neural_nets_larochelle (92 files)

Neural networks [1.1] - Feedforward neural network - artificial neuron-SGZ6BttHMPw.mp4 | 58.87MB |

Neural networks [1.2] - Feedforward neural network - activation function-tCHIkgWZLOQ.mp4 | 43.92MB |

Neural networks [1.3] - Feedforward neural network - capacity of single neuron-iT5P4z6Fzj8.mp4 | 23.71MB |

Neural networks [1.4] - Feedforward neural network - multilayer neural network-apPiZd-qnZ8.mp4 | 48.13MB |

Neural networks [1.5] - Feedforward neural network - capacity of neural network-O4I7dQC4VtU.mp4 | 28.19MB |

Neural networks [1.6] - Feedforward neural network - biological inspiration-cuJ4IC5_pGs.mp4 | 116.10MB |

Neural networks [2.1] - Training neural networks - empirical risk minimization-5adNQvSlF50.mp4 | 68.82MB |

Neural networks [2.2] - Training neural networks - loss function-PpFTODTztsU.mp4 | 29.90MB |

Neural networks [2.3] - Training neural networks - output layer gradient-1N837i4s1T8.mp4 | 46.51MB |

Neural networks [2.4] - Training neural networks - hidden layer gradient-xFhM_Kwqw48.mp4 | 104.17MB |

Neural networks [2.5] - Training neural networks - activation function derivative-tf9p1xQbWNM.mp4 | 24.51MB |

Neural networks [2.6] - Training neural networks - parameter gradient-p5tL2JqCRDo.mp4 | 32.73MB |

Neural networks [2.7] - Training neural networks - backpropagation-_KoWTD8T45Q.mp4 | 96.36MB |

Neural networks [2.8] - Training neural networks - regularization-JfkbyODyujw.mp4 | 82.17MB |

Neural networks [2.9] - Training neural networks - parameter initialization-sLfogkzFNfc.mp4 | 50.95MB |

Neural networks [2.10] - Training neural networks - model selection-Fs-raHUnF2M.mp4 | 69.13MB |

Neural networks [2.11] - Training neural networks - optimization-Bver7Ttgb9M.mp4 | 125.69MB |

Neural networks [3.1] - Conditional random fields - motivation-GF3iSJkgPbA.mp4 | 28.44MB |

Neural networks [3.2] - Conditional random fields - linear chain CRF-PGBlyKtfB74.mp4 | 62.18MB |

Neural networks [3.3] - Conditional random fields - context window-G4lnHc2M1CA.mp4 | 77.40MB |

Neural networks [3.4] - Conditional random fields - computing the partition function-fGdXkVv1qNQ.mp4 | 142.98MB |

Neural networks [3.5] - Conditional random fields - computing marginals-hjkwp-eDwt8.mp4 | 58.01MB |

Neural networks [3.6] - Conditional random fields - performing classification-pQJvX9U-MyE.mp4 | 102.82MB |

Neural networks [3.7] - Conditional random fields - factors, sufficient statistics and linear CRF-uXV2an9TdJY.mp4 | 73.27MB |

Neural networks [3.8] - Conditional random fields - Markov network-ZYUnyyVgtyA.mp4 | 71.66MB |

Neural networks [3.9] - Conditional random fields - factor graph-Q5GTCHVsHXY.mp4 | 42.89MB |

Neural networks [3.10] - Conditional random fields - belief propagation--z5lKPHcumo.mp4 | 209.38MB |

Neural networks [4.1] - Training CRFs - loss function-6dpGB60Q1Ts.mp4 | 35.50MB |

Neural networks [4.2] - Training CRFs - unary log-factor gradient-fU2W7KRoS2U.mp4 | 75.03MB |

Neural networks [4.3] - Training CRFs - pairwise log-factor gradient-1W2lkcGV2Zo.mp4 | 49.58MB |

Neural networks [4.4] - Training CRFs - discriminative vs. generative learning-MD4mY3Zj5E4.mp4 | 56.66MB |

Neural networks [4.5] - Training CRFs - maximum-entropy Markov model-aMi2xnYEwbc.mp4 | 74.48MB |

Neural networks [4.6] - Training CRFs - hidden Markov model-jdlJfM707MM.mp4 | 36.46MB |

Neural networks [4.7] - Training CRFs - general conditional random field-QY9k7tJistU.mp4 | 55.03MB |

Neural networks [4.8] - Training CRFs - pseudolikelihood-ltRT1m7vaBU.mp4 | 43.70MB |

Neural networks [5.1] - Restricted Boltzmann machine - definition-p4Vh_zMw-HQ.mp4 | 101.69MB |

Neural networks [5.2] - Restricted Boltzmann machine - inference-lekCh_i32iE.mp4 | 76.83MB |

Neural networks [5.3] - Restricted Boltzmann machine - free energy-e0Ts_7Y6hZU.mp4 | 64.90MB |

Neural networks [5.4] - Restricted Boltzmann machine - contrastive divergence-MD8qXWucJBY.mp4 | 82.87MB |

Neural networks [5.5] - Restricted Boltzmann machine - contrastive divergence (parameter update)-wMb7cads0go.mp4 | 65.20MB |

Neural networks [5.6] - Restricted Boltzmann machine - persistent CD-S0kFFiHzR8M.mp4 | 44.50MB |

Neural networks [5.7] - Restricted Boltzmann machine - example-n26NdEtma8U.mp4 | 53.53MB |

Neural networks [5.8] - Restricted Boltzmann machine - extensions-iPuqoQih9xk.mp4 | 55.75MB |

Neural networks [6.1] - Autoencoder - definition-FzS3tMl4Nsc.mp4 | 39.23MB |

Neural networks [6.2] - Autoencoder - loss function-xTU79Zs4XKY.mp4 | 73.12MB |

Neural networks [6.3] - Autoencoder - example-6DO_jVbDP3I.mp4 | 18.76MB |

Neural networks [6.4] - Autoencoder - linear autoencoder-xq-I0Rl8mt0.mp4 | 166.49MB |

Neural networks [6.5] - Autoencoder - undercomplete vs. overcomplete hidden layer-5rLgoM2Pkso.mp4 | 48.65MB |

Neural networks [6.6] - Autoencoder - denoising autoencoder-t2NQ_c5BFOc.mp4 | 124.20MB |

**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= {} }