mY3Zj5E4.mp456.66MB
Neural networks [4.5] - Training CRFs - maximum-entropy Markov model-aMi2xnYEwbc.mp474.48MB
Neural networks [4.6] - Training CRFs - hidden Markov model-jdlJfM707MM.mp436.46MB
Neural networks [4.7] - Training CRFs - general conditional random field-QY9k7tJistU.mp455.03MB
Neural networks [4.8] - Training CRFs - pseudolikelihood-ltRT1m7vaBU.mp443.70MB
Neural networks [5.1] - Restricted Boltzmann machine - definition-p4Vh_zMw-HQ.mp4101.69MB
Neural networks [5.2] - Restricted Boltzmann machine - inference-lekCh_i32iE.mp476.83MB
Neural networks [5.3] - Restricted Boltzmann machine - free energy-e0Ts_7Y6hZU.mp464.90MB
Neural networks [5.4] - Restricted Boltzmann machine - contrastive divergence-MD8qXWucJBY.mp482.87MB
Neural networks [5.5] - Restricted Boltzmann machine - contrastive divergence (parameter update)-wMb7cads0go.mp465.20MB
Neural networks [5.6] - Restricted Boltzmann machine - persistent CD-S0kFFiHzR8M.mp444.50MB
Neural networks [5.7] - Restricted Boltzmann machine - example-n26NdEtma8U.mp453.53MB
Neural networks [5.8] - Restricted Boltzmann machine - extensions-iPuqoQih9xk.mp455.75MB
Neural networks [6.1] - Autoencoder - definition-FzS3tMl4Nsc.mp439.23MB
Neural networks [6.2] - Autoencoder - loss function-xTU79Zs4XKY.mp473.12MB
Neural networks [6.3] - Autoencoder - example-6DO_jVbDP3I.mp418.76MB
Neural networks [6.4] - Autoencoder - linear autoencoder-xq-I0Rl8mt0.mp4166.49MB
Neural networks [6.5] - Autoencoder - undercomplete vs. overcomplete hidden layer-5rLgoM2Pkso.mp448.65MB
Neural networks [6.6] - Autoencoder - denoising autoencoder-t2NQ_c5BFOc.mp4124.20MB
Neural networks [6.7] - Autoencoder - contractive autoencoder-79sYlJ8Cvlc.mp473.93MB
Neural networks [7.1] - Deep learning - motivation-vXMpKYRhpmI.mp4100.49MB
Neural networks [7.2] - Deep learning - difficulty of training-YoiUlN_77LU.mp472.44MB
Neural networks [7.3] - Deep learning - unsupervised pre-training-Oq38pINmddk.mp486.50MB
Neural networks [7.4] - Deep learning - example-SXnG-lQ7RJo.mp485.69MB
Neural networks [7.5] - Deep learning - dropout-UcKPdAM8cnI.mp458.84MB
Neural networks [7.6] - Deep learning - deep autoencoder-z5ZYm_wJ37c.mp465.88MB
Neural networks [7.7] - Deep learning - deep belief network-vkb6AWYXZ5I.mp499.13MB
Neural networks [7.8] - Deep learning - variational bound-pStDscJh2Wo.mp489.04MB
Neural networks [7.9] - Deep learning - DBN pre-training-35MUlYCColk.mp4140.26MB
Neural networks [8.1] - Sparse coding - definition-7a0_iEruGoM.mp468.77MB
Neural networks [8.2] - Sparse coding - inference (ISTA algorithm)-L6qhzWWtqQs.mp472.97MB
Neural networks [8.3] - Sparse coding - dictionary update with projected gradient descent-bhqNSjJ_A20.mp422.61MB
Neural networks [8.4] - Sparse coding - dictionary update with block-coordinate descent-UMdNfhgPKTc.mp482.37MB
Neural networks [8.5] - Sparse coding - dictionary learning algorithm-PzNMff7cYjM.mp447.52MB
Neural networks [8.6] - Sparse coding - online dictionary learning algorithm-IePxTepLvQc.mp477.36MB
Neural networks [8.7] - Sparse coding - ZCA preprocessing-eUiwhV1QcQ4.mp450.63MB
Neural networks [8.8] - Sparse coding - feature extraction-FL81zSjAEEg.mp468.91MB
Neural networks [8.9] - relationship with V1-MdomgSiL86Q.mp449.68MB
Neural networks [9.1] - Computer vision - motivation-rxKrCa4bg1I.mp446.77MB
Neural networks [9.2] - Computer vision - local connectivity-vLf3KVe2Z1k.mp430.80MB
Neural networks [9.3] - Computer vision - parameter sharing-aAT1t9p7ShM.mp482.76MB
Neural networks [9.4] - Computer vision - discrete convolution-Y7TMwqAWEdo.mp486.37MB
Neural networks [9.5] - Computer vision - pooling and subsampling-I-JKxcpbRT4.mp452.43MB
Neural networks [9.6] - Computer vision - convolutional network-cDdpwAIsuD8.mp4116.76MB
Neural networks [9.7] - Computer vision - object recognition-eU83LSM3xnk.mp469.26MB
Neural networks [9.8] - Computer vision - example-Gk8VvSL3IMk.mp4124.16MB
Neural networks [9.9] - Computer vision - data set expansion-Km1Q5VcSKAg.mp451.28MB
Neural networks [9.10] - Computer vision - convolutional RBM-y0SISi_T6s8.mp471.47MB
Neural networks [10.1] - Natural language processing - motivation-OzZIOiMVUyM.mp419.74MB
Neural networks [10.2] - Natural language processing - preprocessing-jcrhYEYwO9k.mp460.48MB
Neural networks [10.3] - Natural language processing - one-hot encoding-iZ3e_cifP7Y.mp451.95MB
Neural networks [10.4] - Natural language processing - word representations-PKszi8iogak.mp471.48MB
Neural networks [10.5] - Natural language processing - language modeling-iGmHnICXDss.mp457.46MB
Neural networks [10.6] - Natural language processing - neural network language model-FoDz01QNSiY.mp4109.55MB
Neural networks [10.7] - Natural language processing - hierarchical output layer-B95LTf2rVWM.mp4117.45MB
Neural networks [10.8] - Natural language processing - word tagging-pCLIo4Z-PsM.mp492.46MB
Neural networks [10.9] - Natural language processing - convolutional network-6jCDUQ-e-fY.mp4108.85MB
Neural networks [10.10] - Natural language processing - multitask learning-ciNBQupWsAc.mp4136.05MB
Neural networks [10.11] - Natural language processing - recursive network-AqEF2HIMjYA.mp449.86MB
Neural networks [10.12] - Natural language processing - merging representations-gPNPINa7PaM.mp431.31MB
Neural networks [10.13] - Natural language processing - tree inference-NJozqoejJnA.mp4100.64MB
Neural networks [10.14] - Natural language processing - recursive network training-oRxgRHJztPI.mp487.35MB
Type: Course
Tags:

Bibtex:
@article{,
title= {Neural Networks Video Lectures - Hugo Larochelle},
journal= {},
author= {Hugo Larochelle},
year= {},
url= {http://info.usherbrooke.ca/hlarochelle/neural_networks/content.html},
abstract= {Here is the list of topics covered in the course, segmented over 10 weeks. Each week is associated with explanatory video clips and recommended readings.

0. Introduction and math revision
1. Feedforward neural network
2. Training neural networks
3. Conditional random fields 
4. Training CRFs
5. Restricted Boltzmann machine
6. Autoencoders
7. Deep learning
8. Sparse coding
9. Computer vision
10. Natural language processing


},
keywords= {},
terms= {}
}