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文档列表

19 - Understand and design CNNs/005 Examine feature map activations.mp4 273.2 MB
22 - Style transfer/004 Transferring the screaming bathtub.mp4 227.4 MB
19 - Understand and design CNNs/012 The EMNIST dataset (letter recognition).mp4 211.1 MB
19 - Understand and design CNNs/002 CNN to classify MNIST digits.mp4 210.1 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/005 CodeChallenge sine wave extrapolation.mp4 205.2 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/009 Lorem ipsum.mp4 201.9 MB
07 - ANNs (Artificial Neural Networks)/013 Multi-output ANN (iris dataset).mp4 195.8 MB
19 - Understand and design CNNs/004 Classify Gaussian blurs.mp4 194.1 MB
09 - Regularization/004 Dropout regularization in practice.mp4 192.1 MB
16 - Autoencoders/006 Autoencoder with tied weights.mp4 186.4 MB
18 - Convolution and transformations/003 Convolution in code.mp4 181.5 MB
08 - Overfitting and cross-validation/006 Cross-validation -- DataLoader.mp4 180.7 MB
23 - Generative adversarial networks/002 Linear GAN with MNIST.mp4 178.2 MB
07 - ANNs (Artificial Neural Networks)/009 Learning rates comparison.mp4 176.8 MB
12 - More on data/003 CodeChallenge unbalanced data.mp4 174.3 MB
11 - FFNs (Feed-Forward Networks)/003 FFN to classify digits.mp4 169.7 MB
16 - Autoencoders/005 The latent code of MNIST.mp4 169.7 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/004 Predicting alternating sequences.mp4 167.9 MB
07 - ANNs (Artificial Neural Networks)/018 Model depth vs. breadth.mp4 166.6 MB
12 - More on data/007 Data feature augmentation.mp4 166.0 MB
21 - Transfer learning/007 Pretraining with autoencoders.mp4 164.2 MB
14 - FFN milestone projects/004 Project 2 My solution.mp4 163.3 MB
21 - Transfer learning/008 CIFAR10 with autoencoder-pretrained model.mp4 160.8 MB
07 - ANNs (Artificial Neural Networks)/008 ANN for classifying qwerties.mp4 158.5 MB
21 - Transfer learning/005 Transfer learning with ResNet-18.mp4 155.7 MB
19 - Understand and design CNNs/008 Do autoencoders clean Gaussians.mp4 155.1 MB
15 - Weight inits and investigations/009 Learning-related changes in weights.mp4 153.9 MB
07 - ANNs (Artificial Neural Networks)/010 Multilayer ANN.mp4 151.7 MB
10 - Metaparameters (activations, optimizers)/002 The wine quality dataset.mp4 150.5 MB
08 - Overfitting and cross-validation/005 Cross-validation -- scikitlearn.mp4 149.8 MB
26 - Where to go from here/002 How to read academic DL papers.mp4 148.7 MB
18 - Convolution and transformations/012 Creating and using custom DataLoaders.mp4 146.3 MB
07 - ANNs (Artificial Neural Networks)/007 CodeChallenge manipulate regression slopes.mp4 145.9 MB
09 - Regularization/003 Dropout regularization.mp4 145.1 MB
16 - Autoencoders/004 AEs for occlusion.mp4 144.9 MB
10 - Metaparameters (activations, optimizers)/015 Loss functions in PyTorch.mp4 144.8 MB
19 - Understand and design CNNs/011 Discover the Gaussian parameters.mp4 143.3 MB
12 - More on data/001 Anatomy of a torch dataset and dataloader.mp4 142.4 MB
23 - Generative adversarial networks/004 CNN GAN with Gaussians.mp4 142.3 MB
12 - More on data/002 Data size and network size.mp4 142.3 MB
06 - Gradient descent/007 Parametric experiments on g.d.mp4 142.2 MB
07 - ANNs (Artificial Neural Networks)/006 ANN for regression.mp4 142.1 MB
16 - Autoencoders/003 CodeChallenge How many units.mp4 142.0 MB
15 - Weight inits and investigations/005 Xavier and Kaiming initializations.mp4 140.6 MB
19 - Understand and design CNNs/010 CodeChallenge Custom loss functions.mp4 139.3 MB
07 - ANNs (Artificial Neural Networks)/016 Depth vs. breadth number of parameters.mp4 138.5 MB
18 - Convolution and transformations/011 Image transforms.mp4 136.2 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/007 GRU and LSTM.mp4 136.0 MB
15 - Weight inits and investigations/006 CodeChallenge Xavier vs. Kaiming.mp4 132.6 MB
12 - More on data/010 Save the best-performing model.mp4 132.6 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/003 The RNN class in PyTorch.mp4 129.0 MB
12 - More on data/005 Data oversampling in MNIST.mp4 128.5 MB
10 - Metaparameters (activations, optimizers)/013 CodeChallenge Predict sugar.mp4 128.0 MB
15 - Weight inits and investigations/002 A surprising demo of weight initializations.mp4 127.5 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/008 The LSTM and GRU classes.mp4 126.0 MB
19 - Understand and design CNNs/006 CodeChallenge Softcode internal parameters.mp4 125.9 MB
06 - Gradient descent/003 Gradient descent in 1D.mp4 125.1 MB
10 - Metaparameters (activations, optimizers)/003 CodeChallenge Minibatch size in the wine dataset.mp4 124.6 MB
21 - Transfer learning/003 CodeChallenge letters to numbers.mp4 124.5 MB
20 - CNN milestone projects/002 Project 1 My solution.mp4 124.4 MB
16 - Autoencoders/002 Denoising MNIST.mp4 124.3 MB
11 - FFNs (Feed-Forward Networks)/006 Distributions of weights pre- and post-learning.mp4 121.9 MB
03 - Concepts in deep learning/005 Are artificial neurons like biological neurons.mp4 120.2 MB
06 - Gradient descent/008 CodeChallenge fixed vs. dynamic learning rate.mp4 119.1 MB
09 - Regularization/007 L2 regularization in practice.mp4 115.8 MB
29 - Python intro Functions/008 Classes and object-oriented programming.mp4 113.4 MB
31 - Python intro Text and plots/004 Making the graphs look nicer.mp4 112.9 MB
13 - Measuring model performance/004 APRF example 1 wine quality.mp4 112.6 MB
12 - More on data/006 Data noise augmentation (with devset+test).mp4 111.2 MB
05 - Math, numpy, PyTorch/011 Entropy and cross-entropy.mp4 111.1 MB
15 - Weight inits and investigations/004 CodeChallenge Weight variance inits.mp4 109.0 MB
11 - FFNs (Feed-Forward Networks)/002 The MNIST dataset.mp4 106.3 MB
18 - Convolution and transformations/005 The Conv2 class in PyTorch.mp4 105.1 MB
30 - Python intro Flow control/010 Function error checking and handling.mp4 104.7 MB
10 - Metaparameters (activations, optimizers)/016 More practice with multioutput ANNs.mp4 104.6 MB
14 - FFN milestone projects/002 Project 1 My solution.mp4 104.6 MB
09 - Regularization/008 L1 regularization in practice.mp4 104.3 MB
13 - Measuring model performance/005 APRF example 2 MNIST.mp4 103.4 MB
08 - Overfitting and cross-validation/004 Cross-validation -- manual separation.mp4 103.1 MB
10 - Metaparameters (activations, optimizers)/017 Optimizers (minibatch, momentum).mp4 102.8 MB
18 - Convolution and transformations/001 Convolution concepts.mp4 102.7 MB
10 - Metaparameters (activations, optimizers)/009 Activation functions.mp4 101.7 MB
10 - Metaparameters (activations, optimizers)/023 Learning rate decay.mp4 101.6 MB
21 - Transfer learning/001 Transfer learning What, why, and when.mp4 101.3 MB
06 - Gradient descent/005 Gradient descent in 2D.mp4 101.1 MB
11 - FFNs (Feed-Forward Networks)/005 CodeChallenge Data normalization.mp4 100.9 MB
05 - Math, numpy, PyTorch/009 Softmax.mp4 100.6 MB
09 - Regularization/012 CodeChallenge Effects of mini-batch size.mp4 100.1 MB
11 - FFNs (Feed-Forward Networks)/007 CodeChallenge MNIST and breadth vs. depth.mp4 99.8 MB
07 - ANNs (Artificial Neural Networks)/014 CodeChallenge more qwerties!.mp4 99.7 MB
31 - Python intro Text and plots/001 Printing and string interpolation.mp4 99.4 MB
19 - Understand and design CNNs/007 CodeChallenge How wide the FC.mp4 98.6 MB
31 - Python intro Text and plots/006 Images.mp4 98.1 MB
15 - Weight inits and investigations/008 Freezing weights during learning.mp4 97.7 MB
18 - Convolution and transformations/007 Transpose convolution.mp4 97.4 MB
19 - Understand and design CNNs/015 CodeChallenge Varying number of channels.mp4 96.9 MB
10 - Metaparameters (activations, optimizers)/010 Activation functions in PyTorch.mp4 95.9 MB
30 - Python intro Flow control/002 If-else statements, part 2.mp4 95.5 MB
30 - Python intro Flow control/008 while loops.mp4 95.5 MB
30 - Python intro Flow control/006 Initializing variables.mp4 95.5 MB
21 - Transfer learning/002 Transfer learning MNIST - FMNIST.mp4 94.7 MB
10 - Metaparameters (activations, optimizers)/014 Loss functions.mp4 94.7 MB
23 - Generative adversarial networks/001 GAN What, why, and how.mp4 94.1 MB
07 - ANNs (Artificial Neural Networks)/017 Defining models using sequential vs. class.mp4 93.8 MB
09 - Regularization/010 Batch training in action.mp4 93.4 MB
18 - Convolution and transformations/008 Maxmean pooling.mp4 93.4 MB
17 - Running models on a GPU/001 What is a GPU and why use it.mp4 93.0 MB
29 - Python intro Functions/005 Creating functions.mp4 92.7 MB
05 - Math, numpy, PyTorch/012 Minmax and argminargmax.mp4 92.5 MB
08 - Overfitting and cross-validation/002 Cross-validation.mp4 92.5 MB
15 - Weight inits and investigations/007 CodeChallenge Identically random weights.mp4 92.4 MB
30 - Python intro Flow control/003 For loops.mp4 91.4 MB
10 - Metaparameters (activations, optimizers)/020 Optimizers comparison.mp4 91.1 MB
31 - Python intro Text and plots/003 Subplot geometry.mp4 91.0 MB
07 - ANNs (Artificial Neural Networks)/001 The perceptron and ANN architecture.mp4 90.0 MB
05 - Math, numpy, PyTorch/008 Matrix multiplication.mp4 89.8 MB
05 - Math, numpy, PyTorch/014 Random sampling and sampling variability.mp4 89.6 MB
09 - Regularization/006 Weight regularization (L1L2) math.mp4 89.6 MB
19 - Understand and design CNNs/013 Dropout in CNNs.mp4 86.7 MB
13 - Measuring model performance/007 Computation time.mp4 85.7 MB
05 - Math, numpy, PyTorch/013 Mean and variance.mp4 85.4 MB
05 - Math, numpy, PyTorch/016 The t-test.mp4 85.3 MB
29 - Python intro Functions/003 Python libraries (pandas).mp4 85.1 MB
18 - Convolution and transformations/009 Pooling in PyTorch.mp4 85.0 MB
05 - Math, numpy, PyTorch/017 Derivatives intuition and polynomials.mp4 84.2 MB
09 - Regularization/001 Regularization Concept and methods.mp4 83.9 MB
15 - Weight inits and investigations/003 Theory Why and how to initialize weights.mp4 83.3 MB
08 - Overfitting and cross-validation/007 Splitting data into train, devset, test.mp4 83.1 MB
27 - Python intro Data types/003 Math and printing.mp4 82.3 MB
11 - FFNs (Feed-Forward Networks)/010 Shifted MNIST.mp4 81.7 MB
27 - Python intro Data types/002 Variables.mp4 81.3 MB
06 - Gradient descent/004 CodeChallenge unfortunate starting value.mp4 80.8 MB
27 - Python intro Data types/007 Booleans.mp4 80.6 MB
10 - Metaparameters (activations, optimizers)/006 Batch normalization.mp4 80.5 MB
10 - Metaparameters (activations, optimizers)/019 Optimizers (RMSprop, Adam).mp4 80.5 MB
17 - Running models on a GPU/002 Implementation.mp4 80.3 MB
20 - CNN milestone projects/005 Project 4 Psychometric functions in CNNs.mp4 80.0 MB
14 - FFN milestone projects/006 Project 3 My solution.mp4 79.1 MB
25 - Ethics of deep learning/004 Will deep learning take our jobs.mp4 78.8 MB
30 - Python intro Flow control/007 Single-line loops (list comprehension).mp4 78.8 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/002 How RNNs work.mp4 78.5 MB
03 - Concepts in deep learning/004 Running experiments to understand DL.mp4 78.5 MB
11 - FFNs (Feed-Forward Networks)/011 CodeChallenge The mystery of the missing 7.mp4 77.9 MB
10 - Metaparameters (activations, optimizers)/011 Activation functions comparison.mp4 77.5 MB
08 - Overfitting and cross-validation/001 What is overfitting and is it as bad as they say.mp4 76.7 MB
07 - ANNs (Artificial Neural Networks)/003 ANN math part 1 (forward prop).mp4 76.7 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/001 Leveraging sequences in deep learning.mp4 76.3 MB
03 - Concepts in deep learning/002 How models learn.mp4 76.3 MB
13 - Measuring model performance/002 Accuracy, precision, recall, F1.mp4 76.1 MB
07 - ANNs (Artificial Neural Networks)/015 Comparing the number of hidden units.mp4 74.6 MB
30 - Python intro Flow control/009 Broadcasting in numpy.mp4 74.5 MB
07 - ANNs (Artificial Neural Networks)/002 A geometric view of ANNs.mp4 74.3 MB
18 - Convolution and transformations/002 Feature maps and convolution kernels.mp4 73.8 MB
25 - Ethics of deep learning/005 Accountability and making ethical AI.mp4 73.5 MB
05 - Math, numpy, PyTorch/015 Reproducible randomness via seeding.mp4 73.1 MB
15 - Weight inits and investigations/001 Explanation of weight matrix sizes.mp4 72.3 MB
06 - Gradient descent/001 Overview of gradient descent.mp4 71.8 MB
22 - Style transfer/003 The style transfer algorithm.mp4 70.6 MB
06 - Gradient descent/002 What about local minima.mp4 70.3 MB
18 - Convolution and transformations/004 Convolution parameters (stride, padding).mp4 70.2 MB
30 - Python intro Flow control/001 If-else statements.mp4 70.0 MB
22 - Style transfer/002 The Gram matrix (feature activation covariance).mp4 69.7 MB
25 - Ethics of deep learning/003 Some other possible ethical scenarios.mp4 69.5 MB
29 - Python intro Functions/006 Global and local variable scopes.mp4 69.2 MB
25 - Ethics of deep learning/001 Will AI save us or destroy us.mp4 69.1 MB
03 - Concepts in deep learning/001 What is an artificial neural network.mp4 68.6 MB
10 - Metaparameters (activations, optimizers)/005 The importance of data normalization.mp4 67.8 MB
10 - Metaparameters (activations, optimizers)/012 CodeChallenge Compare relu variants.mp4 67.1 MB
29 - Python intro Functions/002 Python libraries (numpy).mp4 66.5 MB
23 - Generative adversarial networks/003 CodeChallenge Linear GAN with FMNIST.mp4 65.8 MB
09 - Regularization/009 Training in mini-batches.mp4 65.1 MB
10 - Metaparameters (activations, optimizers)/018 SGD with momentum.mp4 65.1 MB
10 - Metaparameters (activations, optimizers)/007 Batch normalization in practice.mp4 64.8 MB
10 - Metaparameters (activations, optimizers)/024 How to pick the right metaparameters.mp4 64.7 MB
23 - Generative adversarial networks/007 CodeChallenge CNN GAN with CIFAR.mp4 63.7 MB
08 - Overfitting and cross-validation/008 Cross-validation on regression.mp4 63.3 MB
11 - FFNs (Feed-Forward Networks)/009 Scrambled MNIST.mp4 63.1 MB
09 - Regularization/011 The importance of equal batch sizes.mp4 63.0 MB
10 - Metaparameters (activations, optimizers)/004 Data normalization.mp4 62.7 MB
31 - Python intro Text and plots/005 Seaborn.mp4 62.6 MB
18 - Convolution and transformations/006 CodeChallenge Choose the parameters.mp4 61.6 MB
30 - Python intro Flow control/004 Enumerate and zip.mp4 61.4 MB
07 - ANNs (Artificial Neural Networks)/021 Reflection Are DL models understandable yet.mp4 61.4 MB
19 - Understand and design CNNs/003 CNN on shifted MNIST.mp4 61.2 MB
19 - Understand and design CNNs/001 The canonical CNN architecture.mp4 58.5 MB
12 - More on data/009 Save and load trained models.mp4 58.4 MB
05 - Math, numpy, PyTorch/019 Derivatives product and chain rules.mp4 58.3 MB
18 - Convolution and transformations/010 To pool or to stride.mp4 58.2 MB
19 - Understand and design CNNs/014 CodeChallenge How low can you go.mp4 58.0 MB
27 - Python intro Data types/004 Lists (1 of 2).mp4 57.7 MB
01 - Introduction/001 How to learn from this course.mp4 57.6 MB
23 - Generative adversarial networks/006 CNN GAN with FMNIST.mp4 57.2 MB
01 - Introduction/002 Using Udemy like a pro.mp4 57.0 MB
12 - More on data/004 What to do about unbalanced designs.mp4 56.8 MB
09 - Regularization/005 Dropout example 2.mp4 56.5 MB
31 - Python intro Text and plots/002 Plotting dots and lines.mp4 56.5 MB
22 - Style transfer/005 CodeChallenge Style transfer with AlexNet.mp4 56.1 MB
23 - Generative adversarial networks/005 CodeChallenge Gaussians with fewer layers.mp4 55.6 MB
10 - Metaparameters (activations, optimizers)/022 CodeChallenge Adam with L2 regularization.mp4 55.6 MB
17 - Running models on a GPU/003 CodeChallenge Run an experiment on the GPU.mp4 55.6 MB
25 - Ethics of deep learning/002 Example case studies.mp4 55.5 MB
07 - ANNs (Artificial Neural Networks)/005 ANN math part 3 (backprop).mp4 55.5 MB
13 - Measuring model performance/003 APRF in code.mp4 54.3 MB
07 - ANNs (Artificial Neural Networks)/019 CodeChallenge convert sequential to class.mp4 53.9 MB
28 - Python intro Indexing, slicing/001 Indexing.mp4 53.6 MB
05 - Math, numpy, PyTorch/003 Spectral theories in mathematics.mp4 53.5 MB
27 - Python intro Data types/008 Dictionaries.mp4 53.1 MB
14 - FFN milestone projects/003 Project 2 Predicting heart disease.mp4 53.1 MB
07 - ANNs (Artificial Neural Networks)/011 Linear solutions to linear problems.mp4 52.8 MB
05 - Math, numpy, PyTorch/007 OMG it's the dot product!.mp4 52.5 MB
10 - Metaparameters (activations, optimizers)/021 CodeChallenge Optimizers and... something.mp4 52.2 MB
11 - FFNs (Feed-Forward Networks)/012 Universal approximation theorem.mp4 51.6 MB
16 - Autoencoders/001 What are autoencoders and what do they do.mp4 51.4 MB
29 - Python intro Functions/004 Getting help on functions.mp4 51.0 MB
14 - FFN milestone projects/001 Project 1 A gratuitously complex adding machine.mp4 50.9 MB
07 - ANNs (Artificial Neural Networks)/004 ANN math part 2 (errors, loss, cost).mp4 50.8 MB
28 - Python intro Indexing, slicing/002 Slicing.mp4 50.8 MB
20 - CNN milestone projects/001 Project 1 Import and classify CIFAR10.mp4 50.7 MB
27 - Python intro Data types/005 Lists (2 of 2).mp4 49.0 MB
11 - FFNs (Feed-Forward Networks)/008 CodeChallenge Optimizers and MNIST.mp4 48.5 MB
02 - Download all course materials/001 Downloading and using the code.mp4 47.9 MB
05 - Math, numpy, PyTorch/018 Derivatives find minima.mp4 47.7 MB
14 - FFN milestone projects/005 Project 3 FFN for missing data interpolation.mp4 47.6 MB
13 - Measuring model performance/008 Better performance in test than train.mp4 47.0 MB
05 - Math, numpy, PyTorch/010 Logarithms.mp4 46.0 MB
12 - More on data/008 Getting data into colab.mp4 45.9 MB
26 - Where to go from here/001 How to learn topic _X_ in deep learning.mp4 44.1 MB
12 - More on data/011 Where to find online datasets.mp4 43.7 MB
10 - Metaparameters (activations, optimizers)/008 CodeChallenge Batch-normalize the qwerties.mp4 43.4 MB
21 - Transfer learning/004 Famous CNN architectures.mp4 43.3 MB
11 - FFNs (Feed-Forward Networks)/004 CodeChallenge Binarized MNIST images.mp4 42.8 MB
22 - Style transfer/001 What is style transfer and how does it work.mp4 42.5 MB
13 - Measuring model performance/001 Two perspectives of the world.mp4 42.0 MB
06 - Gradient descent/006 CodeChallenge 2D gradient ascent.mp4 41.3 MB
09 - Regularization/002 train() and eval() modes.mp4 40.2 MB
05 - Math, numpy, PyTorch/004 Terms and datatypes in math and computers.mp4 39.9 MB
05 - Math, numpy, PyTorch/006 Vector and matrix transpose.mp4 39.5 MB
27 - Python intro Data types/006 Tuples.mp4 37.5 MB
03 - Concepts in deep learning/003 The role of DL in science and knowledge.mp4 36.4 MB
20 - CNN milestone projects/003 Project 2 CIFAR-autoencoder.mp4 35.0 MB
05 - Math, numpy, PyTorch/005 Converting reality to numbers.mp4 34.8 MB
30 - Python intro Flow control/005 Continue.mp4 34.6 MB
10 - Metaparameters (activations, optimizers)/001 What are metaparameters.mp4 34.3 MB
08 - Overfitting and cross-validation/003 Generalization.mp4 34.0 MB
06 - Gradient descent/009 Vanishing and exploding gradients.mp4 31.7 MB
29 - Python intro Functions/001 Inputs and outputs.mp4 30.9 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/006 More on RNNs Hidden states, embeddings.mp4 30.4 MB
19 - Understand and design CNNs/009 CodeChallenge AEs and occluded Gaussians.mp4 30.0 MB
15 - Weight inits and investigations/010 Use default inits or apply your own.mp4 29.4 MB
07 - ANNs (Artificial Neural Networks)/012 Why multilayer linear models don't exist.mp4 27.7 MB
20 - CNN milestone projects/004 Project 3 FMNIST.mp4 27.7 MB
11 - FFNs (Feed-Forward Networks)/001 What are fully-connected and feedforward networks.mp4 26.8 MB
13 - Measuring model performance/006 CodeChallenge MNIST with unequal groups.mp4 26.3 MB
29 - Python intro Functions/007 Copies and referents of variables.mp4 24.9 MB
04 - About the Python tutorial/001 Should you watch the Python tutorial.mp4 24.9 MB
27 - Python intro Data types/001 How to learn from the Python tutorial.mp4 23.0 MB
19 - Understand and design CNNs/016 So many possibilities! How to create a CNN.mp4 22.1 MB
21 - Transfer learning/006 CodeChallenge VGG-16.mp4 21.3 MB
31 - Python intro Text and plots/007 Export plots in low and high resolution.mp4 18.0 MB
05 - Math, numpy, PyTorch/002 Introduction to this section.mp4 11.7 MB
02 - Download all course materials/002 My policy on code-sharing.mp4 10.7 MB
06 - Gradient descent/010 Tangent Notebook revision history.mp4 10.4 MB
02 - Download all course materials/001 DUDL-PythonCode.zip 676.7 kB
19 - Understand and design CNNs/005 Examine feature map activations_en.srt 39.9 kB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/005 CodeChallenge sine wave extrapolation_en.srt 38.5 kB
19 - Understand and design CNNs/002 CNN to classify MNIST digits_en.srt 37.5 kB
07 - ANNs (Artificial Neural Networks)/013 Multi-output ANN (iris dataset)_en.srt 37.0 kB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/009 Lorem ipsum_en.srt 36.9 kB
07 - ANNs (Artificial Neural Networks)/009 Learning rates comparison_en.srt 35.7 kB
19 - Understand and design CNNs/012 The EMNIST dataset (letter recognition)_en.srt 35.6 kB
07 - ANNs (Artificial Neural Networks)/006 ANN for regression_en.srt 35.3 kB
16 - Autoencoders/006 Autoencoder with tied weights_en.srt 34.3 kB
19 - Understand and design CNNs/004 Classify Gaussian blurs_en.srt 33.8 kB
07 - ANNs (Artificial Neural Networks)/008 ANN for classifying qwerties_en.srt 33.5 kB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/007 GRU and LSTM_en.srt 32.9 kB
09 - Regularization/004 Dropout regularization in practice_en.srt 32.9 kB
11 - FFNs (Feed-Forward Networks)/003 FFN to classify digits_en.srt 32.4 kB
15 - Weight inits and investigations/009 Learning-related changes in weights_en.srt 32.3 kB
18 - Convolution and transformations/001 Convolution concepts_en.srt 31.9 kB
22 - Style transfer/004 Transferring the screaming bathtub_en.srt 31.8 kB
23 - Generative adversarial networks/002 Linear GAN with MNIST_en.srt 31.5 kB
16 - Autoencoders/005 The latent code of MNIST_en.srt 31.2 kB
09 - Regularization/003 Dropout regularization_en.srt 31.1 kB
07 - ANNs (Artificial Neural Networks)/018 Model depth vs. breadth_en.srt 30.4 kB
29 - Python intro Functions/005 Creating functions_en.srt 30.4 kB
18 - Convolution and transformations/003 Convolution in code_en.srt 30.1 kB
08 - Overfitting and cross-validation/005 Cross-validation -- scikitlearn_en.srt 30.0 kB
19 - Understand and design CNNs/010 CodeChallenge Custom loss functions_en.srt 29.5 kB
07 - ANNs (Artificial Neural Networks)/010 Multilayer ANN_en.srt 29.0 kB
12 - More on data/003 CodeChallenge unbalanced data_en.srt 28.9 kB
16 - Autoencoders/003 CodeChallenge How many units_en.srt 28.5 kB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/004 Predicting alternating sequences_en.srt 28.4 kB
21 - Transfer learning/007 Pretraining with autoencoders_en.srt 28.4 kB
08 - Overfitting and cross-validation/006 Cross-validation -- DataLoader_en.srt 28.2 kB
12 - More on data/007 Data feature augmentation_en.srt 28.0 kB
07 - ANNs (Artificial Neural Networks)/007 CodeChallenge manipulate regression slopes_en.srt 27.9 kB
07 - ANNs (Artificial Neural Networks)/001 The perceptron and ANN architecture_en.srt 27.5 kB
30 - Python intro Flow control/008 while loops_en.srt 27.5 kB
05 - Math, numpy, PyTorch/009 Softmax_en.srt 27.4 kB
14 - FFN milestone projects/004 Project 2 My solution_en.srt 27.3 kB
27 - Python intro Data types/007 Booleans_en.srt 27.3 kB
10 - Metaparameters (activations, optimizers)/017 Optimizers (minibatch, momentum)_en.srt 27.0 kB
27 - Python intro Data types/002 Variables_en.srt 26.8 kB
06 - Gradient descent/007 Parametric experiments on g.d_en.srt 26.8 kB
09 - Regularization/006 Weight regularization (L1L2) math_en.srt 26.7 kB
31 - Python intro Text and plots/004 Making the graphs look nicer_en.srt 26.6 kB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/003 The RNN class in PyTorch_en.srt 26.6 kB
10 - Metaparameters (activations, optimizers)/015 Loss functions in PyTorch_en.srt 26.5 kB
27 - Python intro Data types/003 Math and printing_en.srt 26.4 kB
18 - Convolution and transformations/008 Maxmean pooling_en.srt 26.3 kB
29 - Python intro Functions/008 Classes and object-oriented programming_en.srt 26.2 kB
10 - Metaparameters (activations, optimizers)/009 Activation functions_en.srt 26.1 kB
18 - Convolution and transformations/012 Creating and using custom DataLoaders_en.srt 26.1 kB
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30 - Python intro Flow control/005 Continue_en.srt 9.9 kB
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