[FreeCourseSite.com] Udemy - A deep understanding of deep learning (with Python intro)

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19 - Understand and design CNNs/005 Examine feature map activations.mp4 251.4 MB
22 - Style transfer/004 Transferring the screaming bathtub.mp4 210.4 MB
19 - Understand and design CNNs/004 Classify Gaussian blurs.mp4 176.0 MB
07 - ANNs (Artificial Neural Networks)/009 Learning rates comparison.mp4 168.6 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/005 CodeChallenge sine wave extrapolation.mp4 166.6 MB
18 - Convolution and transformations/003 Convolution in code.mp4 165.7 MB
14 - FFN milestone projects/004 Project 2 My solution.mp4 155.7 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/004 Predicting alternating sequences.mp4 153.8 MB
19 - Understand and design CNNs/002 CNN to classify MNIST digits.mp4 144.8 MB
19 - Understand and design CNNs/012 The EMNIST dataset (letter recognition).mp4 143.9 MB
07 - ANNs (Artificial Neural Networks)/013 Multi-output ANN (iris dataset).mp4 142.0 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/009 Lorem ipsum.mp4 141.6 MB
16 - Autoencoders/004 AEs for occlusion.mp4 138.2 MB
26 - Where to go from here/002 How to read academic DL papers.mp4 137.3 MB
19 - Understand and design CNNs/011 Discover the Gaussian parameters.mp4 136.7 MB
21 - Transfer learning/007 Pretraining with autoencoders.mp4 136.0 MB
16 - Autoencoders/006 Autoencoder with tied weights.mp4 131.5 MB
23 - Generative adversarial networks/004 CNN GAN with Gaussians.mp4 131.4 MB
09 - Regularization/004 Dropout regularization in practice.mp4 130.7 MB
07 - ANNs (Artificial Neural Networks)/008 ANN for classifying qwerties.mp4 130.4 MB
19 - Understand and design CNNs/008 Do autoencoders clean Gaussians.mp4 128.8 MB
21 - Transfer learning/005 Transfer learning with ResNet-18.mp4 128.3 MB
18 - Convolution and transformations/011 Image transforms.mp4 124.7 MB
10 - Metaparameters (activations, optimizers)/002 The wine quality dataset.mp4 124.6 MB
23 - Generative adversarial networks/002 Linear GAN with MNIST.mp4 121.5 MB
08 - Overfitting and cross-validation/006 Cross-validation -- DataLoader.mp4 121.3 MB
12 - More on data/003 CodeChallenge unbalanced data.mp4 117.8 MB
16 - Autoencoders/005 The latent code of MNIST.mp4 117.8 MB
11 - FFNs (Feed-Forward Networks)/003 FFN to classify digits.mp4 117.3 MB
07 - ANNs (Artificial Neural Networks)/018 Model depth vs. breadth.mp4 115.0 MB
12 - More on data/007 Data feature augmentation.mp4 114.3 MB
19 - Understand and design CNNs/006 CodeChallenge Softcode internal parameters.mp4 113.7 MB
15 - Weight inits and investigations/006 CodeChallenge Xavier vs. Kaiming.mp4 109.4 MB
21 - Transfer learning/008 CIFAR10 with autoencoder-pretrained model.mp4 108.9 MB
15 - Weight inits and investigations/009 Learning-related changes in weights.mp4 108.0 MB
08 - Overfitting and cross-validation/005 Cross-validation -- scikitlearn.mp4 105.8 MB
07 - ANNs (Artificial Neural Networks)/010 Multilayer ANN.mp4 105.3 MB
09 - Regularization/003 Dropout regularization.mp4 103.7 MB
10 - Metaparameters (activations, optimizers)/003 CodeChallenge Minibatch size in the wine dataset.mp4 103.5 MB
13 - Measuring model performance/004 APRF example 1 wine quality.mp4 103.0 MB
18 - Convolution and transformations/012 Creating and using custom DataLoaders.mp4 102.4 MB
10 - Metaparameters (activations, optimizers)/015 Loss functions in PyTorch.mp4 101.7 MB
07 - ANNs (Artificial Neural Networks)/007 CodeChallenge manipulate regression slopes.mp4 101.1 MB
12 - More on data/001 Anatomy of a torch dataset and dataloader.mp4 100.8 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/007 GRU and LSTM.mp4 100.3 MB
16 - Autoencoders/003 CodeChallenge How many units.mp4 100.0 MB
06 - Gradient descent/007 Parametric experiments on g.d.mp4 98.7 MB
19 - Understand and design CNNs/010 CodeChallenge Custom loss functions.mp4 98.7 MB
07 - ANNs (Artificial Neural Networks)/016 Depth vs. breadth number of parameters.mp4 97.7 MB
12 - More on data/002 Data size and network size.mp4 97.2 MB
06 - Gradient descent/005 Gradient descent in 2D.mp4 96.4 MB
15 - Weight inits and investigations/005 Xavier and Kaiming initializations.mp4 96.3 MB
13 - Measuring model performance/005 APRF example 2 MNIST.mp4 94.5 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/006 More on RNNs Hidden states, embeddings.mp4 94.2 MB
19 - Understand and design CNNs/007 CodeChallenge How wide the FC.mp4 90.6 MB
11 - FFNs (Feed-Forward Networks)/007 CodeChallenge MNIST and breadth vs. depth.mp4 90.4 MB
12 - More on data/010 Save the best-performing model.mp4 90.1 MB
11 - FFNs (Feed-Forward Networks)/006 Distributions of weights pre- and post-learning.mp4 89.7 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/003 The RNN class in PyTorch.mp4 89.6 MB
10 - Metaparameters (activations, optimizers)/013 CodeChallenge Predict sugar.mp4 89.4 MB
12 - More on data/005 Data oversampling in MNIST.mp4 89.3 MB
11 - FFNs (Feed-Forward Networks)/002 The MNIST dataset.mp4 88.7 MB
18 - Convolution and transformations/001 Convolution concepts.mp4 88.4 MB
15 - Weight inits and investigations/008 Freezing weights during learning.mp4 88.3 MB
06 - Gradient descent/003 Gradient descent in 1D.mp4 87.8 MB
03 - Concepts in deep learning/003 The role of DL in science and knowledge.mp4 87.7 MB
16 - Autoencoders/002 Denoising MNIST.mp4 86.5 MB
15 - Weight inits and investigations/002 A surprising demo of weight initializations.mp4 85.9 MB
10 - Metaparameters (activations, optimizers)/009 Activation functions.mp4 84.9 MB
21 - Transfer learning/003 CodeChallenge letters to numbers.mp4 84.9 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/008 The LSTM and GRU classes.mp4 84.3 MB
06 - Gradient descent/008 CodeChallenge fixed vs. dynamic learning rate.mp4 84.0 MB
09 - Regularization/012 CodeChallenge Effects of mini-batch size.mp4 83.3 MB
07 - ANNs (Artificial Neural Networks)/014 CodeChallenge more qwerties!.mp4 81.9 MB
20 - CNN milestone projects/002 Project 1 My solution.mp4 81.3 MB
19 - Understand and design CNNs/009 CodeChallenge AEs and occluded Gaussians.mp4 78.6 MB
09 - Regularization/007 L2 regularization in practice.mp4 78.5 MB
21 - Transfer learning/002 Transfer learning MNIST - FMNIST.mp4 78.2 MB
30 - Python intro Flow control/010 Function error checking and handling.mp4 77.0 MB
20 - CNN milestone projects/005 Project 4 Psychometric functions in CNNs.mp4 76.5 MB
09 - Regularization/010 Batch training in action.mp4 76.4 MB
12 - More on data/006 Data noise augmentation (with devset+test).mp4 76.1 MB
18 - Convolution and transformations/005 The Conv2 class in PyTorch.mp4 75.5 MB
03 - Concepts in deep learning/004 Running experiments to understand DL.mp4 74.8 MB
07 - ANNs (Artificial Neural Networks)/006 ANN for regression.mp4 74.2 MB
15 - Weight inits and investigations/003 Theory Why and how to initialize weights.mp4 73.6 MB
15 - Weight inits and investigations/004 CodeChallenge Weight variance inits.mp4 72.9 MB
10 - Metaparameters (activations, optimizers)/016 More practice with multioutput ANNs.mp4 71.9 MB
31 - Python intro Text and plots/006 Images.mp4 71.0 MB
11 - FFNs (Feed-Forward Networks)/005 CodeChallenge Data normalization.mp4 71.0 MB
09 - Regularization/008 L1 regularization in practice.mp4 70.9 MB
19 - Understand and design CNNs/013 Dropout in CNNs.mp4 70.6 MB
10 - Metaparameters (activations, optimizers)/011 Activation functions comparison.mp4 70.6 MB
13 - Measuring model performance/007 Computation time.mp4 70.5 MB
08 - Overfitting and cross-validation/004 Cross-validation -- manual separation.mp4 70.4 MB
05 - Math, numpy, PyTorch/009 Softmax.mp4 70.2 MB
14 - FFN milestone projects/002 Project 1 My solution.mp4 69.8 MB
18 - Convolution and transformations/007 Transpose convolution.mp4 69.4 MB
10 - Metaparameters (activations, optimizers)/023 Learning rate decay.mp4 69.1 MB
10 - Metaparameters (activations, optimizers)/014 Loss functions.mp4 68.6 MB
19 - Understand and design CNNs/015 CodeChallenge Varying number of channels.mp4 67.3 MB
10 - Metaparameters (activations, optimizers)/010 Activation functions in PyTorch.mp4 67.0 MB
22 - Style transfer/002 The Gram matrix (feature activation covariance).mp4 66.5 MB
07 - ANNs (Artificial Neural Networks)/017 Defining models using sequential vs. class.mp4 65.8 MB
15 - Weight inits and investigations/007 CodeChallenge Identically random weights.mp4 65.3 MB
10 - Metaparameters (activations, optimizers)/012 CodeChallenge Compare relu variants.mp4 64.0 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/001 Leveraging sequences in deep learning.mp4 63.9 MB
13 - Measuring model performance/002 Accuracy, precision, recall, F1.mp4 63.7 MB
10 - Metaparameters (activations, optimizers)/018 SGD with momentum.mp4 62.1 MB
10 - Metaparameters (activations, optimizers)/020 Optimizers comparison.mp4 61.8 MB
09 - Regularization/001 Regularization Concept and methods.mp4 61.5 MB
25 - Ethics of deep learning/005 Accountability and making ethical AI.mp4 61.2 MB
29 - Python intro Functions/003 Python libraries (pandas).mp4 60.9 MB
29 - Python intro Functions/008 Classes and object-oriented programming.mp4 60.6 MB
11 - FFNs (Feed-Forward Networks)/009 Scrambled MNIST.mp4 60.2 MB
05 - Math, numpy, PyTorch/016 The t-test.mp4 59.7 MB
15 - Weight inits and investigations/001 Explanation of weight matrix sizes.mp4 59.6 MB
13 - Measuring model performance/006 CodeChallenge MNIST with unequal groups.mp4 59.0 MB
31 - Python intro Text and plots/004 Making the graphs look nicer.mp4 59.0 MB
05 - Math, numpy, PyTorch/011 Entropy and cross-entropy.mp4 58.8 MB
30 - Python intro Flow control/004 Enumerate and zip.mp4 58.6 MB
23 - Generative adversarial networks/003 CodeChallenge Linear GAN with FMNIST.mp4 58.5 MB
25 - Ethics of deep learning/003 Some other possible ethical scenarios.mp4 58.3 MB
11 - FFNs (Feed-Forward Networks)/010 Shifted MNIST.mp4 57.3 MB
06 - Gradient descent/004 CodeChallenge unfortunate starting value.mp4 57.0 MB
03 - Concepts in deep learning/005 Are artificial neurons like biological neurons.mp4 56.3 MB
08 - Overfitting and cross-validation/007 Splitting data into train, devset, test.mp4 56.3 MB
01 - Introduction/001 How to learn from this course.mp4 55.0 MB
08 - Overfitting and cross-validation/001 What is overfitting and is it as bad as they say.mp4 54.3 MB
30 - Python intro Flow control/002 If-else statements, part 2.mp4 53.7 MB
18 - Convolution and transformations/002 Feature maps and convolution kernels.mp4 53.6 MB
11 - FFNs (Feed-Forward Networks)/011 CodeChallenge The mystery of the missing 7.mp4 53.4 MB
14 - FFN milestone projects/006 Project 3 My solution.mp4 52.9 MB
07 - ANNs (Artificial Neural Networks)/021 Reflection Are DL models understandable yet.mp4 51.7 MB
09 - Regularization/011 The importance of equal batch sizes.mp4 51.3 MB
23 - Generative adversarial networks/005 CodeChallenge Gaussians with fewer layers.mp4 51.3 MB
18 - Convolution and transformations/008 Maxmean pooling.mp4 51.2 MB
22 - Style transfer/005 CodeChallenge Style transfer with AlexNet.mp4 50.9 MB
17 - Running models on a GPU/001 What is a GPU and why use it.mp4 50.4 MB
09 - Regularization/006 Weight regularization (L1L2) math.mp4 49.3 MB
18 - Convolution and transformations/010 To pool or to stride.mp4 49.2 MB
05 - Math, numpy, PyTorch/015 Reproducible randomness via seeding.mp4 49.1 MB
08 - Overfitting and cross-validation/002 Cross-validation.mp4 49.1 MB
31 - Python intro Text and plots/003 Subplot geometry.mp4 48.7 MB
30 - Python intro Flow control/008 while loops.mp4 48.2 MB
10 - Metaparameters (activations, optimizers)/005 The importance of data normalization.mp4 47.8 MB
31 - Python intro Text and plots/001 Printing and string interpolation.mp4 47.2 MB
23 - Generative adversarial networks/006 CNN GAN with FMNIST.mp4 46.9 MB
30 - Python intro Flow control/006 Initializing variables.mp4 46.5 MB
27 - Python intro Data types/007 Booleans.mp4 46.0 MB
05 - Math, numpy, PyTorch/012 Minmax and argminargmax.mp4 45.7 MB
05 - Math, numpy, PyTorch/008 Matrix multiplication.mp4 45.5 MB
10 - Metaparameters (activations, optimizers)/004 Data normalization.mp4 45.4 MB
10 - Metaparameters (activations, optimizers)/007 Batch normalization in practice.mp4 45.2 MB
30 - Python intro Flow control/003 For loops.mp4 44.7 MB
18 - Convolution and transformations/009 Pooling in PyTorch.mp4 44.2 MB
30 - Python intro Flow control/007 Single-line loops (list comprehension).mp4 44.1 MB
05 - Math, numpy, PyTorch/003 Spectral theories in mathematics.mp4 43.9 MB
23 - Generative adversarial networks/007 CodeChallenge CNN GAN with CIFAR.mp4 43.2 MB
10 - Metaparameters (activations, optimizers)/017 Optimizers (minibatch, momentum).mp4 42.2 MB
19 - Understand and design CNNs/003 CNN on shifted MNIST.mp4 41.4 MB
05 - Math, numpy, PyTorch/014 Random sampling and sampling variability.mp4 41.3 MB
27 - Python intro Data types/002 Variables.mp4 41.1 MB
21 - Transfer learning/001 Transfer learning What, why, and when.mp4 40.5 MB
29 - Python intro Functions/005 Creating functions.mp4 40.1 MB
06 - Gradient descent/001 Overview of gradient descent.mp4 40.1 MB
10 - Metaparameters (activations, optimizers)/022 CodeChallenge Adam with L2 regularization.mp4 39.9 MB
10 - Metaparameters (activations, optimizers)/008 CodeChallenge Batch-normalize the qwerties.mp4 39.9 MB
17 - Running models on a GPU/002 Implementation.mp4 39.7 MB
29 - Python intro Functions/006 Global and local variable scopes.mp4 39.2 MB
19 - Understand and design CNNs/014 CodeChallenge How low can you go.mp4 39.2 MB
10 - Metaparameters (activations, optimizers)/006 Batch normalization.mp4 39.1 MB
12 - More on data/009 Save and load trained models.mp4 38.7 MB
23 - Generative adversarial networks/001 GAN What, why, and how.mp4 38.7 MB
25 - Ethics of deep learning/002 Example case studies.mp4 38.4 MB
13 - Measuring model performance/003 APRF in code.mp4 38.2 MB
09 - Regularization/005 Dropout example 2.mp4 38.1 MB
10 - Metaparameters (activations, optimizers)/019 Optimizers (RMSprop, Adam).mp4 38.0 MB
31 - Python intro Text and plots/007 Export plots in low and high resolution.mp4 37.4 MB
07 - ANNs (Artificial Neural Networks)/004 ANN math part 2 (errors, loss, cost).mp4 37.3 MB
07 - ANNs (Artificial Neural Networks)/001 The perceptron and ANN architecture.mp4 37.1 MB
30 - Python intro Flow control/009 Broadcasting in numpy.mp4 37.1 MB
17 - Running models on a GPU/003 CodeChallenge Run an experiment on the GPU.mp4 36.9 MB
07 - ANNs (Artificial Neural Networks)/011 Linear solutions to linear problems.mp4 36.8 MB
20 - CNN milestone projects/001 Project 1 Import and classify CIFAR10.mp4 36.6 MB
10 - Metaparameters (activations, optimizers)/021 CodeChallenge Optimizers and... something.mp4 36.6 MB
07 - ANNs (Artificial Neural Networks)/019 CodeChallenge convert sequential to class.mp4 36.5 MB
27 - Python intro Data types/003 Math and printing.mp4 35.9 MB
03 - Concepts in deep learning/002 How models learn.mp4 35.4 MB
31 - Python intro Text and plots/005 Seaborn.mp4 34.3 MB
25 - Ethics of deep learning/004 Will deep learning take our jobs.mp4 33.8 MB
02 - Download all course materials/001 Downloading and using the code.mp4 33.7 MB
11 - FFNs (Feed-Forward Networks)/008 CodeChallenge Optimizers and MNIST.mp4 33.2 MB
05 - Math, numpy, PyTorch/013 Mean and variance.mp4 32.9 MB
07 - ANNs (Artificial Neural Networks)/003 ANN math part 1 (forward prop).mp4 32.8 MB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/002 How RNNs work.mp4 32.6 MB
05 - Math, numpy, PyTorch/017 Derivatives intuition and polynomials.mp4 32.1 MB
12 - More on data/008 Getting data into colab.mp4 31.9 MB
07 - ANNs (Artificial Neural Networks)/015 Comparing the number of hidden units.mp4 31.8 MB
30 - Python intro Flow control/001 If-else statements.mp4 30.2 MB
07 - ANNs (Artificial Neural Networks)/002 A geometric view of ANNs.mp4 29.8 MB
03 - Concepts in deep learning/001 What is an artificial neural network.mp4 29.4 MB
20 - CNN milestone projects/003 Project 2 CIFAR-autoencoder.mp4 29.2 MB
28 - Python intro Indexing, slicing/002 Slicing.mp4 29.0 MB
31 - Python intro Text and plots/002 Plotting dots and lines.mp4 28.9 MB
11 - FFNs (Feed-Forward Networks)/004 CodeChallenge Binarized MNIST images.mp4 28.7 MB
12 - More on data/011 Where to find online datasets.mp4 28.5 MB
07 - ANNs (Artificial Neural Networks)/005 ANN math part 3 (backprop).mp4 28.0 MB
29 - Python intro Functions/002 Python libraries (numpy).mp4 28.0 MB
06 - Gradient descent/006 CodeChallenge 2D gradient ascent.mp4 27.8 MB
18 - Convolution and transformations/004 Convolution parameters (stride, padding).mp4 27.4 MB
22 - Style transfer/003 The style transfer algorithm.mp4 26.7 MB
08 - Overfitting and cross-validation/008 Cross-validation on regression.mp4 26.3 MB
14 - FFN milestone projects/001 Project 1 A gratuitously complex adding machine.mp4 25.9 MB
05 - Math, numpy, PyTorch/019 Derivatives product and chain rules.mp4 25.9 MB
01 - Introduction/002 Using Udemy like a pro.mp4 25.7 MB
06 - Gradient descent/002 What about local minima.mp4 25.6 MB
10 - Metaparameters (activations, optimizers)/024 How to pick the right metaparameters.mp4 25.5 MB
27 - Python intro Data types/004 Lists (1 of 2).mp4 24.9 MB
29 - Python intro Functions/004 Getting help on functions.mp4 24.8 MB
11 - FFNs (Feed-Forward Networks)/012 Universal approximation theorem.mp4 24.2 MB
09 - Regularization/009 Training in mini-batches.mp4 24.1 MB
25 - Ethics of deep learning/001 Will AI save us or destroy us.mp4 23.8 MB
19 - Understand and design CNNs/001 The canonical CNN architecture.mp4 23.8 MB
14 - FFN milestone projects/003 Project 2 Predicting heart disease.mp4 23.7 MB
27 - Python intro Data types/005 Lists (2 of 2).mp4 23.6 MB
28 - Python intro Indexing, slicing/001 Indexing.mp4 23.4 MB
27 - Python intro Data types/008 Dictionaries.mp4 23.2 MB
06 - Gradient descent/009 Vanishing and exploding gradients.mp4 22.3 MB
21 - Transfer learning/004 Famous CNN architectures.mp4 22.3 MB
16 - Autoencoders/001 What are autoencoders and what do they do.mp4 21.2 MB
05 - Math, numpy, PyTorch/010 Logarithms.mp4 20.8 MB
21 - Transfer learning/006 CodeChallenge VGG-16.mp4 20.3 MB
05 - Math, numpy, PyTorch/007 OMG it's the dot product!.mp4 19.8 MB
14 - FFN milestone projects/005 Project 3 FFN for missing data interpolation.mp4 19.6 MB
20 - CNN milestone projects/004 Project 3 FMNIST.mp4 19.4 MB
07 - ANNs (Artificial Neural Networks)/012 Why multilayer linear models don't exist.mp4 19.3 MB
18 - Convolution and transformations/006 CodeChallenge Choose the parameters.mp4 19.0 MB
13 - Measuring model performance/001 Two perspectives of the world.mp4 18.9 MB
12 - More on data/004 What to do about unbalanced designs.mp4 18.8 MB
05 - Math, numpy, PyTorch/018 Derivatives find minima.mp4 18.7 MB
13 - Measuring model performance/008 Better performance in test than train.mp4 18.2 MB
05 - Math, numpy, PyTorch/006 Vector and matrix transpose.mp4 17.8 MB
26 - Where to go from here/001 How to learn topic _X_ in deep learning.mp4 17.5 MB
22 - Style transfer/001 What is style transfer and how does it work.mp4 16.8 MB
05 - Math, numpy, PyTorch/004 Terms and datatypes in math and computers.mp4 15.8 MB
09 - Regularization/002 train() and eval() modes.mp4 15.7 MB
27 - Python intro Data types/006 Tuples.mp4 15.4 MB
06 - Gradient descent/010 Tangent Notebook revision history.mp4 14.8 MB
30 - Python intro Flow control/005 Continue.mp4 14.3 MB
29 - Python intro Functions/001 Inputs and outputs.mp4 13.5 MB
05 - Math, numpy, PyTorch/005 Converting reality to numbers.mp4 13.4 MB
08 - Overfitting and cross-validation/003 Generalization.mp4 13.3 MB
11 - FFNs (Feed-Forward Networks)/001 What are fully-connected and feedforward networks.mp4 12.7 MB
10 - Metaparameters (activations, optimizers)/001 What are metaparameters.mp4 12.4 MB
27 - Python intro Data types/001 How to learn from the Python tutorial.mp4 12.3 MB
15 - Weight inits and investigations/010 Use default inits or apply your own.mp4 10.9 MB
29 - Python intro Functions/007 Copies and referents of variables.mp4 10.6 MB
04 - About the Python tutorial/001 Should you watch the Python tutorial.mp4 9.4 MB
19 - Understand and design CNNs/016 So many possibilities! How to create a CNN.mp4 9.2 MB
05 - Math, numpy, PyTorch/002 Introduction to this section.mp4 4.5 MB
02 - Download all course materials/002 My policy on code-sharing.mp4 3.9 MB
02 - Download all course materials/001 DUDL-PythonCode.zip 1.4 MB
19 - Understand and design CNNs/005 Examine feature map activations_en.srt 39.0 KB
07 - ANNs (Artificial Neural Networks)/013 Multi-output ANN (iris dataset)_en.srt 38.7 KB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/005 CodeChallenge sine wave extrapolation_en.srt 38.0 KB
19 - Understand and design CNNs/002 CNN to classify MNIST digits_en.srt 36.6 KB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/009 Lorem ipsum_en.srt 36.0 KB
07 - ANNs (Artificial Neural Networks)/009 Learning rates comparison_en.srt 34.9 KB
19 - Understand and design CNNs/012 The EMNIST dataset (letter recognition)_en.srt 34.8 KB
07 - ANNs (Artificial Neural Networks)/006 ANN for regression_en.srt 34.5 KB
16 - Autoencoders/006 Autoencoder with tied weights_en.srt 33.5 KB
07 - ANNs (Artificial Neural Networks)/008 ANN for classifying qwerties_en.srt 33.3 KB
19 - Understand and design CNNs/004 Classify Gaussian blurs_en.srt 33.0 KB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/007 GRU and LSTM_en.srt 32.1 KB
09 - Regularization/004 Dropout regularization in practice_en.srt 32.1 KB
11 - FFNs (Feed-Forward Networks)/003 FFN to classify digits_en.srt 31.7 KB
15 - Weight inits and investigations/009 Learning-related changes in weights_en.srt 31.6 KB
18 - Convolution and transformations/001 Convolution concepts_en.srt 31.2 KB
22 - Style transfer/004 Transferring the screaming bathtub_en.srt 31.1 KB
09 - Regularization/003 Dropout regularization_en.srt 30.4 KB
16 - Autoencoders/005 The latent code of MNIST_en.srt 30.2 KB
07 - ANNs (Artificial Neural Networks)/018 Model depth vs. breadth_en.srt 29.7 KB
29 - Python intro Functions/005 Creating functions_en.srt 29.7 KB
18 - Convolution and transformations/003 Convolution in code_en.srt 29.4 KB
08 - Overfitting and cross-validation/005 Cross-validation -- scikitlearn_en.srt 29.3 KB
19 - Understand and design CNNs/010 CodeChallenge Custom loss functions_en.srt 28.8 KB
07 - ANNs (Artificial Neural Networks)/010 Multilayer ANN_en.srt 28.3 KB
12 - More on data/003 CodeChallenge unbalanced data_en.srt 28.2 KB
08 - Overfitting and cross-validation/006 Cross-validation -- DataLoader_en.srt 27.9 KB
16 - Autoencoders/003 CodeChallenge How many units_en.srt 27.8 KB
27 - Python intro Data types/007 Booleans_en.srt 27.8 KB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/004 Predicting alternating sequences_en.srt 27.8 KB
21 - Transfer learning/007 Pretraining with autoencoders_en.srt 27.7 KB
12 - More on data/007 Data feature augmentation_en.srt 27.6 KB
07 - ANNs (Artificial Neural Networks)/007 CodeChallenge manipulate regression slopes_en.srt 27.3 KB
07 - ANNs (Artificial Neural Networks)/001 The perceptron and ANN architecture_en.srt 27.0 KB
30 - Python intro Flow control/008 while loops_en.srt 26.9 KB
05 - Math, numpy, PyTorch/009 Softmax_en.srt 26.7 KB
14 - FFN milestone projects/004 Project 2 My solution_en.srt 26.7 KB
10 - Metaparameters (activations, optimizers)/017 Optimizers (minibatch, momentum)_en.srt 26.4 KB
27 - Python intro Data types/002 Variables_en.srt 26.2 KB
06 - Gradient descent/007 Parametric experiments on g.d_en.srt 26.2 KB
09 - Regularization/006 Weight regularization (L1L2) math_en.srt 26.1 KB
31 - Python intro Text and plots/004 Making the graphs look nicer_en.srt 26.0 KB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/003 The RNN class in PyTorch_en.srt 25.9 KB
10 - Metaparameters (activations, optimizers)/015 Loss functions in PyTorch_en.srt 25.8 KB
27 - Python intro Data types/003 Math and printing_en.srt 25.7 KB
18 - Convolution and transformations/008 Maxmean pooling_en.srt 25.7 KB
29 - Python intro Functions/008 Classes and object-oriented programming_en.srt 25.6 KB
10 - Metaparameters (activations, optimizers)/009 Activation functions_en.srt 25.5 KB
12 - More on data/001 Anatomy of a torch dataset and dataloader_en.srt 25.4 KB
18 - Convolution and transformations/012 Creating and using custom DataLoaders_en.srt 25.4 KB
16 - Autoencoders/004 AEs for occlusion_en.srt 24.9 KB
21 - Transfer learning/008 CIFAR10 with autoencoder-pretrained model_en.srt 24.9 KB
10 - Metaparameters (activations, optimizers)/002 The wine quality dataset_en.srt 24.8 KB
31 - Python intro Text and plots/006 Images_en.srt 24.8 KB
07 - ANNs (Artificial Neural Networks)/016 Depth vs. breadth number of parameters_en.srt 24.8 KB
30 - Python intro Flow control/006 Initializing variables_en.srt 24.6 KB
26 - Where to go from here/002 How to read academic DL papers_en.srt 24.5 KB
05 - Math, numpy, PyTorch/011 Entropy and cross-entropy_en.srt 24.5 KB
30 - Python intro Flow control/010 Function error checking and handling_en.srt 24.4 KB
30 - Python intro Flow control/003 For loops_en.srt 24.3 KB
19 - Understand and design CNNs/006 CodeChallenge Softcode internal parameters_en.srt 24.2 KB
10 - Metaparameters (activations, optimizers)/013 CodeChallenge Predict sugar_en.srt 24.1 KB
08 - Overfitting and cross-validation/002 Cross-validation_en.srt 24.1 KB
21 - Transfer learning/001 Transfer learning What, why, and when_en.srt 23.9 KB
06 - Gradient descent/003 Gradient descent in 1D_en.srt 23.8 KB
15 - Weight inits and investigations/006 CodeChallenge Xavier vs. Kaiming_en.srt 23.7 KB
21 - Transfer learning/005 Transfer learning with ResNet-18_en.srt 23.6 KB
11 - FFNs (Feed-Forward Networks)/005 CodeChallenge Data normalization_en.srt 23.6 KB
19 - Understand and design CNNs/008 Do autoencoders clean Gaussians_en.srt 23.5 KB
05 - Math, numpy, PyTorch/017 Derivatives intuition and polynomials_en.srt 23.5 KB
10 - Metaparameters (activations, optimizers)/014 Loss functions_en.srt 23.4 KB
31 - Python intro Text and plots/001 Printing and string interpolation_en.srt 23.4 KB
03 - Concepts in deep learning/005 Are artificial neurons like biological neurons_en.srt 23.3 KB
12 - More on data/005 Data oversampling in MNIST_en.srt 23.2 KB
30 - Python intro Flow control/002 If-else statements, part 2_en.srt 23.2 KB
15 - Weight inits and investigations/002 A surprising demo of weight initializations_en.srt 23.0 KB
18 - Convolution and transformations/011 Image transforms_en.srt 22.9 KB
23 - Generative adversarial networks/001 GAN What, why, and how_en.srt 22.7 KB
06 - Gradient descent/008 CodeChallenge fixed vs. dynamic learning rate_en.srt 22.5 KB
12 - More on data/002 Data size and network size_en.srt 22.5 KB
03 - Concepts in deep learning/003 The role of DL in science and knowledge_en.srt 22.5 KB
19 - Understand and design CNNs/011 Discover the Gaussian parameters_en.srt 22.4 KB
31 - Python intro Text and plots/003 Subplot geometry_en.srt 22.2 KB
10 - Metaparameters (activations, optimizers)/003 CodeChallenge Minibatch size in the wine dataset_en.srt 22.2 KB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/006 More on RNNs Hidden states, embeddings_en.srt 22.0 KB
16 - Autoencoders/002 Denoising MNIST_en.srt 21.9 KB
05 - Math, numpy, PyTorch/013 Mean and variance_en.srt 21.7 KB
15 - Weight inits and investigations/005 Xavier and Kaiming initializations_en.srt 21.7 KB
17 - Running models on a GPU/001 What is a GPU and why use it_en.srt 21.6 KB
07 - ANNs (Artificial Neural Networks)/003 ANN math part 1 (forward prop)_en.srt 21.4 KB
23 - Generative adversarial networks/004 CNN GAN with Gaussians_en.srt 21.3 KB
10 - Metaparameters (activations, optimizers)/019 Optimizers (RMSprop, Adam)_en.srt 21.2 KB
12 - More on data/010 Save the best-performing model_en.srt 21.1 KB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/002 How RNNs work_en.srt 21.0 KB
11 - FFNs (Feed-Forward Networks)/006 Distributions of weights pre- and post-learning_en.srt 20.9 KB
30 - Python intro Flow control/007 Single-line loops (list comprehension)_en.srt 20.9 KB
30 - Python intro Flow control/001 If-else statements_en.srt 20.8 KB
21 - Transfer learning/003 CodeChallenge letters to numbers_en.srt 20.8 KB
06 - Gradient descent/005 Gradient descent in 2D_en.srt 20.7 KB
03 - Concepts in deep learning/001 What is an artificial neural network_en.srt 20.6 KB
30 - Python intro Flow control/009 Broadcasting in numpy_en.srt 20.5 KB
06 - Gradient descent/001 Overview of gradient descent_en.srt 20.1 KB
05 - Math, numpy, PyTorch/008 Matrix multiplication_en.srt 19.8 KB
27 - Python intro Data types/004 Lists (1 of 2)_en.srt 19.6 KB
10 - Metaparameters (activations, optimizers)/016 More practice with multioutput ANNs_en.srt 19.6 KB
29 - Python intro Functions/003 Python libraries (pandas)_en.srt 19.5 KB
18 - Convolution and transformations/009 Pooling in PyTorch_en.srt 19.4 KB
29 - Python intro Functions/002 Python libraries (numpy)_en.srt 19.3 KB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/008 The LSTM and GRU classes_en.srt 19.3 KB
18 - Convolution and transformations/007 Transpose convolution_en.srt 19.2 KB
10 - Metaparameters (activations, optimizers)/004 Data normalization_en.srt 19.0 KB
19 - Understand and design CNNs/015 CodeChallenge Varying number of channels_en.srt 18.9 KB
29 - Python intro Functions/006 Global and local variable scopes_en.srt 18.9 KB
07 - ANNs (Artificial Neural Networks)/002 A geometric view of ANNs_en.srt 18.7 KB
05 - Math, numpy, PyTorch/016 The t-test_en.srt 18.7 KB
15 - Weight inits and investigations/008 Freezing weights during learning_en.srt 18.5 KB
13 - Measuring model performance/004 APRF example 1 wine quality_en.srt 18.5 KB
03 - Concepts in deep learning/004 Running experiments to understand DL_en.srt 18.4 KB
09 - Regularization/001 Regularization Concept and methods_en.srt 18.4 KB
09 - Regularization/007 L2 regularization in practice_en.srt 18.3 KB
18 - Convolution and transformations/005 The Conv2 class in PyTorch_en.srt 18.3 KB
24 - RNNs (Recurrent Neural Networks) (and GRULSTM)/001 Leveraging sequences in deep learning_en.srt 18.1 KB
03 - Concepts in deep learning/002 How models learn_en.srt 18.1 KB
10 - Metaparameters (activations, optimizers)/006 Batch normalization_en.srt 18.0 KB
12 - More on data/006 Data noise augmentation (with devset+test)_en.srt 17.9 KB
08 - Overfitting and cross-validation/004 Cross-validation -- manual separation_en.srt 17.9 KB
15 - Weight inits and investigations/004 CodeChallenge Weight variance inits_en.srt 17.7 KB
11 - FFNs (Feed-Forward Networks)/002 The MNIST dataset_en.srt 17.7 KB
15 - Weight inits and investigations/003 Theory Why and how to initialize weights_en.srt 17.6 KB
05 - Math, numpy, PyTorch/012 Minmax and argminargmax_en.srt 17.5 KB
09 - Regularization/012 CodeChallenge Effects of mini-batch size_en.srt 17.4 KB
18 - Convolution and transformations/004 Convolution parameters (stride, padding)_en.srt 17.4 KB
08 - Overfitting and cross-validation/001 What is overfitting and is it as bad as they say_en.srt 17.4 KB
28 - Python intro Indexing, slicing/001 Indexing_en.srt 17.4 KB
13 - Measuring model performance/002 Accuracy, precision, recall, F1_en.srt 17.3 KB
15 - Weight inits and investigations/007 CodeChallenge Identically random weights_en.srt 17.3 KB
31 - Python intro Text and plots/002 Plotting dots and lines_en.srt 17.3 KB
10 - Metaparameters (activations, optimizers)/023 Learning rate decay_en.srt 17.2 KB
07 - ANNs (Artificial Neural Networks)/014 CodeChallenge more qwerties!_en.srt 17.1 KB
11 - FFNs (Feed-Forward Networks)/007 CodeChallenge MNIST and breadth vs. depth_en.srt 17.1 KB
09 - Regularization/008 L1 regularization in practice_en.srt 16.8 KB
20 - CNN milestone projects/002 Project 1 My solution_en.srt 16.6 KB
15 - Weight inits and investigations/001 Explanation of weight matrix sizes_en.srt 16.6 KB
06 - Gradient descent/002 What about local minima_en.srt 16.5 KB
13 - Measuring model performance/005 APRF example 2 MNIST_en.srt 16.5 KB
27 - Python intro Data types/008 Dictionaries_en.srt 16.4 KB
10 - Metaparameters (activations, optimizers)/010 Activation functions in PyTorch_en.srt 16.4 KB
19 - Understand and design CNNs/007 CodeChallenge How wide the FC_en.srt 16.3 KB
14 - FFN milestone projects/002 Project 1 My solution_en.srt 16.3 KB
16 - Autoencoders/001 What are autoencoders and what do they do_en.srt 16.3 KB
20 - CNN milestone projects/005 Project 4 Psychometric functions in CNNs_en.srt 16.3 KB
10 - Metaparameters (activations, optimizers)/024 How to pick the right metaparameters_en.srt 16.2 KB
09 - Regularization/009 Training in mini-batches_en.srt 16.2 KB
22 - Style transfer/002 The Gram matrix (feature activation covariance)_en.srt 16.2 KB
11 - FFNs (Feed-Forward Networks)/010 Shifted MNIST_en.srt 16.2 KB
25 - Ethics of deep learning/005 Accountability and making ethical AI_en.srt 16.1 KB
05 - Math, numpy, PyTorch/014 Random sampling and sampling variability_en.srt 15.8 KB
30 - Python intro Flow control/004 Enumerate and zip_en.srt 15.4 KB
06 - Gradient descent/004 CodeChallenge unfortunate starting value_en.srt 15.4 KB
31 - Python intro Text and plots/005 Seaborn_en.srt 15.3 KB
11 - FFNs (Feed-Forward Networks)/011 CodeChallenge The mystery of the missing 7_en.srt 15.2 KB
19 - Understand and design CNNs/001 The canonical CNN architecture_en.srt 15.1 KB
09 - Regularization/010 Batch training in action_en.srt 15.1 KB
07 - ANNs (Artificial Neural Networks)/005 ANN math part 3 (backprop)_en.srt 14.7 KB
25 - Ethics of deep learning/003 Some other possible ethical scenarios_en.srt 14.7 KB
10 - Metaparameters (activations, optimizers)/020 Optimizers comparison_en.srt 14.3 KB
17 - Running models on a GPU/002 Implementation_en.srt 14.2 KB
07 - ANNs (Artificial Neural Networks)/015 Comparing the number of hidden units_en.srt 14.0 KB
21 - Transfer learning/002 Transfer learning MNIST - FMNIST_en.srt 14.0 KB
18 - Convolution and transformations/010 To pool or to stride_en.srt 14.0 KB
27 - Python intro Data types/005 Lists (2 of 2)_en.srt 14.0 KB
14 - FFN milestone projects/005 Project 3 FFN for missing data interpolation_en.srt 13.9 KB
25 - Ethics of deep learning/001 Will AI save us or destroy us_en.srt 13.8 KB
13 - Measuring model performance/007 Computation time_en.srt 13.7 KB
19 - Understand and design CNNs/013 Dropout in CNNs_en.srt 13.7 KB
05 - Math, numpy, PyTorch/019 Derivatives product and chain rules_en.srt 13.6 KB
19 - Understand and design CNNs/009 CodeChallenge AEs and occluded Gaussians_en.srt 13.5 KB
18 - Convolution and transformations/002 Feature maps and convolution kernels_en.srt 13.4 KB
05 - Math, numpy, PyTorch/007 OMG it's the dot product!_en.srt 13.4 KB
23 - Generative adversarial networks/003 CodeChallenge Linear GAN with FMNIST_en.srt 13.4 KB
07 - ANNs (Artificial Neural Networks)/004 ANN math part 2 (errors, loss, cost)_en.srt 13.4 KB
08 - Overfitting and cross-validation/007 Splitting data into train, devset, test_en.srt 13.3 KB
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10 - Metaparameters (activations, optimizers)/011 Activation functions comparison_en.srt 13.1 KB
05 - Math, numpy, PyTorch/003 Spectral theories in mathematics_en.srt 13.1 KB
01 - Introduction/001 How to learn from this course_en.srt 12.5 KB
13 - Measuring model performance/006 CodeChallenge MNIST with unequal groups_en.srt 12.5 KB
07 - ANNs (Artificial Neural Networks)/021 Reflection Are DL models understandable yet_en.srt 12.1 KB
26 - Where to go from here/001 How to learn topic _X_ in deep learning_en.srt 11.9 KB
05 - Math, numpy, PyTorch/018 Derivatives find minima_en.srt 11.8 KB
01 - Introduction/002 Using Udemy like a pro_en.srt 11.8 KB
07 - ANNs (Artificial Neural Networks)/011 Linear solutions to linear problems_en.srt 11.7 KB
19 - Understand and design CNNs/003 CNN on shifted MNIST_en.srt 11.7 KB
27 - Python intro Data types/006 Tuples_en.srt 11.5 KB
13 - Measuring model performance/008 Better performance in test than train_en.srt 11.5 KB
08 - Overfitting and cross-validation/008 Cross-validation on regression_en.srt 11.5 KB
14 - FFN milestone projects/006 Project 3 My solution_en.srt 11.4 KB
05 - Math, numpy, PyTorch/015 Reproducible randomness via seeding_en.srt 11.3 KB
11 - FFNs (Feed-Forward Networks)/012 Universal approximation theorem_en.srt 11.3 KB
23 - Generative adversarial networks/007 CodeChallenge CNN GAN with CIFAR_en.srt 11.2 KB
10 - Metaparameters (activations, optimizers)/018 SGD with momentum_en.srt 11.1 KB
10 - Metaparameters (activations, optimizers)/007 Batch normalization in practice_en.srt 11.1 KB
05 - Math, numpy, PyTorch/010 Logarithms_en.srt 11.0 KB
31 - Python intro Text and plots/007 Export plots in low and high resolution_en.srt 10.9 KB
10 - Metaparameters (activations, optimizers)/012 CodeChallenge Compare relu variants_en.srt 10.9 KB
11 - FFNs (Feed-Forward Networks)/009 Scrambled MNIST_en.srt 10.8 KB
12 - More on data/004 What to do about unbalanced designs_en.srt 10.8 KB
29 - Python intro Functions/004 Getting help on functions_en.srt 10.6 KB
14 - FFN milestone projects/003 Project 2 Predicting heart disease_en.srt 10.6 KB
14 - FFN milestone projects/001 Project 1 A gratuitously complex adding machine_en.srt 10.3 KB
05 - Math, numpy, PyTorch/004 Terms and datatypes in math and computers_en.srt 10.3 KB
20 - CNN milestone projects/001 Project 1 Import and classify CIFAR10_en.srt 10.2 KB
29 - Python intro Functions/001 Inputs and outputs_en.srt 10.2 KB
22 - Style transfer/005 CodeChallenge Style transfer with AlexNet_en.srt 10.1 KB
10 - Metaparameters (activations, optimizers)/022 CodeChallenge Adam with L2 regularization_en.srt 9.9 KB
13 - Measuring model performance/001 Two perspectives of the world_en.srt 9.9 KB
09 - Regularization/002 train() and eval() modes_en.srt 9.8 KB
18 - Convolution and transformations/006 CodeChallenge Choose the parameters_en.srt 9.8 KB
30 - Python intro Flow control/005 Continue_en.srt 9.7 KB
05 - Math, numpy, PyTorch/006 Vector and matrix transpose_en.srt 9.6 KB
19 - Understand and design CNNs/014 CodeChallenge How low can you go_en.srt 9.6 KB
11 - FFNs (Feed-Forward Networks)/008 CodeChallenge Optimizers and MNIST_en.srt 9.6 KB
17 - Running models on a GPU/003 CodeChallenge Run an experiment on the GPU_en.srt 9.4 KB
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10 - Metaparameters (activations, optimizers)/021 CodeChallenge Optimizers and... something_en.srt 9.2 KB
05 - Math, numpy, PyTorch/005 Converting reality to numbers_en.srt 9.1 KB
09 - Regularization/011 The importance of equal batch sizes_en.srt 9.1 KB
02 - Download all course materials/001 Downloading and using the code_en.srt 9.1 KB
13 - Measuring model performance/003 APRF in code_en.srt 9.0 KB
23 - Generative adversarial networks/006 CNN GAN with FMNIST_en.srt 8.9 KB
07 - ANNs (Artificial Neural Networks)/012 Why multilayer linear models don't exist_en.srt 8.9 KB
06 - Gradient descent/009 Vanishing and exploding gradients_en.srt 8.8 KB
09 - Regularization/005 Dropout example 2_en.srt 8.8 KB
25 - Ethics of deep learning/002 Example case studies_en.srt 8.8 KB
12 - More on data/009 Save and load trained models_en.srt 8.6 KB
23 - Generative adversarial networks/005 CodeChallenge Gaussians with fewer layers_en.srt 8.6 KB
12 - More on data/008 Getting data into colab_en.srt 8.6 KB
08 - Overfitting and cross-validation/003 Generalization_en.srt 8.5 KB
21 - Transfer learning/004 Famous CNN architectures_en.srt 8.4 KB
12 - More on data/011 Where to find online datasets_en.srt 8.1 KB
06 - Gradient descent/006 CodeChallenge 2D gradient ascent_en.srt 7.2 KB
10 - Metaparameters (activations, optimizers)/008 CodeChallenge Batch-normalize the qwerties_en.srt 7.2 KB
11 - FFNs (Feed-Forward Networks)/004 CodeChallenge Binarized MNIST images_en.srt 7.1 KB
10 - Metaparameters (activations, optimizers)/001 What are metaparameters_en.srt 7.1 KB
29 - Python intro Functions/007 Copies and referents of variables_en.srt 7.0 KB
20 - CNN milestone projects/003 Project 2 CIFAR-autoencoder_en.srt 6.7 KB
11 - FFNs (Feed-Forward Networks)/001 What are fully-connected and feedforward networks_en.srt 6.7 KB
19 - Understand and design CNNs/016 So many possibilities! How to create a CNN_en.srt 6.3 KB
15 - Weight inits and investigations/010 Use default inits or apply your own_en.srt 6.1 KB
22 - Style transfer/001 What is style transfer and how does it work_en.srt 6.1 KB
04 - About the Python tutorial/001 Should you watch the Python tutorial_en.srt 5.9 KB
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21 - Transfer learning/006 CodeChallenge VGG-16_en.srt 4.9 KB
32 - Bonus section/001 Bonus content.html 4.6 KB
05 - Math, numpy, PyTorch/002 Introduction to this section_en.srt 2.8 KB
06 - Gradient descent/010 Tangent Notebook revision history_en.srt 2.7 KB
02 - Download all course materials/002 My policy on code-sharing_en.srt 2.4 KB
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07 - ANNs (Artificial Neural Networks)/020 Diversity of ANN visual representations.html 517 bytes
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