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| [CourseClub.Me].url |
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| [CourseClub.Me].url |
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| [CourseClub.Me].url |
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| [CourseClub.Me].url |
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| [CourseClub.Me].url |
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| [CourseClub.Me].url |
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| [GigaCourse.Com].url |
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| [GigaCourse.Com].url |
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| [GigaCourse.Com].url |
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| [GigaCourse.Com].url |
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| [GigaCourse.Com].url |
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| 001 Anatomy of a torch dataset and dataloader_en.srt |
25.42Кб |
| 001 Anatomy of a torch dataset and dataloader.mp4 |
135.84Мб |
| 001 Bonus content.html |
3.64Кб |
| 001 Convolution concepts_en.srt |
31.18Кб |
| 001 Convolution concepts.mp4 |
97.99Мб |
| 001 Downloading and using the code_en.srt |
9.06Кб |
| 001 Downloading and using the code.mp4 |
45.65Мб |
| 001 DUDL-PythonCode.zip |
660.79Кб |
| 001 Explanation of weight matrix sizes_en.srt |
16.55Кб |
| 001 Explanation of weight matrix sizes.mp4 |
68.98Мб |
| 001 GAN What, why, and how_en.srt |
22.67Кб |
| 001 GAN What, why, and how.mp4 |
89.74Мб |
| 001 How to learn from the Python tutorial_en.srt |
4.67Кб |
| 001 How to learn from the Python tutorial.mp4 |
21.97Мб |
| 001 How to learn from this course_en.srt |
12.48Кб |
| 001 How to learn from this course.mp4 |
54.97Мб |
| 001 How to learn topic _X_ in deep learning_en.srt |
11.88Кб |
| 001 How to learn topic _X_ in deep learning.mp4 |
42.03Мб |
| 001 If-else statements_en.srt |
20.84Кб |
| 001 If-else statements.mp4 |
66.80Мб |
| 001 Indexing_en.srt |
17.40Кб |
| 001 Indexing.mp4 |
51.07Мб |
| 001 Inputs and outputs_en.srt |
10.16Кб |
| 001 Inputs and outputs.mp4 |
29.49Мб |
| 001 Leveraging sequences in deep learning_en.srt |
18.13Кб |
| 001 Leveraging sequences in deep learning.mp4 |
72.79Мб |
| 001 Overview of gradient descent_en.srt |
20.10Кб |
| 001 Overview of gradient descent.mp4 |
68.44Мб |
| 001 Printing and string interpolation_en.srt |
23.41Кб |
| 001 Printing and string interpolation.mp4 |
94.83Мб |
| 001 Project 1 A gratuitously complex adding machine_en.srt |
10.33Кб |
| 001 Project 1 A gratuitously complex adding machine.mp4 |
48.55Мб |
| 001 Project 1 Import and classify CIFAR10_en.srt |
10.17Кб |
| 001 Project 1 Import and classify CIFAR10.mp4 |
48.36Мб |
| 001 PyTorch or TensorFlow.html |
1.07Кб |
| 001 Regularization Concept and methods_en.srt |
18.35Кб |
| 001 Regularization Concept and methods.mp4 |
80.05Мб |
| 001 Should you watch the Python tutorial_en.srt |
5.92Кб |
| 001 Should you watch the Python tutorial.mp4 |
23.77Мб |
| 001 The canonical CNN architecture_en.srt |
15.12Кб |
| 001 The canonical CNN architecture.mp4 |
55.83Мб |
| 001 The perceptron and ANN architecture_en.srt |
26.89Кб |
| 001 The perceptron and ANN architecture.mp4 |
85.84Мб |
| 001 Transfer learning What, why, and when_en.srt |
23.86Кб |
| 001 Transfer learning What, why, and when.mp4 |
96.61Мб |
| 001 Two perspectives of the world_en.srt |
9.91Кб |
| 001 Two perspectives of the world.mp4 |
40.01Мб |
| 001 What are autoencoders and what do they do_en.srt |
16.30Кб |
| 001 What are autoencoders and what do they do.mp4 |
49.04Мб |
| 001 What are fully-connected and feedforward networks_en.srt |
6.70Кб |
| 001 What are fully-connected and feedforward networks.mp4 |
25.53Мб |
| 001 What are metaparameters_en.srt |
7.09Кб |
| 001 What are metaparameters.mp4 |
32.70Мб |
| 001 What is a GPU and why use it_en.srt |
21.62Кб |
| 001 What is a GPU and why use it.mp4 |
88.73Мб |
| 001 What is an artificial neural network_en.srt |
20.55Кб |
| 001 What is an artificial neural network.mp4 |
65.38Мб |
| 001 What is overfitting and is it as bad as they say_en.srt |
17.66Кб |
| 001 What is overfitting and is it as bad as they say.mp4 |
73.13Мб |
| 001 What is style transfer and how does it work_en.srt |
6.11Кб |
| 001 What is style transfer and how does it work.mp4 |
40.57Мб |
| 001 Will AI save us or destroy us_en.srt |
13.83Кб |
| 001 Will AI save us or destroy us.mp4 |
65.92Мб |
| 002 Accuracy, precision, recall, F1_en.srt |
17.32Кб |
| 002 Accuracy, precision, recall, F1.mp4 |
72.58Мб |
| 002 A geometric view of ANNs_en.srt |
18.65Кб |
| 002 A geometric view of ANNs.mp4 |
70.88Мб |
| 002 A surprising demo of weight initializations_en.srt |
23.00Кб |
| 002 A surprising demo of weight initializations.mp4 |
121.57Мб |
| 002 CNN to classify MNIST digits_en.srt |
36.60Кб |
| 002 CNN to classify MNIST digits.mp4 |
200.33Мб |
| 002 Cross-validation_en.srt |
24.05Кб |
| 002 Cross-validation.mp4 |
88.19Мб |
| 002 Data size and network size_en.srt |
22.54Кб |
| 002 Data size and network size.mp4 |
135.67Мб |
| 002 Denoising MNIST_en.srt |
21.93Кб |
| 002 Denoising MNIST.mp4 |
118.53Мб |
| 002 Example case studies_en.srt |
8.83Кб |
| 002 Example case studies.mp4 |
52.92Мб |
| 002 Feature maps and convolution kernels_en.srt |
13.45Кб |
| 002 Feature maps and convolution kernels.mp4 |
70.41Мб |
| 002 How models learn_en.srt |
18.07Кб |
| 002 How models learn.mp4 |
72.79Мб |
| 002 How RNNs work_en.srt |
20.96Кб |
| 002 How RNNs work.mp4 |
74.85Мб |
| 002 How to read academic DL papers_en.srt |
24.46Кб |
| 002 How to read academic DL papers.mp4 |
141.85Мб |
| 002 If-else statements, part 2_en.srt |
22.02Кб |
| 002 If-else statements, part 2.mp4 |
91.12Мб |
| 002 Implementation_en.srt |
14.24Кб |
| 002 Implementation.mp4 |
76.60Мб |
| 002 Introduction to this section_en.srt |
2.80Кб |
| 002 Introduction to this section.mp4 |
11.12Мб |
| 002 Linear GAN with MNIST_en.srt |
30.76Кб |
| 002 Linear GAN with MNIST.mp4 |
169.90Мб |
| 002 My policy on code-sharing_en.srt |
2.43Кб |
| 002 My policy on code-sharing.mp4 |
10.24Мб |
| 002 Plotting dots and lines_en.srt |
17.02Кб |
| 002 Plotting dots and lines.mp4 |
53.87Мб |
| 002 Project 1 My solution_en.srt |
16.30Кб |
| 002 Project 1 My solution_en.srt |
16.52Кб |
| 002 Project 1 My solution.mp4 |
99.75Мб |
| 002 Project 1 My solution.mp4 |
118.60Мб |
| 002 Python libraries (numpy)_en.srt |
19.26Кб |
| 002 Python libraries (numpy).mp4 |
63.39Мб |
| 002 Slicing_en.srt |
17.26Кб |
| 002 Slicing.mp4 |
48.45Мб |
| 002 The Gram matrix (feature activation covariance)_en.srt |
16.19Кб |
| 002 The Gram matrix (feature activation covariance).mp4 |
66.49Мб |
| 002 The MNIST dataset_en.srt |
17.70Кб |
| 002 The MNIST dataset.mp4 |
101.38Мб |
| 002 The wine quality dataset_en.srt |
24.77Кб |
| 002 The wine quality dataset.mp4 |
143.50Мб |
| 002 train() and eval() modes_en.srt |
9.82Кб |
| 002 train() and eval() modes.mp4 |
38.34Мб |
| 002 Transfer learning MNIST - FMNIST_en.srt |
14.02Кб |
| 002 Transfer learning MNIST - FMNIST.mp4 |
90.35Мб |
| 002 Using Udemy like a pro_en.srt |
11.84Кб |
| 002 Using Udemy like a pro.mp4 |
54.37Мб |
| 002 Variables_en.srt |
26.21Кб |
| 002 Variables.mp4 |
77.58Мб |
| 002 What about local minima_en.srt |
16.54Кб |
| 002 What about local minima.mp4 |
67.08Мб |
| 003 ANN math part 1 (forward prop)_en.srt |
21.38Кб |
| 003 ANN math part 1 (forward prop).mp4 |
73.12Мб |
| 003 APRF in code_en.srt |
9.03Кб |
| 003 APRF in code.mp4 |
51.79Мб |
| 003 CNN on shifted MNIST_en.srt |
11.66Кб |
| 003 CNN on shifted MNIST.mp4 |
58.34Мб |
| 003 CodeChallenge How many units_en.srt |
27.80Кб |
| 003 CodeChallenge How many units.mp4 |
135.38Мб |
| 003 CodeChallenge letters to numbers_en.srt |
19.77Кб |
| 003 CodeChallenge letters to numbers.mp4 |
118.74Мб |
| 003 CodeChallenge Linear GAN with FMNIST_en.srt |
13.38Кб |
| 003 CodeChallenge Linear GAN with FMNIST.mp4 |
62.73Мб |
| 003 CodeChallenge Minibatch size in the wine dataset_en.srt |
22.22Кб |
| 003 CodeChallenge Minibatch size in the wine dataset.mp4 |
118.79Мб |
| 003 CodeChallenge Run an experiment on the GPU_en.srt |
9.43Кб |
| 003 CodeChallenge Run an experiment on the GPU.mp4 |
52.99Мб |
| 003 CodeChallenge unbalanced data_en.srt |
28.21Кб |
| 003 CodeChallenge unbalanced data.mp4 |
166.26Мб |
| 003 Convolution in code_en.srt |
29.39Кб |
| 003 Convolution in code.mp4 |
173.10Мб |
| 003 Dropout regularization_en.srt |
30.42Кб |
| 003 Dropout regularization.mp4 |
138.39Мб |
| 003 FFN to classify digits_en.srt |
31.66Кб |
| 003 FFN to classify digits.mp4 |
161.85Мб |
| 003 For loops_en.srt |
24.28Кб |
| 003 For loops.mp4 |
87.13Мб |
| 003 Generalization_en.srt |
8.50Кб |
| 003 Generalization.mp4 |
32.44Мб |
| 003 Gradient descent in 1D_en.srt |
23.78Кб |
| 003 Gradient descent in 1D.mp4 |
119.29Мб |
| 003 Math and printing_en.srt |
25.73Кб |
| 003 Math and printing.mp4 |
78.50Мб |
| 003 Project 2 CIFAR-autoencoder_en.srt |
6.75Кб |
| 003 Project 2 CIFAR-autoencoder.mp4 |
33.37Мб |
| 003 Project 2 Predicting heart disease_en.srt |
10.57Кб |
| 003 Project 2 Predicting heart disease.mp4 |
50.61Мб |
| 003 Python libraries (pandas)_en.srt |
19.51Кб |
| 003 Python libraries (pandas).mp4 |
81.19Мб |
| 003 Some other possible ethical scenarios_en.srt |
14.65Кб |
| 003 Some other possible ethical scenarios.mp4 |
66.25Мб |
| 003 Spectral theories in mathematics_en.srt |
13.09Кб |
| 003 Spectral theories in mathematics.mp4 |
51.06Мб |
| 003 Subplot geometry_en.srt |
22.24Кб |
| 003 Subplot geometry.mp4 |
86.78Мб |
| 003 Theory Why and how to initialize weights_en.srt |
17.59Кб |
| 003 Theory Why and how to initialize weights.mp4 |
79.41Мб |
| 003 The RNN class in PyTorch_en.srt |
25.94Кб |
| 003 The RNN class in PyTorch.mp4 |
122.98Мб |
| 003 The role of DL in science and knowledge_en.srt |
22.48Кб |
| 003 The role of DL in science and knowledge.mp4 |
34.76Мб |
| 003 The style transfer algorithm_en.srt |
14.54Кб |
| 003 The style transfer algorithm.mp4 |
67.31Мб |
| 004 AEs for occlusion_en.srt |
24.48Кб |
| 004 AEs for occlusion.mp4 |
138.20Мб |
| 004 ANN math part 2 (errors, loss, cost)_en.srt |
13.39Кб |
| 004 ANN math part 2 (errors, loss, cost).mp4 |
48.47Мб |
| 004 APRF example 1 wine quality_en.srt |
18.52Кб |
| 004 APRF example 1 wine quality.mp4 |
107.35Мб |
| 004 Classify Gaussian blurs_en.srt |
33.00Кб |
| 004 Classify Gaussian blurs.mp4 |
185.14Мб |
| 004 CNN GAN with Gaussians_en.srt |
21.29Кб |
| 004 CNN GAN with Gaussians.mp4 |
135.70Мб |
| 004 CodeChallenge Binarized MNIST images_en.srt |
7.10Кб |
| 004 CodeChallenge Binarized MNIST images.mp4 |
40.78Мб |
| 004 CodeChallenge unfortunate starting value_en.srt |
15.37Кб |
| 004 CodeChallenge unfortunate starting value.mp4 |
77.09Мб |
| 004 CodeChallenge Weight variance inits_en.srt |
17.75Кб |
| 004 CodeChallenge Weight variance inits.mp4 |
103.96Мб |
| 004 Convolution parameters (stride, padding)_en.srt |
17.41Кб |
| 004 Convolution parameters (stride, padding).mp4 |
66.93Мб |
| 004 Cross-validation -- manual separation_en.srt |
17.89Кб |
| 004 Cross-validation -- manual separation.mp4 |
98.30Мб |
| 004 Data normalization_en.srt |
18.96Кб |
| 004 Data normalization.mp4 |
59.81Мб |
| 004 Dropout regularization in practice_en.srt |
32.13Кб |
| 004 Dropout regularization in practice.mp4 |
183.23Мб |
| 004 Enumerate and zip_en.srt |
15.41Кб |
| 004 Enumerate and zip.mp4 |
58.59Мб |
| 004 Famous CNN architectures_en.srt |
8.39Кб |
| 004 Famous CNN architectures.mp4 |
41.28Мб |
| 004 Getting help on functions_en.srt |
10.65Кб |
| 004 Getting help on functions.mp4 |
48.60Мб |
| 004 Lists (1 of 2)_en.srt |
19.65Кб |
| 004 Lists (1 of 2).mp4 |
55.04Мб |
| 004 Making the graphs look nicer_en.srt |
25.96Кб |
| 004 Making the graphs look nicer.mp4 |
107.66Мб |
| 004 Predicting alternating sequences_en.srt |
27.77Кб |
| 004 Predicting alternating sequences.mp4 |
160.16Мб |
| 004 Project 2 My solution_en.srt |
26.69Кб |
| 004 Project 2 My solution.mp4 |
155.73Мб |
| 004 Project 3 FMNIST_en.srt |
4.93Кб |
| 004 Project 3 FMNIST.mp4 |
26.45Мб |
| 004 Running experiments to understand DL_en.srt |
18.53Кб |
| 004 Running experiments to understand DL.mp4 |
74.84Мб |
| 004 Terms and datatypes in math and computers_en.srt |
10.27Кб |
| 004 Terms and datatypes in math and computers.mp4 |
38.08Мб |
| 004 Transferring the screaming bathtub_en.srt |
31.05Кб |
| 004 Transferring the screaming bathtub.mp4 |
216.82Мб |
| 004 What to do about unbalanced designs_en.srt |
10.75Кб |
| 004 What to do about unbalanced designs.mp4 |
54.21Мб |
| 004 Will deep learning take our jobs_en.srt |
14.35Кб |
| 004 Will deep learning take our jobs.mp4 |
75.14Мб |
| 005 Accountability and making ethical AI_en.srt |
16.10Кб |
| 005 Accountability and making ethical AI.mp4 |
70.06Мб |
| 005 ANN math part 3 (backprop)_en.srt |
14.71Кб |
| 005 ANN math part 3 (backprop).mp4 |
52.89Мб |
| 005 APRF example 2 MNIST_en.srt |
16.52Кб |
| 005 APRF example 2 MNIST.mp4 |
98.62Мб |
| 005 Are artificial neurons like biological neurons_en.srt |
23.29Кб |
| 005 Are artificial neurons like biological neurons.mp4 |
114.65Мб |
| 005 CodeChallenge Data normalization_en.srt |
23.57Кб |
| 005 CodeChallenge Data normalization.mp4 |
96.25Мб |
| 005 CodeChallenge Gaussians with fewer layers_en.srt |
8.61Кб |
| 005 CodeChallenge Gaussians with fewer layers.mp4 |
53.06Мб |
| 005 CodeChallenge sine wave extrapolation_en.srt |
37.55Кб |
| 005 CodeChallenge sine wave extrapolation.mp4 |
195.67Мб |
| 005 CodeChallenge Style transfer with AlexNet_en.srt |
10.07Кб |
| 005 CodeChallenge Style transfer with AlexNet.mp4 |
53.47Мб |
| 005 Continue_en.srt |
9.71Кб |
| 005 Continue.mp4 |
33.03Мб |
| 005 Converting reality to numbers_en.srt |
9.21Кб |
| 005 Converting reality to numbers.mp4 |
33.21Мб |
| 005 Creating functions_en.srt |
29.69Кб |
| 005 Creating functions.mp4 |
88.43Мб |
| 005 Cross-validation -- scikitlearn_en.srt |
29.31Кб |
| 005 Cross-validation -- scikitlearn.mp4 |
142.88Мб |
| 005 Data oversampling in MNIST_en.srt |
23.24Кб |
| 005 Data oversampling in MNIST.mp4 |
122.59Мб |
| 005 Dropout example 2_en.srt |
8.83Кб |
| 005 Dropout example 2.mp4 |
53.87Мб |
| 005 Examine feature map activations_en.srt |
39.00Кб |
| 005 Examine feature map activations.mp4 |
260.56Мб |
| 005 Gradient descent in 2D_en.srt |
20.74Кб |
| 005 Gradient descent in 2D.mp4 |
96.38Мб |
| 005 Lists (2 of 2)_en.srt |
14.00Кб |
| 005 Lists (2 of 2).mp4 |
46.69Мб |
| 005 Project 3 FFN for missing data interpolation_en.srt |
13.85Кб |
| 005 Project 3 FFN for missing data interpolation.mp4 |
45.39Мб |
| 005 Project 4 Psychometric functions in CNNs_en.srt |
10.74Кб |
| 005 Project 4 Psychometric functions in CNNs_en.vtt |
14.22Кб |
| 005 Project 4 Psychometric functions in CNNs.mp4 |
76.27Мб |
| 005 Seaborn_en.srt |
15.11Кб |
| 005 Seaborn.mp4 |
59.72Мб |
| 005 The Conv2 class in PyTorch_en.srt |
18.19Кб |
| 005 The Conv2 class in PyTorch.mp4 |
100.19Мб |
| 005 The importance of data normalization_en.srt |
13.26Кб |
| 005 The importance of data normalization.mp4 |
64.65Мб |
| 005 The latent code of MNIST_en.srt |
30.47Кб |
| 005 The latent code of MNIST.mp4 |
161.81Мб |
| 005 Transfer learning with ResNet-18_en.srt |
23.64Кб |
| 005 Transfer learning with ResNet-18.mp4 |
148.46Мб |
| 005 Xavier and Kaiming initializations_en.srt |
21.70Кб |
| 005 Xavier and Kaiming initializations.mp4 |
134.08Мб |
| 006 ANN for regression_en.srt |
34.52Кб |
| 006 ANN for regression.mp4 |
135.50Мб |
| 006 Autoencoder with tied weights_en.srt |
33.51Кб |
| 006 Autoencoder with tied weights.mp4 |
177.74Мб |
| 006 Batch normalization_en.srt |
18.02Кб |
| 006 Batch normalization.mp4 |
76.81Мб |
| 006 CNN GAN with FMNIST_en.srt |
8.88Кб |
| 006 CNN GAN with FMNIST.mp4 |
54.58Мб |
| 006 CodeChallenge 2D gradient ascent_en.srt |
7.24Кб |
| 006 CodeChallenge 2D gradient ascent.mp4 |
39.36Мб |
| 006 CodeChallenge Choose the parameters_en.srt |
9.75Кб |
| 006 CodeChallenge Choose the parameters.mp4 |
58.71Мб |
| 006 CodeChallenge MNIST with unequal groups_en.srt |
12.25Кб |
| 006 CodeChallenge MNIST with unequal groups.mp4 |
25.07Мб |
| 006 CodeChallenge Softcode internal parameters_en.srt |
24.12Кб |
| 006 CodeChallenge Softcode internal parameters.mp4 |
120.10Мб |
| 006 CodeChallenge VGG-16_en.srt |
4.87Кб |
| 006 CodeChallenge VGG-16.mp4 |
20.28Мб |
| 006 CodeChallenge Xavier vs. Kaiming_en.srt |
23.71Кб |
| 006 CodeChallenge Xavier vs. Kaiming.mp4 |
126.50Мб |
| 006 Cross-validation -- DataLoader_en.srt |
27.51Кб |
| 006 Cross-validation -- DataLoader.mp4 |
172.32Мб |
| 006 Data noise augmentation (with devset+test)_en.srt |
17.93Кб |
| 006 Data noise augmentation (with devset+test).mp4 |
106.09Мб |
| 006 Distributions of weights pre- and post-learning_en.srt |
21.22Кб |
| 006 Distributions of weights pre- and post-learning.mp4 |
116.26Мб |
| 006 Global and local variable scopes_en.srt |
18.92Кб |
| 006 Global and local variable scopes.mp4 |
65.96Мб |
| 006 Images_en.srt |
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| 006 Images.mp4 |
93.56Мб |
| 006 Initializing variables_en.srt |
24.64Кб |
| 006 Initializing variables.mp4 |
91.05Мб |
| 006 More on RNNs Hidden states, embeddings_en.srt |
22.04Кб |
| 006 More on RNNs Hidden states, embeddings.mp4 |
29.04Мб |
| 006 Project 3 My solution_en.srt |
11.43Кб |
| 006 Project 3 My solution.mp4 |
75.48Мб |
| 006 Tuples_en.srt |
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| 006 Tuples.mp4 |
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| 006 Vector and matrix transpose_en.srt |
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| 006 Vector and matrix transpose.mp4 |
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| 006 Weight regularization (L1L2) math_en.srt |
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| 006 Weight regularization (L1L2) math.mp4 |
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| 007 Batch normalization in practice_en.srt |
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| 007 Batch normalization in practice.mp4 |
61.76Мб |
| 007 Booleans_en.srt |
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| 007 Booleans.mp4 |
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| 007 CodeChallenge CNN GAN with CIFAR_en.srt |
11.22Кб |
| 007 CodeChallenge CNN GAN with CIFAR.mp4 |
60.77Мб |
| 007 CodeChallenge How wide the FC_en.srt |
15.86Кб |
| 007 CodeChallenge How wide the FC.mp4 |
94.08Мб |
| 007 CodeChallenge Identically random weights_en.srt |
17.26Кб |
| 007 CodeChallenge Identically random weights.mp4 |
88.17Мб |
| 007 CodeChallenge manipulate regression slopes_en.srt |
27.25Кб |
| 007 CodeChallenge manipulate regression slopes.mp4 |
139.12Мб |
| 007 CodeChallenge MNIST and breadth vs. depth_en.srt |
17.09Кб |
| 007 CodeChallenge MNIST and breadth vs. depth.mp4 |
95.21Мб |
| 007 Computation time_en.srt |
13.73Кб |
| 007 Computation time.mp4 |
81.73Мб |
| 007 Copies and referents of variables_en.srt |
6.98Кб |
| 007 Copies and referents of variables.mp4 |
23.78Мб |
| 007 Data feature augmentation_en.srt |
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| 007 Data feature augmentation.mp4 |
158.27Мб |
| 007 Export plots in low and high resolution_en.srt |
10.94Кб |
| 007 Export plots in low and high resolution.mp4 |
17.17Мб |
| 007 GRU and LSTM_en.srt |
32.14Кб |
| 007 GRU and LSTM.mp4 |
129.66Мб |
| 007 L2 regularization in practice_en.srt |
18.27Кб |
| 007 L2 regularization in practice.mp4 |
110.47Мб |
| 007 OMG it's the dot product!_en.srt |
13.43Кб |
| 007 OMG it's the dot product!.mp4 |
50.11Мб |
| 007 Parametric experiments on g.d_en.srt |
26.16Кб |
| 007 Parametric experiments on g.d.mp4 |
135.61Мб |
| 007 Pretraining with autoencoders_en.srt |
27.70Кб |
| 007 Pretraining with autoencoders.mp4 |
156.58Мб |
| 007 Single-line loops (list comprehension)_en.srt |
20.91Кб |
| 007 Single-line loops (list comprehension).mp4 |
75.14Мб |
| 007 Splitting data into train, devset, test_en.srt |
13.31Кб |
| 007 Splitting data into train, devset, test.mp4 |
79.21Мб |
| 007 Transpose convolution_en.srt |
19.17Кб |
| 007 Transpose convolution.mp4 |
92.89Мб |
| 008 ANN for classifying qwerties_en.srt |
32.73Кб |
| 008 ANN for classifying qwerties.mp4 |
151.12Мб |
| 008 Better performance in test than train_en.srt |
11.54Кб |
| 008 Better performance in test than train.mp4 |
44.83Мб |
| 008 CIFAR10 with autoencoder-pretrained model_en.srt |
24.93Кб |
| 008 CIFAR10 with autoencoder-pretrained model.mp4 |
153.34Мб |
| 008 Classes and object-oriented programming_en.srt |
25.61Кб |
| 008 Classes and object-oriented programming.mp4 |
108.18Мб |
| 008 CodeChallenge Batch-normalize the qwerties_en.srt |
7.23Кб |
| 008 CodeChallenge Batch-normalize the qwerties.mp4 |
41.43Мб |
| 008 CodeChallenge fixed vs. dynamic learning rate_en.srt |
22.55Кб |
| 008 CodeChallenge fixed vs. dynamic learning rate.mp4 |
113.60Мб |
| 008 CodeChallenge Optimizers and MNIST_en.srt |
9.56Кб |
| 008 CodeChallenge Optimizers and MNIST.mp4 |
46.26Мб |
| 008 Cross-validation on regression_en.srt |
11.53Кб |
| 008 Cross-validation on regression.mp4 |
60.35Мб |
| 008 Dictionaries_en.srt |
16.36Кб |
| 008 Dictionaries.mp4 |
50.67Мб |
| 008 Do autoencoders clean Gaussians_en.srt |
23.50Кб |
| 008 Do autoencoders clean Gaussians.mp4 |
147.88Мб |
| 008 Freezing weights during learning_en.srt |
18.54Кб |
| 008 Freezing weights during learning.mp4 |
93.15Мб |
| 008 Getting data into colab_en.srt |
8.52Кб |
| 008 Getting data into colab.mp4 |
43.75Мб |
| 008 L1 regularization in practice_en.srt |
16.79Кб |
| 008 L1 regularization in practice.mp4 |
99.44Мб |
| 008 Matrix multiplication_en.srt |
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| 008 Matrix multiplication.mp4 |
85.67Мб |
| 008 Maxmean pooling_en.srt |
25.71Кб |
| 008 Maxmean pooling.mp4 |
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| 008 The LSTM and GRU classes_en.srt |
19.26Кб |
| 008 The LSTM and GRU classes.mp4 |
120.14Мб |
| 008 while loops_en.srt |
26.87Кб |
| 008 while loops.mp4 |
91.10Мб |
| 009 Activation functions_en.srt |
25.53Кб |
| 009 Activation functions.mp4 |
97.03Мб |
| 009 Broadcasting in numpy_en.srt |
20.52Кб |
| 009 Broadcasting in numpy.mp4 |
71.05Мб |
| 009 CodeChallenge AEs and occluded Gaussians_en.srt |
13.49Кб |
| 009 CodeChallenge AEs and occluded Gaussians.mp4 |
28.58Мб |
| 009 Learning rates comparison_en.srt |
34.85Кб |
| 009 Learning rates comparison.mp4 |
168.64Мб |
| 009 Learning-related changes in weights_en.srt |
31.55Кб |
| 009 Learning-related changes in weights.mp4 |
146.78Мб |
| 009 Lorem ipsum_en.srt |
35.99Кб |
| 009 Lorem ipsum.mp4 |
192.53Мб |
| 009 Pooling in PyTorch_en.srt |
18.90Кб |
| 009 Pooling in PyTorch.mp4 |
81.02Мб |
| 009 Save and load trained models_en.srt |
8.61Кб |
| 009 Save and load trained models.mp4 |
55.71Мб |
| 009 Scrambled MNIST_en.srt |
10.82Кб |
| 009 Scrambled MNIST.mp4 |
60.17Мб |
| 009 Softmax_en.srt |
26.74Кб |
| 009 Softmax.mp4 |
95.96Мб |
| 009 Training in mini-batches_en.srt |
16.24Кб |
| 009 Training in mini-batches.mp4 |
62.12Мб |
| 009 Vanishing and exploding gradients_en.srt |
8.71Кб |
| 009 Vanishing and exploding gradients.mp4 |
30.24Мб |
| 010 Activation functions in PyTorch_en.srt |
16.35Кб |
| 010 Activation functions in PyTorch.mp4 |
91.46Мб |
| 010 Batch training in action_en.srt |
15.06Кб |
| 010 Batch training in action.mp4 |
89.10Мб |
| 010 CodeChallenge Custom loss functions_en.srt |
28.77Кб |
| 010 CodeChallenge Custom loss functions.mp4 |
132.89Мб |
| 010 Function error checking and handling_en.srt |
24.39Кб |
| 010 Function error checking and handling.mp4 |
99.87Мб |
| 010 Logarithms_en.srt |
11.05Кб |
| 010 Logarithms.mp4 |
43.88Мб |
| 010 Multilayer ANN_en.srt |
28.29Кб |
| 010 Multilayer ANN.mp4 |
144.70Мб |
| 010 Save the best-performing model_en.srt |
21.15Кб |
| 010 Save the best-performing model.mp4 |
126.50Мб |
| 010 Shifted MNIST_en.srt |
15.84Кб |
| 010 Shifted MNIST.mp4 |
77.91Мб |
| 010 Tangent Notebook revision history_en.srt |
2.66Кб |
| 010 Tangent Notebook revision history.mp4 |
9.88Мб |
| 010 To pool or to stride_en.srt |
13.75Кб |
| 010 To pool or to stride.mp4 |
55.51Мб |
| 010 Use default inits or apply your own_en.srt |
6.12Кб |
| 010 Use default inits or apply your own.mp4 |
28.05Мб |
| 011 Activation functions comparison_en.srt |
13.08Кб |
| 011 Activation functions comparison.mp4 |
73.90Мб |
| 011 CodeChallenge The mystery of the missing 7_en.srt |
15.17Кб |
| 011 CodeChallenge The mystery of the missing 7.mp4 |
74.25Мб |
| 011 Discover the Gaussian parameters_en.srt |
22.41Кб |
| 011 Discover the Gaussian parameters.mp4 |
136.65Мб |
| 011 Entropy and cross-entropy_en.srt |
24.46Кб |
| 011 Entropy and cross-entropy.mp4 |
106.00Мб |
| 011 Image transforms_en.srt |
23.01Кб |
| 011 Image transforms.mp4 |
129.90Мб |
| 011 Linear solutions to linear problems_en.srt |
11.73Кб |
| 011 Linear solutions to linear problems.mp4 |
50.37Мб |
| 011 The importance of equal batch sizes_en.srt |
9.12Кб |
| 011 The importance of equal batch sizes.mp4 |
60.11Мб |
| 011 Where to find online datasets_en.srt |
7.89Кб |
| 011 Where to find online datasets.mp4 |
41.70Мб |
| 012 CodeChallenge Compare relu variants_en.srt |
10.87Кб |
| 012 CodeChallenge Compare relu variants.mp4 |
63.97Мб |
| 012 CodeChallenge Effects of mini-batch size_en.srt |
17.42Кб |
| 012 CodeChallenge Effects of mini-batch size.mp4 |
95.42Мб |
| 012 Creating and using custom DataLoaders_en.srt |
25.50Кб |
| 012 Creating and using custom DataLoaders.mp4 |
139.53Мб |
| 012 Minmax and argminargmax_en.srt |
17.49Кб |
| 012 Minmax and argminargmax.mp4 |
88.21Мб |
| 012 The EMNIST dataset (letter recognition)_en.srt |
34.76Кб |
| 012 The EMNIST dataset (letter recognition).mp4 |
201.31Мб |
| 012 Universal approximation theorem_en.srt |
11.28Кб |
| 012 Universal approximation theorem.mp4 |
49.18Мб |
| 012 Why multilayer linear models don't exist_en.srt |
8.86Кб |
| 012 Why multilayer linear models don't exist.mp4 |
26.46Мб |
| 013 CodeChallenge Predict sugar_en.srt |
24.06Кб |
| 013 CodeChallenge Predict sugar.mp4 |
122.10Мб |
| 013 Dropout in CNNs_en.srt |
13.68Кб |
| 013 Dropout in CNNs.mp4 |
82.73Мб |
| 013 Mean and variance_en.srt |
21.74Кб |
| 013 Mean and variance.mp4 |
81.42Мб |
| 013 Multi-output ANN (iris dataset)_en.srt |
36.08Кб |
| 013 Multi-output ANN (iris dataset).mp4 |
186.77Мб |
| 014 CodeChallenge How low can you go_en.srt |
9.58Кб |
| 014 CodeChallenge How low can you go.mp4 |
55.36Мб |
| 014 CodeChallenge more qwerties!_en.srt |
17.13Кб |
| 014 CodeChallenge more qwerties!.mp4 |
95.10Мб |
| 014 Loss functions_en.srt |
23.32Кб |
| 014 Loss functions.mp4 |
90.30Мб |
| 014 Random sampling and sampling variability_en.srt |
15.75Кб |
| 014 Random sampling and sampling variability.mp4 |
85.42Мб |
| 015 CodeChallenge Varying number of channels_en.srt |
18.92Кб |
| 015 CodeChallenge Varying number of channels.mp4 |
92.37Мб |
| 015 Comparing the number of hidden units_en.srt |
14.09Кб |
| 015 Comparing the number of hidden units.mp4 |
71.15Мб |
| 015 Loss functions in PyTorch_en.srt |
25.85Кб |
| 015 Loss functions in PyTorch.mp4 |
138.10Мб |
| 015 Reproducible randomness via seeding_en.srt |
11.32Кб |
| 015 Reproducible randomness via seeding.mp4 |
69.70Мб |
| 016 Depth vs. breadth number of parameters_en.srt |
24.75Кб |
| 016 Depth vs. breadth number of parameters.mp4 |
132.07Мб |
| 016 More practice with multioutput ANNs_en.srt |
19.57Кб |
| 016 More practice with multioutput ANNs.mp4 |
99.80Мб |
| 016 So many possibilities! How to create a CNN_en.srt |
6.27Кб |
| 016 So many possibilities! How to create a CNN.mp4 |
21.04Мб |
| 016 The t-test_en.srt |
18.69Кб |
| 016 The t-test.mp4 |
81.36Мб |
| 017 Defining models using sequential vs. class_en.srt |
18.45Кб |
| 017 Defining models using sequential vs. class.mp4 |
89.48Мб |
| 017 Derivatives intuition and polynomials_en.srt |
23.48Кб |
| 017 Derivatives intuition and polynomials.mp4 |
80.30Мб |
| 017 Optimizers (minibatch, momentum)_en.srt |
26.39Кб |
| 017 Optimizers (minibatch, momentum).mp4 |
98.07Мб |
| 018 Derivatives find minima_en.srt |
11.71Кб |
| 018 Derivatives find minima.mp4 |
45.47Мб |
| 018 Model depth vs. breadth_en.srt |
29.73Кб |
| 018 Model depth vs. breadth.mp4 |
158.91Мб |
| 018 SGD with momentum_en.srt |
11.12Кб |
| 018 SGD with momentum.mp4 |
62.10Мб |
| 019 CodeChallenge convert sequential to class_en.srt |
9.36Кб |
| 019 CodeChallenge convert sequential to class.mp4 |
51.44Мб |
| 019 Derivatives product and chain rules_en.srt |
13.04Кб |
| 019 Derivatives product and chain rules.mp4 |
55.63Мб |
| 019 Optimizers (RMSprop, Adam)_en.srt |
21.25Кб |
| 019 Optimizers (RMSprop, Adam).mp4 |
76.73Мб |
| 020 Diversity of ANN visual representations.html |
517б |
| 020 Optimizers comparison_en.srt |
14.10Кб |
| 020 Optimizers comparison.mp4 |
86.88Мб |
| 021 CodeChallenge Optimizers and... something_en.srt |
9.03Кб |
| 021 CodeChallenge Optimizers and... something.mp4 |
49.77Мб |
| 021 Reflection Are DL models understandable yet_en.srt |
11.95Кб |
| 021 Reflection Are DL models understandable yet.mp4 |
58.59Мб |
| 022 CodeChallenge Adam with L2 regularization_en.srt |
9.94Кб |
| 022 CodeChallenge Adam with L2 regularization.mp4 |
53.00Мб |
| 023 Learning rate decay_en.srt |
17.23Кб |
| 023 Learning rate decay.mp4 |
96.90Мб |
| 024 How to pick the right metaparameters_en.srt |
16.08Кб |
| 024 How to pick the right metaparameters.mp4 |
61.74Мб |