Deep Learning A-Z™ Hands-On Artificial Neural Networks

磁力链接/BT种子名称

Deep Learning A-Z™ Hands-On Artificial Neural Networks

磁力链接/BT种子简介

种子哈希：8eb880fe918ea42c5d71fafb0323889c5f62dbc4
文件大小： 3.15G
已经下载：2921次
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收录时间：2017-07-27
最近下载：2025-01-02

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文件列表

  • 1151632 - 105 - Building a Boltzmann Machine - Step 4.mp4 67.7 MB
  • 1151632 - 079 - Reading an Advanced SOM.mp4 64.9 MB
  • 1151632 - 114 - Building a Boltzmann Machine - Step 13.mp4 61.4 MB
  • 1151632 - 025 - Evaluating the ANN.mp4 58.5 MB
  • 1151632 - 115 - Building a Boltzmann Machine - Step 14.mp4 56.7 MB
  • 1151632 - 054 - Practical intuition.mp4 55.4 MB
  • 1151632 - 133 - Building an AutoEncoder - Step 6.mp4 54.7 MB
  • 1151632 - 027 - Tuning the ANN.mp4 53.2 MB
  • 1151632 - 131 - Building an AutoEncoder - Step 4.mp4 52.0 MB
  • 1151632 - 089 - Mega Case Study - Step 3.mp4 51.6 MB
  • 1151632 - 047 - Building a CNN - Step 9.mp4 49.1 MB
  • 1151632 - 053 - LSTMs.mp4 48.2 MB
  • 1151632 - 015 - Building an ANN - Step 2.mp4 48.1 MB
  • 1151632 - 034 - Step 4 - Full Connection.mp4 44.8 MB
  • 1151632 - 154 - Logistic Regression Implementation - Step 5.mp4 44.5 MB
  • 1151632 - 113 - Building a Boltzmann Machine - Step 12.mp4 43.6 MB
  • 1151632 - 049 - Homework Solution.mp4 42.9 MB
  • 1151632 - 032 - Step 2 - Pooling.mp4 42.2 MB
  • 1151632 - 109 - Building a Boltzmann Machine - Step 8.mp4 41.3 MB
  • 1151632 - 095 - Restricted Boltzmann Machine.mp4 41.2 MB
  • 1151632 - 024 - Homework Solution.mp4 39.5 MB
  • 1151632 - 051 - The idea behind Recurrent Neural Networks.mp4 39.1 MB
  • 1151632 - 128 - Building an AutoEncoder - Step 1.mp4 38.5 MB
  • 1151632 - 085 - Building a SOM - Step 3.mp4 37.8 MB
  • 1151632 - 101 - Building a Boltzmann Machine - Introduction.mp4 35.7 MB
  • 1151632 - 135 - Building an AutoEncoder - Step 8.mp4 35.5 MB
  • 1151632 - 134 - Building an AutoEncoder - Step 7.mp4 35.3 MB
  • 1151632 - 036 - Softmax & Cross-Entropy.mp4 34.8 MB
  • 1151632 - 111 - Building a Boltzmann Machine - Step 10.mp4 34.8 MB
  • 1151632 - 092 - Boltzmann Machine.mp4 33.5 MB
  • 1151632 - 136 - Building an AutoEncoder - Step 9.mp4 33.1 MB
  • 1151632 - 001 - What is Deep Learning .mp4 32.8 MB
  • 1151632 - 108 - Building a Boltzmann Machine - Step 7.mp4 32.7 MB
  • 1151632 - 076 - How do Self-Organizing Maps Learn (Part 1).mp4 32.6 MB
  • 1151632 - 030 - Step 1 - Convolution Operation.mp4 32.5 MB
  • 1151632 - 090 - Mega Case Study - Step 4.mp4 32.3 MB
  • 1151632 - 083 - Building a SOM - Step 1.mp4 32.2 MB
  • 1151632 - 102 - Building a Boltzmann Machine - Step 1.mp4 31.9 MB
  • 1151632 - 058 - Building a RNN - Step 1.mp4 31.8 MB
  • 1151632 - 018 - Building an ANN - Step 5.mp4 31.0 MB
  • 1151632 - 005 - The Neuron.mp4 31.0 MB
  • 1151632 - 103 - Building a Boltzmann Machine - Step 2.mp4 31.0 MB
  • 1151632 - 096 - Contrastive Divergence.mp4 31.0 MB
  • 1151632 - 029 - What are convolutional neural networks .mp4 30.9 MB
  • 1151632 - 142 - Logistic Regression Intuition.mp4 30.6 MB
  • 1151632 - 052 - The Vanishing Gradient Problem.mp4 30.4 MB
  • 1151632 - 146 - Data Preprocessing - Step 4.mp4 30.4 MB
  • 1151632 - 086 - Building a SOM - Step 4.mp4 30.1 MB
  • 1151632 - 138 - Building an AutoEncoder - Step 11.mp4 29.7 MB
  • 1151632 - 117 - Auto Encoders.mp4 29.6 MB
  • 1151632 - 129 - Building an AutoEncoder - Step 2.mp4 29.2 MB
  • 1151632 - 094 - Editing Wikipedia - Our Contribution to the World.mp4 28.7 MB
  • 1151632 - 042 - Building a CNN - Step 4.mp4 28.5 MB
  • 1151632 - 008 - How do Neural Networks learn .mp4 27.8 MB
  • 1151632 - 104 - Building a Boltzmann Machine - Step 3.mp4 27.2 MB
  • 1151632 - 107 - Building a Boltzmann Machine - Step 6.mp4 26.4 MB
  • 1151632 - 075 - K-Means Clustering (Refresher).mp4 26.2 MB
  • 1151632 - 014 - Building an ANN - Step 1.mp4 25.5 MB
  • 1151632 - 007 - How do Neural Networks work .mp4 24.7 MB
  • 1151632 - 147 - Data Preprocessing - Step 5.mp4 24.0 MB
  • 1151632 - 148 - Data Preprocessing - Step 6.mp4 23.9 MB
  • 1151632 - 112 - Building a Boltzmann Machine - Step 11.mp4 23.5 MB
  • 1151632 - 081 - EXTRA K-means Clustering (part 3).mp4 22.9 MB
  • 1151632 - 145 - Data Preprocessing - Step 3.mp4 22.8 MB
  • 1151632 - 048 - Building a CNN - Step 10.mp4 21.5 MB
  • 1151632 - 110 - Building a Boltzmann Machine - Step 9.mp4 21.4 MB
  • 1151632 - 002 - Installing Python.mp4 21.4 MB
  • 1151632 - 130 - Building an AutoEncoder - Step 3.mp4 21.1 MB
  • 1151632 - 073 - How do Self-Organizing Maps Work .mp4 21.0 MB
  • 1151632 - 026 - Improving the ANN.mp4 20.8 MB
  • 1151632 - 084 - Building a SOM - Step 2.mp4 20.4 MB
  • 1151632 - 039 - Building a CNN - Step 1.mp4 20.1 MB
  • 1151632 - 068 - Building a RNN - Step 11.mp4 19.9 MB
  • 1151632 - 077 - How do Self-Organizing Maps Learn (Part 2).mp4 19.6 MB
  • 1151632 - 078 - Live SOM example.mp4 19.4 MB
  • 1151632 - 009 - Gradient Descent.mp4 19.4 MB
  • 1151632 - 093 - Energy-Based Models (EBM).mp4 19.4 MB
  • 1151632 - 056 - Ethical Disclosure.mp4 19.2 MB
  • 1151632 - 021 - Building an ANN - Step 8.mp4 19.1 MB
  • 1151632 - 069 - Building a RNN - Step 12.mp4 19.1 MB
  • 1151632 - 023 - Building an ANN - Step 10.mp4 18.3 MB
  • 1151632 - 022 - Building an ANN - Step 9.mp4 17.7 MB
  • 1151632 - 010 - Stochastic Gradient Descent.mp4 17.6 MB
  • 1151632 - 013 - Business Problem Description.mp4 17.2 MB
  • 1151632 - 144 - Data Preprocessing - Step 2.mp4 16.6 MB
  • 1151632 - 106 - Building a Boltzmann Machine - Step 5.mp4 16.2 MB
  • 1151632 - 070 - Homework Solution.mp4 15.7 MB
  • 1151632 - 006 - The Activation Function.mp4 15.5 MB
  • 1151632 - 031 - Step 1(b) - ReLU Layer.mp4 14.8 MB
  • 1151632 - 121 - Sparse Autoencoders.mp4 14.7 MB
  • 1151632 - 119 - Training an Auto Encoder.mp4 14.2 MB
  • 1151632 - 088 - Mega Case Study - Step 2.mp4 14.0 MB
  • 1151632 - 143 - Data Preprocessing - Step 1.mp4 13.9 MB
  • 1151632 - 071 - Evaluating the RNN.mp4 13.8 MB
  • 1151632 - 097 - Deep Belief Networks.mp4 13.2 MB
  • 1151632 - 045 - Building a CNN - Step 7.mp4 13.2 MB
  • 1151632 - 080 - EXTRA K-means Clustering (part 2).mp4 12.9 MB
  • 1151632 - 150 - Logistic Regression Implementation - Step 1.mp4 12.8 MB
  • 1151632 - 061 - Building a RNN - Step 4.mp4 12.6 MB
  • 1151632 - 132 - Building an AutoEncoder - Step 5.mp4 12.4 MB
  • 1151632 - 155 - Classification Template.mp4 12.3 MB
  • 1151632 - 137 - Building an AutoEncoder - Step 10.mp4 11.8 MB
  • 1151632 - 063 - Building a RNN - Step 6.mp4 11.7 MB
  • 1151632 - 011 - Backpropagation.mp4 11.5 MB
  • 1151632 - 043 - Building a CNN - Step 5.mp4 10.4 MB
  • 1151632 - 044 - Building a CNN - Step 6.mp4 10.2 MB
  • 1151632 - 153 - Logistic Regression Implementation - Step 4.mp4 10.1 MB
  • 1151632 - 139 - Simple Linear Regression Intuition - Step 1.mp4 9.9 MB
  • 1151632 - 060 - Building a RNN - Step 3.mp4 9.6 MB
  • 1151632 - 020 - Building an ANN - Step 7.mp4 9.4 MB
  • 1151632 - 016 - Building an ANN - Step 3.mp4 8.8 MB
  • 1151632 - 151 - Logistic Regression Implementation - Step 2.mp4 8.5 MB
  • 1151632 - 149 - Data Preprocessing Template.mp4 8.5 MB
  • 1151632 - 059 - Building a RNN - Step 2.mp4 8.4 MB
  • 1151632 - 066 - Building a RNN - Step 9.mp4 8.3 MB
  • 1151632 - 035 - Summary.mp4 8.3 MB
  • 1151632 - 038 - Introduction to CNNs.mp4 8.2 MB
  • 1151632 - 065 - Building a RNN - Step 8.mp4 8.1 MB
  • 1151632 - 120 - Overcomplete hidden layers.mp4 8.0 MB
  • 1151632 - 055 - EXTRA LSTM Variations.mp4 7.7 MB
  • 1151632 - 019 - Building an ANN - Step 6.mp4 7.4 MB
  • 1151632 - 062 - Building a RNN - Step 5.mp4 7.2 MB
  • 1151632 - 046 - Building a CNN - Step 8.mp4 7.1 MB
  • 1151632 - 067 - Building a RNN - Step 10.mp4 7.0 MB
  • 1151632 - 126 - How to get the dataset.mp4 6.8 MB
  • 1151632 - 057 - How to get the dataset.mp4 6.8 MB
  • 1151632 - 099 - How to get the dataset.mp4 6.8 MB
  • 1151632 - 003 - How to get the dataset.mp4 6.8 MB
  • 1151632 - 082 - How to get the dataset.mp4 6.8 MB
  • 1151632 - 037 - How to get the dataset.mp4 6.8 MB
  • 1151632 - 012 - How to get the dataset.mp4 6.8 MB
  • 1151632 - 152 - Logistic Regression Implementation - Step 3.mp4 6.2 MB
  • 1151632 - 017 - Building an ANN - Step 4.mp4 6.2 MB
  • 1151632 - 028 - Plan of attack.mp4 6.2 MB
  • 1151632 - 040 - Building a CNN - Step 2.mp4 6.1 MB
  • 1151632 - 098 - Deep Boltzmann Machines.mp4 6.1 MB
  • 1151632 - 122 - Denoising Autoencoders.mp4 6.0 MB
  • 1151632 - 100 - Installing PyTorch.mp4 6.0 MB
  • 1151632 - 127 - Installing PyTorch.mp4 6.0 MB
  • 1151632 - 087 - Mega Case Study - Step 1.mp4 5.7 MB
  • 1151632 - 140 - Simple Linear Regression Intuition - Step 2.mp4 5.6 MB
  • 1151632 - 123 - Contractive Autoencoders.mp4 5.5 MB
  • 1151632 - 072 - Plan of attack.mp4 5.4 MB
  • 1151632 - 004 - Plan of Attack.mp4 5.0 MB
  • 1151632 - 124 - Stacked Autoencoders.mp4 4.7 MB
  • 1151632 - 050 - Plan of attack.mp4 4.4 MB
  • 1151632 - 064 - Building a RNN - Step 7.mp4 4.4 MB
  • 1151632 - 116 - Plan of attack.mp4 4.3 MB
  • 1151632 - 074 - Why revisit K-Means .mp4 4.2 MB
  • 1151632 - 091 - Plan of attack.mp4 4.0 MB
  • 1151632 - 125 - Deep Autoencoders.mp4 3.5 MB
  • 1151632 - 033 - Step 3 - Flattening.mp4 3.4 MB
  • 1151632 - 118 - A Note on Biases.mp4 2.6 MB
  • 1151632 - 041 - Building a CNN - Step 3.mp4 2.3 MB
  • 1151632 - 141 - Multiple Linear Regression Intuition.mp4 1.9 MB

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