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种子名称： Deep Learning A-Z™ Hands-On Artificial Neural Networks
文件类型： 视频
文件数目： 155个文件
文件大小： 3.15 GB
收录时间： 2017-8-6 11:09
已经下载： 3次
资源热度： 277
最近下载： 2024-9-28 16:10

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

1151632 - 105 - Building a Boltzmann Machine - Step 4.mp464.52MB

1151632 - 079 - Reading an Advanced SOM.mp461.87MB

1151632 - 114 - Building a Boltzmann Machine - Step 13.mp458.52MB

1151632 - 025 - Evaluating the ANN.mp455.84MB

1151632 - 115 - Building a Boltzmann Machine - Step 14.mp454.1MB

1151632 - 054 - Practical intuition.mp452.84MB

1151632 - 133 - Building an AutoEncoder - Step 6.mp452.13MB

1151632 - 027 - Tuning the ANN.mp450.76MB

1151632 - 131 - Building an AutoEncoder - Step 4.mp449.56MB

1151632 - 089 - Mega Case Study - Step 3.mp449.25MB

1151632 - 047 - Building a CNN - Step 9.mp446.87MB

1151632 - 053 - LSTMs.mp445.96MB

1151632 - 015 - Building an ANN - Step 2.mp445.86MB

1151632 - 034 - Step 4 - Full Connection.mp442.74MB

1151632 - 154 - Logistic Regression Implementation - Step 5.mp442.48MB

1151632 - 113 - Building a Boltzmann Machine - Step 12.mp441.57MB

1151632 - 049 - Homework Solution.mp440.96MB

1151632 - 032 - Step 2 - Pooling.mp440.24MB

1151632 - 109 - Building a Boltzmann Machine - Step 8.mp439.4MB

1151632 - 095 - Restricted Boltzmann Machine.mp439.25MB

1151632 - 024 - Homework Solution.mp437.62MB

1151632 - 051 - The idea behind Recurrent Neural Networks.mp437.3MB

1151632 - 128 - Building an AutoEncoder - Step 1.mp436.71MB

1151632 - 085 - Building a SOM - Step 3.mp436.03MB

1151632 - 101 - Building a Boltzmann Machine - Introduction.mp434.05MB

1151632 - 135 - Building an AutoEncoder - Step 8.mp433.83MB

1151632 - 134 - Building an AutoEncoder - Step 7.mp433.7MB

1151632 - 036 - Softmax & Cross-Entropy.mp433.23MB

1151632 - 111 - Building a Boltzmann Machine - Step 10.mp433.15MB

1151632 - 092 - Boltzmann Machine.mp431.92MB

1151632 - 136 - Building an AutoEncoder - Step 9.mp431.6MB

1151632 - 001 - What is Deep Learning .mp431.31MB

1151632 - 108 - Building a Boltzmann Machine - Step 7.mp431.19MB

1151632 - 076 - How do Self-Organizing Maps Learn (Part 1).mp431.1MB

1151632 - 030 - Step 1 - Convolution Operation.mp431.02MB

1151632 - 090 - Mega Case Study - Step 4.mp430.84MB

1151632 - 083 - Building a SOM - Step 1.mp430.66MB

1151632 - 102 - Building a Boltzmann Machine - Step 1.mp430.45MB

1151632 - 058 - Building a RNN - Step 1.mp430.32MB

1151632 - 018 - Building an ANN - Step 5.mp429.58MB

1151632 - 005 - The Neuron.mp429.57MB

1151632 - 103 - Building a Boltzmann Machine - Step 2.mp429.57MB

1151632 - 096 - Contrastive Divergence.mp429.55MB

1151632 - 029 - What are convolutional neural networks .mp429.5MB

1151632 - 142 - Logistic Regression Intuition.mp429.17MB

1151632 - 052 - The Vanishing Gradient Problem.mp429.01MB

1151632 - 146 - Data Preprocessing - Step 4.mp428.95MB

1151632 - 086 - Building a SOM - Step 4.mp428.73MB

1151632 - 138 - Building an AutoEncoder - Step 11.mp428.29MB

1151632 - 117 - Auto Encoders.mp428.19MB

1151632 - 129 - Building an AutoEncoder - Step 2.mp427.81MB

1151632 - 094 - Editing Wikipedia - Our Contribution to the World.mp427.33MB

1151632 - 042 - Building a CNN - Step 4.mp427.19MB

1151632 - 008 - How do Neural Networks learn .mp426.55MB

1151632 - 104 - Building a Boltzmann Machine - Step 3.mp425.95MB

1151632 - 107 - Building a Boltzmann Machine - Step 6.mp425.21MB

1151632 - 075 - K-Means Clustering (Refresher).mp425.01MB

1151632 - 014 - Building an ANN - Step 1.mp424.28MB

1151632 - 007 - How do Neural Networks work .mp423.53MB

1151632 - 147 - Data Preprocessing - Step 5.mp422.89MB

1151632 - 148 - Data Preprocessing - Step 6.mp422.8MB

1151632 - 112 - Building a Boltzmann Machine - Step 11.mp422.39MB

1151632 - 081 - EXTRA K-means Clustering (part 3).mp421.82MB

1151632 - 145 - Data Preprocessing - Step 3.mp421.73MB

1151632 - 048 - Building a CNN - Step 10.mp420.54MB

1151632 - 110 - Building a Boltzmann Machine - Step 9.mp420.39MB

1151632 - 002 - Installing Python.mp420.38MB

1151632 - 130 - Building an AutoEncoder - Step 3.mp420.09MB

1151632 - 073 - How do Self-Organizing Maps Work .mp420.02MB

1151632 - 026 - Improving the ANN.mp419.83MB

1151632 - 084 - Building a SOM - Step 2.mp419.43MB

1151632 - 039 - Building a CNN - Step 1.mp419.18MB

1151632 - 068 - Building a RNN - Step 11.mp418.97MB

1151632 - 077 - How do Self-Organizing Maps Learn (Part 2).mp418.65MB

1151632 - 078 - Live SOM example.mp418.54MB

1151632 - 009 - Gradient Descent.mp418.53MB

1151632 - 093 - Energy-Based Models (EBM).mp418.48MB

1151632 - 056 - Ethical Disclosure.mp418.27MB

1151632 - 021 - Building an ANN - Step 8.mp418.18MB

1151632 - 069 - Building a RNN - Step 12.mp418.18MB

1151632 - 023 - Building an ANN - Step 10.mp417.43MB

1151632 - 022 - Building an ANN - Step 9.mp416.89MB

1151632 - 010 - Stochastic Gradient Descent.mp416.82MB

1151632 - 013 - Business Problem Description.mp416.37MB

1151632 - 144 - Data Preprocessing - Step 2.mp415.85MB

1151632 - 106 - Building a Boltzmann Machine - Step 5.mp415.44MB

1151632 - 070 - Homework Solution.mp415.02MB

1151632 - 006 - The Activation Function.mp414.75MB

1151632 - 031 - Step 1(b) - ReLU Layer.mp414.09MB

1151632 - 121 - Sparse Autoencoders.mp413.99MB

1151632 - 119 - Training an Auto Encoder.mp413.55MB

1151632 - 088 - Mega Case Study - Step 2.mp413.34MB

1151632 - 143 - Data Preprocessing - Step 1.mp413.25MB

1151632 - 071 - Evaluating the RNN.mp413.19MB

1151632 - 097 - Deep Belief Networks.mp412.61MB

1151632 - 045 - Building a CNN - Step 7.mp412.57MB

1151632 - 080 - EXTRA K-means Clustering (part 2).mp412.34MB

1151632 - 150 - Logistic Regression Implementation - Step 1.mp412.19MB

1151632 - 061 - Building a RNN - Step 4.mp412.02MB

1151632 - 132 - Building an AutoEncoder - Step 5.mp411.86MB

1151632 - 155 - Classification Template.mp411.71MB

1151632 - 137 - Building an AutoEncoder - Step 10.mp411.26MB

1151632 - 063 - Building a RNN - Step 6.mp411.19MB

1151632 - 011 - Backpropagation.mp410.92MB

1151632 - 043 - Building a CNN - Step 5.mp49.91MB

1151632 - 044 - Building a CNN - Step 6.mp49.7MB

1151632 - 153 - Logistic Regression Implementation - Step 4.mp49.68MB

1151632 - 139 - Simple Linear Regression Intuition - Step 1.mp49.47MB

1151632 - 060 - Building a RNN - Step 3.mp49.11MB

1151632 - 020 - Building an ANN - Step 7.mp48.99MB

1151632 - 016 - Building an ANN - Step 3.mp48.37MB

1151632 - 151 - Logistic Regression Implementation - Step 2.mp48.14MB

1151632 - 149 - Data Preprocessing Template.mp48.13MB

1151632 - 059 - Building a RNN - Step 2.mp47.99MB

1151632 - 066 - Building a RNN - Step 9.mp47.91MB

1151632 - 035 - Summary.mp47.91MB

1151632 - 038 - Introduction to CNNs.mp47.84MB

1151632 - 065 - Building a RNN - Step 8.mp47.73MB

1151632 - 120 - Overcomplete hidden layers.mp47.64MB

1151632 - 055 - EXTRA LSTM Variations.mp47.33MB

1151632 - 019 - Building an ANN - Step 6.mp47.05MB

1151632 - 062 - Building a RNN - Step 5.mp46.86MB

1151632 - 046 - Building a CNN - Step 8.mp46.79MB

1151632 - 067 - Building a RNN - Step 10.mp46.72MB

1151632 - 126 - How to get the dataset.mp46.48MB

1151632 - 057 - How to get the dataset.mp46.48MB

1151632 - 099 - How to get the dataset.mp46.48MB

1151632 - 003 - How to get the dataset.mp46.48MB

1151632 - 082 - How to get the dataset.mp46.48MB

1151632 - 037 - How to get the dataset.mp46.48MB

1151632 - 012 - How to get the dataset.mp46.48MB

1151632 - 152 - Logistic Regression Implementation - Step 3.mp45.96MB

1151632 - 017 - Building an ANN - Step 4.mp45.95MB

1151632 - 028 - Plan of attack.mp45.93MB

1151632 - 040 - Building a CNN - Step 2.mp45.85MB

1151632 - 098 - Deep Boltzmann Machines.mp45.85MB

1151632 - 122 - Denoising Autoencoders.mp45.72MB

1151632 - 100 - Installing PyTorch.mp45.71MB

1151632 - 127 - Installing PyTorch.mp45.71MB

1151632 - 087 - Mega Case Study - Step 1.mp45.45MB

1151632 - 140 - Simple Linear Regression Intuition - Step 2.mp45.37MB

1151632 - 123 - Contractive Autoencoders.mp45.29MB

1151632 - 072 - Plan of attack.mp45.19MB

1151632 - 004 - Plan of Attack.mp44.74MB

1151632 - 124 - Stacked Autoencoders.mp44.53MB

1151632 - 050 - Plan of attack.mp44.19MB

1151632 - 064 - Building a RNN - Step 7.mp44.16MB

1151632 - 116 - Plan of attack.mp44.06MB

1151632 - 074 - Why revisit K-Means .mp44.05MB

1151632 - 091 - Plan of attack.mp43.78MB

1151632 - 125 - Deep Autoencoders.mp43.31MB

1151632 - 033 - Step 3 - Flattening.mp43.27MB

1151632 - 118 - A Note on Biases.mp42.43MB

1151632 - 041 - Building a CNN - Step 3.mp42.23MB

1151632 - 141 - Multiple Linear Regression Intuition.mp41.82MB