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Deep Learning A-Z™ Hands-On Artificial Neural Networks
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8eb880fe918ea42c5d71fafb0323889c5f62dbc4
文档大小:
3.4 GB
文档个数:
155
个文档
下载次数:
5536
次
下载速度:
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收录时间:
2020-02-16
最近下载:
2025-01-11
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文档列表
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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