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| [CourseClub.Me].url |
122б |
| [FreeCourseSite.com].url |
127б |
| [GigaCourse.Com].url |
49б |
| 100 - Optimizers comparison.mp4 |
97.29Мб |
| 100 - Optimizers comparison English.vtt |
12.71Кб |
| 100 - Optimizers comparison Vietnamese.vtt |
15.91Кб |
| 101 - CodeChallenge Optimizers and something.mp4 |
54.40Мб |
| 101 - CodeChallenge Optimizers and something English.vtt |
8.19Кб |
| 101 - CodeChallenge Optimizers and something Vietnamese.vtt |
10.39Кб |
| 102 - CodeChallenge Adam with L2 regularization.mp4 |
61.08Мб |
| 102 - CodeChallenge Adam with L2 regularization English.vtt |
9.04Кб |
| 102 - CodeChallenge Adam with L2 regularization Vietnamese.vtt |
11.01Кб |
| 103 - Learning rate decay.mp4 |
107.19Мб |
| 103 - Learning rate decay English.vtt |
15.45Кб |
| 103 - Learning rate decay Vietnamese.vtt |
19.76Кб |
| 104 - How to pick the right metaparameters.mp4 |
36.80Мб |
| 104 - How to pick the right metaparameters English.vtt |
14.61Кб |
| 104 - How to pick the right metaparameters Vietnamese.vtt |
18.85Кб |
| 105 - What are fullyconnected and feedforward networks.mp4 |
17.84Мб |
| 105 - What are fullyconnected and feedforward networks English.vtt |
6.11Кб |
| 105 - What are fullyconnected and feedforward networks Vietnamese.vtt |
7.91Кб |
| 106 - The MNIST dataset.mp4 |
135.66Мб |
| 106 - The MNIST dataset English.vtt |
15.97Кб |
| 106 - The MNIST dataset Vietnamese.vtt |
19.93Кб |
| 107 - FFN to classify digits.mp4 |
178.30Мб |
| 107 - FFN to classify digits English.vtt |
28.31Кб |
| 107 - FFN to classify digits Vietnamese.vtt |
35.46Кб |
| 108 - CodeChallenge Binarized MNIST images.mp4 |
44.55Мб |
| 108 - CodeChallenge Binarized MNIST images English.vtt |
6.42Кб |
| 108 - CodeChallenge Binarized MNIST images Vietnamese.vtt |
8.12Кб |
| 109 - CodeChallenge Data normalization.mp4 |
104.14Мб |
| 109 - CodeChallenge Data normalization English.vtt |
21.17Кб |
| 109 - CodeChallenge Data normalization Vietnamese.vtt |
26.72Кб |
| 10 - Should you watch the Python tutorial.mp4 |
13.79Мб |
| 10 - Should you watch the Python tutorial English.vtt |
5.39Кб |
| 10 - Should you watch the Python tutorial Vietnamese.vtt |
6.58Кб |
| 110 - Distributions of weights pre and postlearning.mp4 |
141.80Мб |
| 110 - Distributions of weights pre and postlearning English.vtt |
18.77Кб |
| 110 - Distributions of weights pre and postlearning Vietnamese.vtt |
23.88Кб |
| 111 - CodeChallenge MNIST and breadth vs depth.mp4 |
140.35Мб |
| 111 - CodeChallenge MNIST and breadth vs depth English.vtt |
15.43Кб |
| 111 - CodeChallenge MNIST and breadth vs depth Vietnamese.vtt |
19.02Кб |
| 112 - CodeChallenge Optimizers and MNIST.mp4 |
50.72Мб |
| 112 - CodeChallenge Optimizers and MNIST English.vtt |
8.67Кб |
| 112 - CodeChallenge Optimizers and MNIST Vietnamese.vtt |
10.77Кб |
| 113 - Scrambled MNIST.mp4 |
60.17Мб |
| 113 - Scrambled MNIST English.vtt |
9.78Кб |
| 113 - Scrambled MNIST Vietnamese.vtt |
12.51Кб |
| 114 - Shifted MNIST.mp4 |
86.36Мб |
| 114 - Shifted MNIST English.vtt |
14.23Кб |
| 114 - Shifted MNIST Vietnamese.vtt |
18.65Кб |
| 115 - CodeChallenge The mystery of the missing 7.mp4 |
80.88Мб |
| 115 - CodeChallenge The mystery of the missing 7 English.vtt |
13.61Кб |
| 115 - CodeChallenge The mystery of the missing 7 Vietnamese.vtt |
17.14Кб |
| 116 - Universal approximation theorem.mp4 |
34.23Мб |
| 116 - Universal approximation theorem English.vtt |
10.23Кб |
| 116 - Universal approximation theorem Vietnamese.vtt |
12.69Кб |
| 117 - Anatomy of a torch dataset and dataloader.mp4 |
151.86Мб |
| 117 - Anatomy of a torch dataset and dataloader English.vtt |
22.69Кб |
| 117 - Anatomy of a torch dataset and dataloader Vietnamese.vtt |
29.05Кб |
| 118 - Data size and network size.mp4 |
149.51Мб |
| 118 - Data size and network size English.vtt |
20.30Кб |
| 118 - Data size and network size Vietnamese.vtt |
25.32Кб |
| 119 - CodeChallenge unbalanced data.mp4 |
183.43Мб |
| 119 - CodeChallenge unbalanced data English.vtt |
25.26Кб |
| 119 - CodeChallenge unbalanced data Vietnamese.vtt |
32.39Кб |
| 11 - PyTorch or TensorFlow.html |
1.07Кб |
| 120 - What to do about unbalanced designs.mp4 |
29.78Мб |
| 120 - What to do about unbalanced designs English.vtt |
9.70Кб |
| 120 - What to do about unbalanced designs Vietnamese.vtt |
12.39Кб |
| 121 - Data oversampling in MNIST.mp4 |
136.13Мб |
| 121 - Data oversampling in MNIST English.vtt |
20.84Кб |
| 121 - Data oversampling in MNIST Vietnamese.vtt |
26.45Кб |
| 122 - Data noise augmentation with devsettest.mp4 |
117.64Мб |
| 122 - Data noise augmentation with devsettest English.vtt |
16.15Кб |
| 122 - Data noise augmentation with devsettest Vietnamese.vtt |
20.27Кб |
| 123 - Data feature augmentation.mp4 |
176.25Мб |
| 123 - Data feature augmentation English.vtt |
24.60Кб |
| 123 - Data feature augmentation Vietnamese.vtt |
30.79Кб |
| 124 - Getting data into colab.mp4 |
48.76Мб |
| 124 - Getting data into colab English.vtt |
7.71Кб |
| 124 - Getting data into colab Vietnamese.vtt |
9.90Кб |
| 125 - Save and load trained models.mp4 |
61.58Мб |
| 125 - Save and load trained models English.vtt |
7.78Кб |
| 125 - Save and load trained models Vietnamese.vtt |
10.08Кб |
| 126 - Save the bestperforming model.mp4 |
139.86Мб |
| 126 - Save the bestperforming model English.vtt |
18.96Кб |
| 126 - Save the bestperforming model Vietnamese.vtt |
24.33Кб |
| 127 - Where to find online datasets.mp4 |
46.09Мб |
| 127 - Where to find online datasets English.vtt |
7.16Кб |
| 127 - Where to find online datasets Vietnamese.vtt |
8.89Кб |
| 128 - Two perspectives of the world.mp4 |
26.81Мб |
| 128 - Two perspectives of the world English.vtt |
8.91Кб |
| 128 - Two perspectives of the world Vietnamese.vtt |
11.44Кб |
| 129 - Accuracy precision recall F1.mp4 |
90.68Мб |
| 129 - Accuracy precision recall F1 English.vtt |
15.63Кб |
| 129 - Accuracy precision recall F1 Vietnamese.vtt |
18.80Кб |
| 12 - Introduction to this section.mp4 |
6.56Мб |
| 12 - Introduction to this section English.vtt |
2.55Кб |
| 12 - Introduction to this section Vietnamese.vtt |
3.18Кб |
| 130 - APRF in code.mp4 |
61.06Мб |
| 130 - APRF in code English.vtt |
8.17Кб |
| 130 - APRF in code Vietnamese.vtt |
10.24Кб |
| 131 - APRF example 1 wine quality.mp4 |
162.71Мб |
| 131 - APRF example 1 wine quality English.vtt |
16.62Кб |
| 131 - APRF example 1 wine quality Vietnamese.vtt |
21.52Кб |
| 132 - APRF example 2 MNIST.mp4 |
150.26Мб |
| 132 - APRF example 2 MNIST English.vtt |
14.85Кб |
| 132 - APRF example 2 MNIST Vietnamese.vtt |
18.68Кб |
| 133 - CodeChallenge MNIST with unequal groups.mp4 |
91.45Мб |
| 133 - CodeChallenge MNIST with unequal groups English.vtt |
11.13Кб |
| 133 - CodeChallenge MNIST with unequal groups Vietnamese.vtt |
13.75Кб |
| 134 - Computation time.mp4 |
110.41Мб |
| 134 - Computation time English.vtt |
12.37Кб |
| 134 - Computation time Vietnamese.vtt |
15.39Кб |
| 135 - Better performance in test than train.mp4 |
26.26Мб |
| 135 - Better performance in test than train English.vtt |
10.53Кб |
| 135 - Better performance in test than train Vietnamese.vtt |
13.28Кб |
| 136 - Project 1 A gratuitously complex adding machine.mp4 |
37.39Мб |
| 136 - Project 1 A gratuitously complex adding machine English.vtt |
9.34Кб |
| 136 - Project 1 A gratuitously complex adding machine Vietnamese.vtt |
11.77Кб |
| 137 - Project 1 My solution.mp4 |
109.53Мб |
| 137 - Project 1 My solution English.vtt |
14.55Кб |
| 137 - Project 1 My solution Vietnamese.vtt |
18.42Кб |
| 138 - Project 2 Predicting heart disease.mp4 |
35.56Мб |
| 138 - Project 2 Predicting heart disease English.vtt |
9.55Кб |
| 138 - Project 2 Predicting heart disease Vietnamese.vtt |
11.81Кб |
| 139 - Project 2 My solution.mp4 |
155.73Мб |
| 139 - Project 2 My solution English.vtt |
23.79Кб |
| 139 - Project 2 My solution Vietnamese.vtt |
29.77Кб |
| 13 - Spectral theories in mathematics.mp4 |
64.49Мб |
| 13 - Spectral theories in mathematics English.vtt |
11.86Кб |
| 13 - Spectral theories in mathematics Vietnamese.vtt |
14.79Кб |
| 140 - Project 3 FFN for missing data interpolation.mp4 |
27.39Мб |
| 140 - Project 3 FFN for missing data interpolation English.vtt |
12.49Кб |
| 140 - Project 3 FFN for missing data interpolation Vietnamese.vtt |
15.95Кб |
| 141 - Project 3 My solution.mp4 |
83.52Мб |
| 141 - Project 3 My solution English.vtt |
10.28Кб |
| 141 - Project 3 My solution Vietnamese.vtt |
13.09Кб |
| 142 - Explanation of weight matrix sizes.mp4 |
89.58Мб |
| 142 - Explanation of weight matrix sizes English.vtt |
14.93Кб |
| 142 - Explanation of weight matrix sizes Vietnamese.vtt |
18.01Кб |
| 143 - A surprising demo of weight initializations.mp4 |
132.78Мб |
| 143 - A surprising demo of weight initializations English.vtt |
20.66Кб |
| 143 - A surprising demo of weight initializations Vietnamese.vtt |
26.53Кб |
| 144 - Theory Why and how to initialize weights.mp4 |
107.92Мб |
| 144 - Theory Why and how to initialize weights English.vtt |
15.92Кб |
| 144 - Theory Why and how to initialize weights Vietnamese.vtt |
19.85Кб |
| 145 - CodeChallenge Weight variance inits.mp4 |
112.85Мб |
| 145 - CodeChallenge Weight variance inits English.vtt |
16.03Кб |
| 145 - CodeChallenge Weight variance inits Vietnamese.vtt |
20.11Кб |
| 146 - Xavier and Kaiming initializations.mp4 |
148.87Мб |
| 146 - Xavier and Kaiming initializations English.vtt |
19.56Кб |
| 146 - Xavier and Kaiming initializations Vietnamese.vtt |
24.64Кб |
| 147 - CodeChallenge Xavier vs Kaiming.mp4 |
169.10Мб |
| 147 - CodeChallenge Xavier vs Kaiming English.vtt |
21.24Кб |
| 147 - CodeChallenge Xavier vs Kaiming Vietnamese.vtt |
27.00Кб |
| 148 - CodeChallenge Identically random weights.mp4 |
96.12Мб |
| 148 - CodeChallenge Identically random weights English.vtt |
15.55Кб |
| 148 - CodeChallenge Identically random weights Vietnamese.vtt |
19.76Кб |
| 149 - Freezing weights during learning.mp4 |
137.77Мб |
| 149 - Freezing weights during learning English.vtt |
16.69Кб |
| 149 - Freezing weights during learning Vietnamese.vtt |
21.12Кб |
| 14 - Terms and datatypes in math and computers.mp4 |
22.68Мб |
| 14 - Terms and datatypes in math and computers English.vtt |
9.23Кб |
| 14 - Terms and datatypes in math and computers Vietnamese.vtt |
11.46Кб |
| 150 - Learningrelated changes in weights.mp4 |
161.55Мб |
| 150 - Learningrelated changes in weights English.vtt |
28.30Кб |
| 150 - Learningrelated changes in weights Vietnamese.vtt |
34.89Кб |
| 151 - Use default inits or apply your own.mp4 |
16.76Мб |
| 151 - Use default inits or apply your own English.vtt |
5.58Кб |
| 151 - Use default inits or apply your own Vietnamese.vtt |
6.98Кб |
| 152 - What are autoencoders and what do they do.mp4 |
29.40Мб |
| 152 - What are autoencoders and what do they do English.vtt |
14.69Кб |
| 152 - What are autoencoders and what do they do Vietnamese.vtt |
18.47Кб |
| 153 - Denoising MNIST.mp4 |
134.22Мб |
| 153 - Denoising MNIST English.vtt |
19.65Кб |
| 153 - Denoising MNIST Vietnamese.vtt |
24.44Кб |
| 154 - CodeChallenge How many units.mp4 |
148.39Мб |
| 154 - CodeChallenge How many units English.vtt |
25.01Кб |
| 154 - CodeChallenge How many units Vietnamese.vtt |
30.78Кб |
| 155 - AEs for occlusion.mp4 |
138.20Мб |
| 155 - AEs for occlusion English.vtt |
21.99Кб |
| 155 - AEs for occlusion Vietnamese.vtt |
27.71Кб |
| 156 - The latent code of MNIST.mp4 |
182.10Мб |
| 156 - The latent code of MNIST English.vtt |
27.32Кб |
| 156 - The latent code of MNIST Vietnamese.vtt |
33.77Кб |
| 157 - Autoencoder with tied weights.mp4 |
200.67Мб |
| 157 - Autoencoder with tied weights English.vtt |
30.00Кб |
| 157 - Autoencoder with tied weights Vietnamese.vtt |
38.36Кб |
| 158 - What is a GPU and why use it.mp4 |
69.22Мб |
| 158 - What is a GPU and why use it English.vtt |
19.39Кб |
| 158 - What is a GPU and why use it Vietnamese.vtt |
23.74Кб |
| 159 - Implementation.mp4 |
60.70Мб |
| 159 - Implementation English.vtt |
12.79Кб |
| 159 - Implementation Vietnamese.vtt |
16.14Кб |
| 15 - Converting reality to numbers.mp4 |
19.72Мб |
| 15 - Converting reality to numbers English.vtt |
8.32Кб |
| 15 - Converting reality to numbers Vietnamese.vtt |
10.76Кб |
| 160 - CodeChallenge Run an experiment on the GPU.mp4 |
59.16Мб |
| 160 - CodeChallenge Run an experiment on the GPU English.vtt |
8.48Кб |
| 160 - CodeChallenge Run an experiment on the GPU Vietnamese.vtt |
10.04Кб |
| 161 - Convolution concepts.mp4 |
126.08Мб |
| 161 - Convolution concepts English.vtt |
27.96Кб |
| 161 - Convolution concepts Vietnamese.vtt |
34.53Кб |
| 162 - Feature maps and convolution kernels.mp4 |
83.22Мб |
| 162 - Feature maps and convolution kernels English.vtt |
12.14Кб |
| 162 - Feature maps and convolution kernels Vietnamese.vtt |
15.01Кб |
| 163 - Convolution in code.mp4 |
258.22Мб |
| 163 - Convolution in code English.vtt |
26.37Кб |
| 163 - Convolution in code Vietnamese.vtt |
32.94Кб |
| 164 - Convolution parameters stride padding.mp4 |
38.86Мб |
| 164 - Convolution parameters stride padding English.vtt |
15.64Кб |
| 164 - Convolution parameters stride padding Vietnamese.vtt |
19.26Кб |
| 165 - The Conv2 class in PyTorch.mp4 |
113.43Мб |
| 165 - The Conv2 class in PyTorch English.vtt |
16.32Кб |
| 165 - The Conv2 class in PyTorch Vietnamese.vtt |
20.39Кб |
| 166 - CodeChallenge Choose the parameters.mp4 |
31.01Мб |
| 166 - CodeChallenge Choose the parameters English.vtt |
8.85Кб |
| 166 - CodeChallenge Choose the parameters Vietnamese.vtt |
11.19Кб |
| 167 - Transpose convolution.mp4 |
102.82Мб |
| 167 - Transpose convolution English.vtt |
17.25Кб |
| 167 - Transpose convolution Vietnamese.vtt |
21.38Кб |
| 168 - Maxmean pooling.mp4 |
69.37Мб |
| 168 - Maxmean pooling English.vtt |
23.12Кб |
| 168 - Maxmean pooling Vietnamese.vtt |
29.34Кб |
| 169 - Pooling in PyTorch.mp4 |
64.25Мб |
| 169 - Pooling in PyTorch English.vtt |
17.34Кб |
| 169 - Pooling in PyTorch Vietnamese.vtt |
21.71Кб |
| 16 - Vector and matrix transpose.mp4 |
26.18Мб |
| 16 - Vector and matrix transpose English.vtt |
8.68Кб |
| 16 - Vector and matrix transpose Vietnamese.vtt |
10.92Кб |
| 170 - To pool or to stride.mp4 |
70.79Мб |
| 170 - To pool or to stride English.vtt |
12.62Кб |
| 170 - To pool or to stride Vietnamese.vtt |
16.06Кб |
| 171 - Image transforms.mp4 |
192.95Мб |
| 171 - Image transforms English.vtt |
20.48Кб |
| 171 - Image transforms Vietnamese.vtt |
26.16Кб |
| 172 - Creating and using custom DataLoaders.mp4 |
154.12Мб |
| 172 - Creating and using custom DataLoaders English.vtt |
22.85Кб |
| 172 - Creating and using custom DataLoaders Vietnamese.vtt |
29.37Кб |
| 173 - The canonical CNN architecture.mp4 |
33.16Мб |
| 173 - The canonical CNN architecture English.vtt |
13.68Кб |
| 173 - The canonical CNN architecture Vietnamese.vtt |
17.01Кб |
| 174 - CNN to classify MNIST digits.mp4 |
217.84Мб |
| 174 - CNN to classify MNIST digits English.vtt |
32.79Кб |
| 174 - CNN to classify MNIST digits Vietnamese.vtt |
40.74Кб |
| 175 - CNN on shifted MNIST.mp4 |
63.47Мб |
| 175 - CNN on shifted MNIST English.vtt |
10.53Кб |
| 175 - CNN on shifted MNIST Vietnamese.vtt |
13.73Кб |
| 176 - Classify Gaussian blurs.mp4 |
279.72Мб |
| 176 - Classify Gaussian blurs English.vtt |
29.64Кб |
| 176 - Classify Gaussian blurs Vietnamese.vtt |
36.52Кб |
| 177 - Examine feature map activations.mp4 |
412.18Мб |
| 177 - Examine feature map activations English.vtt |
34.96Кб |
| 177 - Examine feature map activations Vietnamese.vtt |
44.07Кб |
| 178 - CodeChallenge Softcode internal parameters.mp4 |
176.05Мб |
| 178 - CodeChallenge Softcode internal parameters English.vtt |
21.73Кб |
| 178 - CodeChallenge Softcode internal parameters Vietnamese.vtt |
27.06Кб |
| 179 - CodeChallenge How wide the FC.mp4 |
144.65Мб |
| 179 - CodeChallenge How wide the FC English.vtt |
14.60Кб |
| 179 - CodeChallenge How wide the FC Vietnamese.vtt |
18.06Кб |
| 17 - OMG its the dot product.mp4 |
28.73Мб |
| 17 - OMG its the dot product English.vtt |
12.11Кб |
| 17 - OMG its the dot product Vietnamese.vtt |
14.35Кб |
| 180 - Do autoencoders clean Gaussians.mp4 |
206.11Мб |
| 180 - Do autoencoders clean Gaussians English.vtt |
21.15Кб |
| 180 - Do autoencoders clean Gaussians Vietnamese.vtt |
26.28Кб |
| 181 - CodeChallenge AEs and occluded Gaussians.mp4 |
128.09Мб |
| 181 - CodeChallenge AEs and occluded Gaussians English.vtt |
12.12Кб |
| 181 - CodeChallenge AEs and occluded Gaussians Vietnamese.vtt |
15.20Кб |
| 182 - CodeChallenge Custom loss functions.mp4 |
154.88Мб |
| 182 - CodeChallenge Custom loss functions English.vtt |
25.77Кб |
| 182 - CodeChallenge Custom loss functions Vietnamese.vtt |
31.62Кб |
| 183 - Discover the Gaussian parameters.mp4 |
136.65Мб |
| 183 - Discover the Gaussian parameters English.vtt |
20.21Кб |
| 183 - Discover the Gaussian parameters Vietnamese.vtt |
24.89Кб |
| 184 - The EMNIST dataset letter recognition.mp4 |
219.98Мб |
| 184 - The EMNIST dataset letter recognition English.vtt |
31.08Кб |
| 184 - The EMNIST dataset letter recognition Vietnamese.vtt |
38.59Кб |
| 185 - Dropout in CNNs.mp4 |
104.23Мб |
| 185 - Dropout in CNNs English.vtt |
12.44Кб |
| 185 - Dropout in CNNs Vietnamese.vtt |
15.46Кб |
| 186 - CodeChallenge How low can you go.mp4 |
60.23Мб |
| 186 - CodeChallenge How low can you go English.vtt |
8.66Кб |
| 186 - CodeChallenge How low can you go Vietnamese.vtt |
10.85Кб |
| 187 - CodeChallenge Varying number of channels.mp4 |
99.83Мб |
| 187 - CodeChallenge Varying number of channels English.vtt |
17.12Кб |
| 187 - CodeChallenge Varying number of channels Vietnamese.vtt |
21.03Кб |
| 188 - So many possibilities How to create a CNN.mp4 |
12.93Мб |
| 188 - So many possibilities How to create a CNN English.vtt |
5.68Кб |
| 188 - So many possibilities How to create a CNN Vietnamese.vtt |
7.10Кб |
| 189 - Project 1 Import and classify CIFAR10.mp4 |
53.17Мб |
| 189 - Project 1 Import and classify CIFAR10 English.vtt |
9.25Кб |
| 189 - Project 1 Import and classify CIFAR10 Vietnamese.vtt |
11.89Кб |
| 18 - Matrix multiplication.mp4 |
66.71Мб |
| 18 - Matrix multiplication English.vtt |
17.86Кб |
| 18 - Matrix multiplication Vietnamese.vtt |
21.74Кб |
| 190 - Project 1 My solution.mp4 |
130.03Мб |
| 190 - Project 1 My solution English.vtt |
14.98Кб |
| 190 - Project 1 My solution Vietnamese.vtt |
18.91Кб |
| 191 - Project 2 CIFARautoencoder.mp4 |
45.25Мб |
| 191 - Project 2 CIFARautoencoder English.vtt |
6.11Кб |
| 191 - Project 2 CIFARautoencoder Vietnamese.vtt |
7.40Кб |
| 192 - Project 3 FMNIST.mp4 |
28.84Мб |
| 192 - Project 3 FMNIST English.vtt |
4.55Кб |
| 192 - Project 3 FMNIST Vietnamese.vtt |
5.62Кб |
| 193 - Project 4 Psychometric functions in CNNs.mp4 |
76.46Мб |
| 193 - Project 4 Psychometric functions in CNNs English.vtt |
14.69Кб |
| 193 - Project 4 Psychometric functions in CNNs Vietnamese.vtt |
18.54Кб |
| 194 - Transfer learning What why and when.mp4 |
58.18Мб |
| 194 - Transfer learning What why and when English.vtt |
21.50Кб |
| 194 - Transfer learning What why and when Vietnamese.vtt |
27.01Кб |
| 195 - Transfer learning MNIST FMNIST.mp4 |
121.34Мб |
| 195 - Transfer learning MNIST FMNIST English.vtt |
12.60Кб |
| 195 - Transfer learning MNIST FMNIST Vietnamese.vtt |
15.55Кб |
| 196 - CodeChallenge letters to numbers.mp4 |
131.86Мб |
| 196 - CodeChallenge letters to numbers English.vtt |
18.61Кб |
| 196 - CodeChallenge letters to numbers Vietnamese.vtt |
23.30Кб |
| 197 - Famous CNN architectures.mp4 |
32.95Мб |
| 197 - Famous CNN architectures English.vtt |
7.63Кб |
| 197 - Famous CNN architectures Vietnamese.vtt |
9.07Кб |
| 198 - Transfer learning with ResNet18.mp4 |
201.17Мб |
| 198 - Transfer learning with ResNet18 English.vtt |
21.15Кб |
| 198 - Transfer learning with ResNet18 Vietnamese.vtt |
26.50Кб |
| 199 - CodeChallenge VGG16.mp4 |
20.28Мб |
| 199 - CodeChallenge VGG16 English.vtt |
4.43Кб |
| 199 - CodeChallenge VGG16 Vietnamese.vtt |
5.34Кб |
| 19 - Softmax.mp4 |
101.37Мб |
| 19 - Softmax English.vtt |
24.02Кб |
| 19 - Softmax Vietnamese.vtt |
29.23Кб |
| 1 - How to learn from this course.mp4 |
54.97Мб |
| 1 - How to learn from this course English.vtt |
11.34Кб |
| 1 - How to learn from this course Vietnamese.vtt |
13.97Кб |
| 200 - Pretraining with autoencoders.mp4 |
208.64Мб |
| 200 - Pretraining with autoencoders English.vtt |
24.91Кб |
| 200 - Pretraining with autoencoders Vietnamese.vtt |
32.53Кб |
| 201 - CIFAR10 with autoencoderpretrained model.mp4 |
166.90Мб |
| 201 - CIFAR10 with autoencoderpretrained model English.vtt |
22.42Кб |
| 201 - CIFAR10 with autoencoderpretrained model Vietnamese.vtt |
28.33Кб |
| 202 - What is style transfer and how does it work.mp4 |
28.14Мб |
| 202 - What is style transfer and how does it work English.vtt |
5.52Кб |
| 202 - What is style transfer and how does it work Vietnamese.vtt |
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| 203 - The Gram matrix feature activation covariance.mp4 |
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| 203 - The Gram matrix feature activation covariance English.vtt |
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| 204 - The style transfer algorithm.mp4 |
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| 204 - The style transfer algorithm English.vtt |
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| 205 - Transferring the screaming bathtub.mp4 |
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| 206 - CodeChallenge Style transfer with AlexNet.mp4 |
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| 207 - GAN What why and how.mp4 |
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| 208 - Linear GAN with MNIST.mp4 |
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| 209 - CodeChallenge Linear GAN with FMNIST.mp4 |
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| 20 - Logarithms.mp4 |
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| 210 - CNN GAN with Gaussians.mp4 |
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| 210 - CNN GAN with Gaussians English.vtt |
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| 211 - CodeChallenge Gaussians with fewer layers.mp4 |
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| 212 - CNN GAN with FMNIST.mp4 |
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| 213 - CodeChallenge CNN GAN with CIFAR.mp4 |
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| 214 - Leveraging sequences in deep learning.mp4 |
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| 215 - How RNNs work.mp4 |
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| 216 - The RNN class in PyTorch.mp4 |
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| 217 - Predicting alternating sequences.mp4 |
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| 218 - CodeChallenge sine wave extrapolation.mp4 |
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| 219 - More on RNNs Hidden states embeddings.mp4 |
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| 21 - Entropy and crossentropy.mp4 |
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| 220 - GRU and LSTM.mp4 |
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| 221 - The LSTM and GRU classes.mp4 |
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| 222 - Lorem ipsum.mp4 |
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| 223 - Will AI save us or destroy us.mp4 |
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| 224 - Example case studies.mp4 |
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| 225 - Some other possible ethical scenarios.mp4 |
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| 226 - Will deep learning take our jobs.mp4 |
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| 227 - Accountability and making ethical AI.mp4 |
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| 228 - How to learn topic X in deep learning.mp4 |
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| 229 - How to read academic DL papers.mp4 |
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| 22 - Minmax and argminargmax.mp4 |
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| 230 - How to learn from the Python tutorial.mp4 |
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| 231 - Variables.mp4 |
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| 232 - Math and printing.mp4 |
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| 233 - Lists 1 of 2.mp4 |
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| 235 - Tuples.mp4 |
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| 236 - Booleans.mp4 |
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| 237 - Dictionaries.mp4 |
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| 238 - Indexing.mp4 |
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| 239 - Slicing.mp4 |
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| 23 - Mean and variance.mp4 |
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| 240 - Inputs and outputs.mp4 |
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| 241 - Python libraries numpy.mp4 |
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| 243 - Getting help on functions.mp4 |
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| 244 - Creating functions.mp4 |
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| 245 - Global and local variable scopes.mp4 |
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| 246 - Copies and referents of variables.mp4 |
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| 247 - Classes and objectoriented programming.mp4 |
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| 248 - Ifelse statements.mp4 |
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| 24 - Random sampling and sampling variability.mp4 |
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| 250 - For loops.mp4 |
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| 251 - Enumerate and zip.mp4 |
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| 252 - Continue.mp4 |
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| 253 - Initializing variables.mp4 |
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| 254 - Singleline loops list comprehension.mp4 |
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| 255 - while loops.mp4 |
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| 256 - Broadcasting in numpy.mp4 |
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| 257 - Function error checking and handling.mp4 |
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| 258 - Printing and string interpolation.mp4 |
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| 259 - Plotting dots and lines.mp4 |
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| 25 - Reproducible randomness via seeding.mp4 |
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| 260 - Subplot geometry.mp4 |
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| 261 - Making the graphs look nicer.mp4 |
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| 262 - Seaborn.mp4 |
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| 263 - Images.mp4 |
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| 264 - Export plots in low and high resolution.mp4 |
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| 265 - Bonus content.html |
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| 26 - The ttest.mp4 |
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| 27 - Derivatives intuition and polynomials.mp4 |
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| 28 - Derivatives find minima.mp4 |
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| 29 - Derivatives product and chain rules.mp4 |
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| 2 - Using Udemy like a pro.mp4 |
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| 30 - Overview of gradient descent.mp4 |
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| 31 - What about local minima.mp4 |
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| 32 - Gradient descent in 1D.mp4 |
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| 33 - CodeChallenge unfortunate starting value.mp4 |
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| 34 - Gradient descent in 2D.mp4 |
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| 35 - CodeChallenge 2D gradient ascent.mp4 |
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| 36 - Parametric experiments on gd.mp4 |
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| 37 - CodeChallenge fixed vs dynamic learning rate.mp4 |
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| 38 - Vanishing and exploding gradients.mp4 |
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| 39 - Tangent Notebook revision history.mp4 |
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| 3 - Code on my github site.txt |
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| 3 - DUDL-PythonCode.zip |
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| 40 - The perceptron and ANN architecture.mp4 |
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| 41 - A geometric view of ANNs.mp4 |
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| 42 - ANN math part 1 forward prop.mp4 |
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| 43 - ANN math part 2 errors loss cost.mp4 |
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| 44 - ANN math part 3 backprop.mp4 |
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| 45 - ANN for regression.mp4 |
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| 46 - CodeChallenge manipulate regression slopes.mp4 |
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| 47 - ANN for classifying qwerties.mp4 |
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| 48 - Learning rates comparison.mp4 |
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| 49 - Multilayer ANN.mp4 |
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| 4 - My policy on codesharing.mp4 |
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| 50 - Linear solutions to linear problems.mp4 |
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| 51 - Why multilayer linear models dont exist.mp4 |
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| 52 - Multioutput ANN iris dataset.mp4 |
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| 53 - CodeChallenge more qwerties.mp4 |
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| 54 - Comparing the number of hidden units.mp4 |
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| 55 - Depth vs breadth number of parameters.mp4 |
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| 56 - Defining models using sequential vs class.mp4 |
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| 57 - Model depth vs breadth.mp4 |
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| 58 - CodeChallenge convert sequential to class.mp4 |
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| 59 - Diversity of ANN visual representations.html |
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| 5 - What is an artificial neural network.mp4 |
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| 60 - Reflection Are DL models understandable yet.mp4 |
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| 61 - What is overfitting and is it as bad as they say.mp4 |
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| 62 - Crossvalidation.mp4 |
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| 63 - Generalization.mp4 |
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| 64 - Crossvalidation manual separation.mp4 |
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| 65 - Crossvalidation scikitlearn.mp4 |
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| 66 - Crossvalidation DataLoader.mp4 |
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| 67 - Splitting data into train devset test.mp4 |
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| 68 - Crossvalidation on regression.mp4 |
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| 69 - Regularization Concept and methods.mp4 |
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| 6 - How models learn.mp4 |
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| 70 - train and eval modes.mp4 |
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| 71 - Dropout regularization.mp4 |
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| 71 - Dropout regularization English.vtt |
27.35Кб |
| 71 - Dropout regularization Vietnamese.vtt |
35.28Кб |
| 72 - Dropout regularization in practice.mp4 |
201.41Мб |
| 72 - Dropout regularization in practice English.vtt |
28.83Кб |
| 72 - Dropout regularization in practice Vietnamese.vtt |
36.31Кб |
| 73 - Dropout example 2.mp4 |
60.78Мб |
| 73 - Dropout example 2 English.vtt |
7.97Кб |
| 73 - Dropout example 2 Vietnamese.vtt |
9.94Кб |
| 74 - Weight regularization L1L2 math.mp4 |
65.46Мб |
| 74 - Weight regularization L1L2 math English.vtt |
23.45Кб |
| 74 - Weight regularization L1L2 math Vietnamese.vtt |
29.01Кб |
| 75 - L2 regularization in practice.mp4 |
123.28Мб |
| 75 - L2 regularization in practice English.vtt |
16.45Кб |
| 75 - L2 regularization in practice Vietnamese.vtt |
20.82Кб |
| 76 - L1 regularization in practice.mp4 |
110.09Мб |
| 76 - L1 regularization in practice English.vtt |
15.11Кб |
| 76 - L1 regularization in practice Vietnamese.vtt |
19.13Кб |
| 77 - Training in minibatches.mp4 |
35.41Мб |
| 77 - Training in minibatches English.vtt |
14.59Кб |
| 77 - Training in minibatches Vietnamese.vtt |
18.51Кб |
| 78 - Batch training in action.mp4 |
120.24Мб |
| 78 - Batch training in action English.vtt |
13.54Кб |
| 78 - Batch training in action Vietnamese.vtt |
17.03Кб |
| 79 - The importance of equal batch sizes.mp4 |
82.14Мб |
| 79 - The importance of equal batch sizes English.vtt |
8.24Кб |
| 79 - The importance of equal batch sizes Vietnamese.vtt |
10.40Кб |
| 7 - The role of DL in science and knowledge.mp4 |
127.10Мб |
| 7 - The role of DL in science and knowledge English.vtt |
20.29Кб |
| 7 - The role of DL in science and knowledge Vietnamese.vtt |
25.90Кб |
| 80 - CodeChallenge Effects of minibatch size.mp4 |
130.36Мб |
| 80 - CodeChallenge Effects of minibatch size English.vtt |
15.68Кб |
| 80 - CodeChallenge Effects of minibatch size Vietnamese.vtt |
19.75Кб |
| 81 - What are metaparameters.mp4 |
18.66Мб |
| 81 - What are metaparameters English.vtt |
6.47Кб |
| 81 - What are metaparameters Vietnamese.vtt |
7.95Кб |
| 82 - The wine quality dataset.mp4 |
194.40Мб |
| 82 - The wine quality dataset English.vtt |
22.28Кб |
| 82 - The wine quality dataset Vietnamese.vtt |
28.19Кб |
| 83 - CodeChallenge Minibatch size in the wine dataset.mp4 |
160.37Мб |
| 83 - CodeChallenge Minibatch size in the wine dataset English.vtt |
20.03Кб |
| 83 - CodeChallenge Minibatch size in the wine dataset Vietnamese.vtt |
25.39Кб |
| 84 - Data normalization.mp4 |
62.45Мб |
| 84 - Data normalization English.vtt |
17.07Кб |
| 84 - Data normalization Vietnamese.vtt |
21.57Кб |
| 85 - The importance of data normalization.mp4 |
72.74Мб |
| 85 - The importance of data normalization English.vtt |
12.02Кб |
| 85 - The importance of data normalization Vietnamese.vtt |
15.01Кб |
| 86 - Batch normalization.mp4 |
54.45Мб |
| 86 - Batch normalization English.vtt |
16.35Кб |
| 86 - Batch normalization Vietnamese.vtt |
20.47Кб |
| 87 - Batch normalization in practice.mp4 |
70.03Мб |
| 87 - Batch normalization in practice English.vtt |
9.61Кб |
| 87 - Batch normalization in practice Vietnamese.vtt |
12.13Кб |
| 88 - CodeChallenge Batchnormalize the qwerties.mp4 |
64.88Мб |
| 88 - CodeChallenge Batchnormalize the qwerties English.vtt |
6.52Кб |
| 88 - CodeChallenge Batchnormalize the qwerties Vietnamese.vtt |
8.24Кб |
| 89 - Activation functions.mp4 |
121.06Мб |
| 89 - Activation functions English.vtt |
23.00Кб |
| 89 - Activation functions Vietnamese.vtt |
28.24Кб |
| 8 - Running experiments to understand DL.mp4 |
74.84Мб |
| 8 - Running experiments to understand DL English.vtt |
16.76Кб |
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21.04Кб |
| 90 - Activation functions in PyTorch.mp4 |
101.28Мб |
| 90 - Activation functions in PyTorch English.vtt |
14.80Кб |
| 90 - Activation functions in PyTorch Vietnamese.vtt |
18.54Кб |
| 91 - Activation functions comparison.mp4 |
112.74Мб |
| 91 - Activation functions comparison English.vtt |
11.79Кб |
| 91 - Activation functions comparison Vietnamese.vtt |
14.88Кб |
| 92 - CodeChallenge Compare relu variants.mp4 |
63.97Мб |
| 92 - CodeChallenge Compare relu variants English.vtt |
9.82Кб |
| 92 - CodeChallenge Compare relu variants Vietnamese.vtt |
12.24Кб |
| 93 - CodeChallenge Predict sugar.mp4 |
134.25Мб |
| 93 - CodeChallenge Predict sugar English.vtt |
21.64Кб |
| 93 - CodeChallenge Predict sugar Vietnamese.vtt |
27.71Кб |
| 94 - Loss functions.mp4 |
96.20Мб |
| 94 - Loss functions English.vtt |
21.12Кб |
| 94 - Loss functions Vietnamese.vtt |
25.56Кб |
| 95 - Loss functions in PyTorch.mp4 |
154.73Мб |
| 95 - Loss functions in PyTorch English.vtt |
23.15Кб |
| 95 - Loss functions in PyTorch Vietnamese.vtt |
28.93Кб |
| 96 - More practice with multioutput ANNs.mp4 |
109.96Мб |
| 96 - More practice with multioutput ANNs English.vtt |
17.60Кб |
| 96 - More practice with multioutput ANNs Vietnamese.vtt |
21.86Кб |
| 97 - Optimizers minibatch momentum.mp4 |
59.43Мб |
| 97 - Optimizers minibatch momentum English.vtt |
23.78Кб |
| 97 - Optimizers minibatch momentum Vietnamese.vtt |
30.52Кб |
| 98 - SGD with momentum.mp4 |
62.10Мб |
| 98 - SGD with momentum English.vtt |
9.97Кб |
| 98 - SGD with momentum Vietnamese.vtt |
12.92Кб |
| 99 - Optimizers RMSprop Adam.mp4 |
52.52Мб |
| 99 - Optimizers RMSprop Adam English.vtt |
19.10Кб |
| 99 - Optimizers RMSprop Adam Vietnamese.vtt |
24.11Кб |
| 9 - Are artificial neurons like biological neurons.mp4 |
85.20Мб |
| 9 - Are artificial neurons like biological neurons English.vtt |
20.98Кб |
| 9 - Are artificial neurons like biological neurons Vietnamese.vtt |
27.44Кб |