[Udemy] A deep understanding of deep learning (with Python intro) (08.2021)

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  • 01 Introduction/001 How to learn from this course.mp4 54.97 MB
    01 Introduction/002 Using Udemy like a pro.en.srt 12.34 KB
    01 Introduction/002 Using Udemy like a pro.mp4 54.37 MB
    02 Download all course materials/001 Downloading and using the code.en.srt 9.41 KB
    02 Download all course materials/001 Downloading and using the code.mp4 45.65 MB
    02 Download all course materials/002 My policy on code-sharing.en.srt 2.52 KB
    02 Download all course materials/002 My policy on code-sharing.mp4 10.24 MB
    02 Download all course materials/003 DUDL_PythonCode.zip 700.8 KB
    03 Concepts in deep learning/001 What is an artificial neural network_.en.srt 21.3 KB
    03 Concepts in deep learning/001 What is an artificial neural network_.mp4 65.38 MB
    03 Concepts in deep learning/002 How models _learn_.en.srt 18.75 KB
    03 Concepts in deep learning/002 How models _learn_.mp4 72.79 MB
    03 Concepts in deep learning/003 The role of DL in science and knowledge.en.srt 23.31 KB
    03 Concepts in deep learning/003 The role of DL in science and knowledge.mp4 121.55 MB
    03 Concepts in deep learning/004 Running experiments to understand DL.en.srt 19.22 KB
    03 Concepts in deep learning/004 Running experiments to understand DL.mp4 74.84 MB
    03 Concepts in deep learning/005 Are artificial _neurons_ like biological neurons_.en.srt 24.16 KB
    03 Concepts in deep learning/005 Are artificial _neurons_ like biological neurons_.mp4 114.65 MB
    04 About the Python tutorial/001 Should you watch the Python tutorial_.en.srt 6.14 KB
    04 About the Python tutorial/001 Should you watch the Python tutorial_.mp4 23.77 MB
    05 Math, numpy, PyTorch/001 Introduction to this section.en.srt 2.9 KB
    05 Math, numpy, PyTorch/001 Introduction to this section.mp4 11.12 MB
    05 Math, numpy, PyTorch/002 Spectral theories in mathematics.en.srt 13.58 KB
    05 Math, numpy, PyTorch/002 Spectral theories in mathematics.mp4 51.06 MB
    05 Math, numpy, PyTorch/003 Terms and datatypes in math and computers.en.srt 10.68 KB
    05 Math, numpy, PyTorch/003 Terms and datatypes in math and computers.mp4 38.08 MB
    05 Math, numpy, PyTorch/004 Converting reality to numbers.en.srt 9.57 KB
    05 Math, numpy, PyTorch/004 Converting reality to numbers.mp4 33.21 MB
    05 Math, numpy, PyTorch/005 Vector and matrix transpose.en.srt 10.01 KB
    05 Math, numpy, PyTorch/005 Vector and matrix transpose.mp4 37.66 MB
    05 Math, numpy, PyTorch/006 OMG it's the dot product!.en.srt 13.95 KB
    05 Math, numpy, PyTorch/006 OMG it's the dot product!.mp4 50.11 MB
    05 Math, numpy, PyTorch/007 Matrix multiplication.en.srt 20.6 KB
    05 Math, numpy, PyTorch/007 Matrix multiplication.mp4 85.67 MB
    05 Math, numpy, PyTorch/008 Softmax.en.srt 27.76 KB
    05 Math, numpy, PyTorch/008 Softmax.mp4 95.96 MB
    05 Math, numpy, PyTorch/009 Logarithms.en.srt 11.47 KB
    05 Math, numpy, PyTorch/009 Logarithms.mp4 43.88 MB
    05 Math, numpy, PyTorch/010 Entropy and cross-entropy.mp4 106 MB
    05 Math, numpy, PyTorch/011 Min_max and argmin_argmax.en.srt 18.16 KB
    05 Math, numpy, PyTorch/011 Min_max and argmin_argmax.mp4 88.21 MB
    05 Math, numpy, PyTorch/012 Mean and variance.en.srt 22.48 KB
    05 Math, numpy, PyTorch/012 Mean and variance.mp4 80.57 MB
    05 Math, numpy, PyTorch/013 Random sampling and sampling variability.en.srt 16.36 KB
    05 Math, numpy, PyTorch/013 Random sampling and sampling variability.mp4 85.42 MB
    05 Math, numpy, PyTorch/014 Reproducible randomness via seeding.en.srt 11.76 KB
    05 Math, numpy, PyTorch/014 Reproducible randomness via seeding.mp4 69.7 MB
    05 Math, numpy, PyTorch/015 The t-test.en.srt 19.38 KB
    05 Math, numpy, PyTorch/015 The t-test.mp4 81.36 MB
    05 Math, numpy, PyTorch/016 Derivatives_ intuition and polynomials.en.srt 24.42 KB
    05 Math, numpy, PyTorch/016 Derivatives_ intuition and polynomials.mp4 80.3 MB
    05 Math, numpy, PyTorch/017 Derivatives find minima.en.srt 12.19 KB
    05 Math, numpy, PyTorch/017 Derivatives find minima.mp4 45.47 MB
    05 Math, numpy, PyTorch/018 Derivatives_ product and chain rules.en.srt 13.53 KB
    05 Math, numpy, PyTorch/018 Derivatives_ product and chain rules.mp4 55.63 MB
    06 Gradient descent/001 Overview of gradient descent.en.srt 20.89 KB
    06 Gradient descent/001 Overview of gradient descent.mp4 68.44 MB
    06 Gradient descent/002 What about local minima_.en.srt 17.17 KB
    06 Gradient descent/002 What about local minima_.mp4 67.08 MB
    06 Gradient descent/003 Gradient descent in 1D.en.srt 24.75 KB
    06 Gradient descent/003 Gradient descent in 1D.mp4 119.29 MB
    06 Gradient descent/004 CodeChallenge_ unfortunate starting value.en.srt 16 KB
    06 Gradient descent/004 CodeChallenge_ unfortunate starting value.mp4 77.09 MB
    06 Gradient descent/005 Gradient descent in 2D.en.srt 21.35 KB
    06 Gradient descent/005 Gradient descent in 2D.mp4 95.9 MB
    06 Gradient descent/006 CodeChallenge_ 2D gradient ascent.en.srt 7.53 KB
    06 Gradient descent/006 CodeChallenge_ 2D gradient ascent.mp4 39.36 MB
    06 Gradient descent/007 Parametric experiments on g.d.en.srt 27.15 KB
    06 Gradient descent/007 Parametric experiments on g.d.mp4 135.61 MB
    06 Gradient descent/008 CodeChallenge_ fixed vs. dynamic learning rate.en.srt 23.05 KB
    06 Gradient descent/008 CodeChallenge_ fixed vs. dynamic learning rate.mp4 114.56 MB
    06 Gradient descent/009 Vanishing and exploding gradients.en.srt 9.06 KB
    06 Gradient descent/009 Vanishing and exploding gradients.mp4 30.24 MB
    06 Gradient descent/010 Tangent_ Notebook revision history.en.srt 2.76 KB
    06 Gradient descent/010 Tangent_ Notebook revision history.mp4 22.18 MB
    07 ANNs/001 The perceptron and ANN architecture.en.srt 26.8 KB
    07 ANNs/001 The perceptron and ANN architecture.mp4 83.64 MB
    07 ANNs/002 A geometric view of ANNs.en.srt 19.37 KB
    07 ANNs/002 A geometric view of ANNs.mp4 70.88 MB
    07 ANNs/003 ANN math part 1 (forward prop).en.srt 17.39 KB
    07 ANNs/003 ANN math part 1 (forward prop).mp4 57.9 MB
    07 ANNs/004 ANN math part 2 (errors, loss, cost).en.srt 13.89 KB
    07 ANNs/004 ANN math part 2 (errors, loss, cost).mp4 48.47 MB
    07 ANNs/005 ANN math part 3 (backprop).en.srt 15.23 KB
    07 ANNs/005 ANN math part 3 (backprop).mp4 52.89 MB
    07 ANNs/006 ANN for regression.en.srt 35.85 KB
    07 ANNs/006 ANN for regression.mp4 135.5 MB
    07 ANNs/007 CodeChallenge_ manipulate regression slopes.en.srt 28.31 KB
    07 ANNs/007 CodeChallenge_ manipulate regression slopes.mp4 139.12 MB
    07 ANNs/008 ANN for classifying qwerties.en.srt 34 KB
    07 ANNs/008 ANN for classifying qwerties.mp4 151.12 MB
    07 ANNs/009 Learning rates comparison.en.srt 36.24 KB
    07 ANNs/009 Learning rates comparison.mp4 168.64 MB
    07 ANNs/010 Multilayer ANN.en.srt 29.4 KB
    07 ANNs/010 Multilayer ANN.mp4 144.7 MB
    07 ANNs/011 Linear solutions to linear problems.en.srt 12.17 KB
    07 ANNs/011 Linear solutions to linear problems.mp4 50.37 MB
    07 ANNs/012 Why multilayer linear models don't exist.en.srt 9.21 KB
    07 ANNs/012 Why multilayer linear models don't exist.mp4 26.46 MB
    07 ANNs/013 Multi-output ANN (iris dataset).en.srt 37.47 KB
    07 ANNs/013 Multi-output ANN (iris dataset).mp4 186.77 MB
    07 ANNs/014 CodeChallenge_ more qwerties!.en.srt 17.79 KB
    07 ANNs/014 CodeChallenge_ more qwerties!.mp4 95.1 MB
    07 ANNs/015 Comparing the number of hidden units.en.srt 14.62 KB
    07 ANNs/015 Comparing the number of hidden units.mp4 71.15 MB
    07 ANNs/016 Depth vs. breadth_ number of parameters.en.srt 25.73 KB
    07 ANNs/016 Depth vs. breadth_ number of parameters.mp4 132.07 MB
    07 ANNs/017 Defining models using sequential vs. class.en.srt 19.17 KB
    07 ANNs/017 Defining models using sequential vs. class.mp4 89.48 MB
    07 ANNs/018 Model depth vs. breadth.en.srt 30.9 KB
    07 ANNs/018 Model depth vs. breadth.mp4 158.91 MB
    07 ANNs/019 CodeChallenge_ convert sequential to class.en.srt 9.73 KB
    07 ANNs/019 CodeChallenge_ convert sequential to class.mp4 51.44 MB
    07 ANNs/020 Diversity of ANN visual representations.html 1.4 KB
    07 ANNs/021 Reflection_ Are DL models understandable yet_.en.srt 12.37 KB
    07 ANNs/021 Reflection_ Are DL models understandable yet_.mp4 58.59 MB
    08 Overfitting and cross-validation/001 What is overfitting and is it as bad as they say_.en.srt 18.32 KB
    08 Overfitting and cross-validation/001 What is overfitting and is it as bad as they say_.mp4 73.13 MB
    08 Overfitting and cross-validation/002 Cross-validation.en.srt 24.96 KB
    08 Overfitting and cross-validation/002 Cross-validation.mp4 88.19 MB
    08 Overfitting and cross-validation/003 Generalization.en.srt 8.82 KB
    08 Overfitting and cross-validation/003 Generalization.mp4 32.44 MB
    08 Overfitting and cross-validation/004 Cross-validation -- manual separation.en.srt 18.59 KB
    08 Overfitting and cross-validation/004 Cross-validation -- manual separation.mp4 98.3 MB
    08 Overfitting and cross-validation/005 Cross-validation -- scikitlearn.en.srt 30.46 KB
    08 Overfitting and cross-validation/005 Cross-validation -- scikitlearn.mp4 142.88 MB
    08 Overfitting and cross-validation/006 Cross-validation -- DataLoader.en.srt 28.57 KB
    08 Overfitting and cross-validation/006 Cross-validation -- DataLoader.mp4 172.32 MB
    08 Overfitting and cross-validation/007 Splitting data into train, devset, test.en.srt 13.82 KB
    08 Overfitting and cross-validation/007 Splitting data into train, devset, test.mp4 79.21 MB
    08 Overfitting and cross-validation/008 Cross-validation on regression.en.srt 11.99 KB
    08 Overfitting and cross-validation/008 Cross-validation on regression.mp4 60.35 MB
    09 Regularization/001 Regularization_ Concept and methods.en.srt 19.02 KB
    09 Regularization/001 Regularization_ Concept and methods.mp4 80.05 MB
    09 Regularization/002 train() and eval() modes.en.srt 10.19 KB
    09 Regularization/002 train() and eval() modes.mp4 38.34 MB
    09 Regularization/003 Dropout regularization.en.srt 31.17 KB
    09 Regularization/003 Dropout regularization.mp4 136.03 MB
    09 Regularization/004 Dropout regularization in practice.en.srt 33.37 KB
    09 Regularization/004 Dropout regularization in practice.mp4 183.23 MB
    09 Regularization/005 Dropout example 2.en.srt 9.18 KB
    09 Regularization/005 Dropout example 2.mp4 53.87 MB
    09 Regularization/006 Weight regularization (L1_L2)_ math.en.srt 27.07 KB
    09 Regularization/006 Weight regularization (L1_L2)_ math.mp4 85.41 MB
    09 Regularization/007 L2 regularization in practice.en.srt 18.97 KB
    09 Regularization/007 L2 regularization in practice.mp4 110.47 MB
    09 Regularization/008 L1 regularization in practice.en.srt 17.44 KB
    09 Regularization/008 L1 regularization in practice.mp4 99.44 MB
    09 Regularization/009 Training in mini-batches.en.srt 16.88 KB
    09 Regularization/009 Training in mini-batches.mp4 62.12 MB
    09 Regularization/010 Batch training in action.en.srt 15.65 KB
    09 Regularization/010 Batch training in action.mp4 89.1 MB
    09 Regularization/011 The importance of equal batch sizes.en.srt 9.47 KB
    09 Regularization/011 The importance of equal batch sizes.mp4 60.11 MB
    09 Regularization/012 CodeChallenge_ Effects of mini-batch size.en.srt 18.09 KB
    09 Regularization/012 CodeChallenge_ Effects of mini-batch size.mp4 95.42 MB
    10 Metaparameters (activations, optimizers)/001 What are _metaparameters__.en.srt 7.35 KB
    10 Metaparameters (activations, optimizers)/001 What are _metaparameters__.mp4 32.7 MB
    10 Metaparameters (activations, optimizers)/002 The _wine quality_ dataset.en.srt 25.71 KB
    10 Metaparameters (activations, optimizers)/002 The _wine quality_ dataset.mp4 143.5 MB
    10 Metaparameters (activations, optimizers)/003 CodeChallenge_ Minibatch size in the wine dataset.en.srt 23.05 KB
    10 Metaparameters (activations, optimizers)/003 CodeChallenge_ Minibatch size in the wine dataset.mp4 118.79 MB
    10 Metaparameters (activations, optimizers)/004 Data normalization.en.srt 19.68 KB
    10 Metaparameters (activations, optimizers)/004 Data normalization.mp4 59.81 MB
    10 Metaparameters (activations, optimizers)/005 The importance of data normalization.en.srt 13.75 KB
    10 Metaparameters (activations, optimizers)/005 The importance of data normalization.mp4 64.65 MB
    10 Metaparameters (activations, optimizers)/006 Batch normalization.en.srt 18.66 KB
    10 Metaparameters (activations, optimizers)/006 Batch normalization.mp4 76.81 MB
    10 Metaparameters (activations, optimizers)/007 Batch normalization in practice.en.srt 11.05 KB
    10 Metaparameters (activations, optimizers)/007 Batch normalization in practice.mp4 61.76 MB
    10 Metaparameters (activations, optimizers)/008 CodeChallenge_ Batch-normalize the qwerties.en.srt 7.52 KB
    10 Metaparameters (activations, optimizers)/008 CodeChallenge_ Batch-normalize the qwerties.mp4 41.43 MB
    10 Metaparameters (activations, optimizers)/009 Activation functions.en.srt 26.49 KB
    10 Metaparameters (activations, optimizers)/009 Activation functions.mp4 97.03 MB
    10 Metaparameters (activations, optimizers)/010 Activation functions in PyTorch.en.srt 16.95 KB
    10 Metaparameters (activations, optimizers)/010 Activation functions in PyTorch.mp4 91.46 MB
    10 Metaparameters (activations, optimizers)/011 Activation functions comparison.en.srt 13.59 KB
    10 Metaparameters (activations, optimizers)/011 Activation functions comparison.mp4 73.9 MB
    10 Metaparameters (activations, optimizers)/012 CodeChallenge_ Compare relu variants.en.srt 11.29 KB
    10 Metaparameters (activations, optimizers)/012 CodeChallenge_ Compare relu variants.mp4 63.97 MB
    10 Metaparameters (activations, optimizers)/013 CodeChallenge_ Predict sugar.en.srt 24.98 KB
    10 Metaparameters (activations, optimizers)/013 CodeChallenge_ Predict sugar.mp4 122.1 MB
    10 Metaparameters (activations, optimizers)/014 Loss functions.en.srt 24.18 KB
    10 Metaparameters (activations, optimizers)/014 Loss functions.mp4 90.3 MB
    10 Metaparameters (activations, optimizers)/015 Loss functions in PyTorch.en.srt 26.87 KB
    10 Metaparameters (activations, optimizers)/015 Loss functions in PyTorch.mp4 138.1 MB
    10 Metaparameters (activations, optimizers)/016 More practice with multioutput ANNs.en.srt 20.32 KB
    10 Metaparameters (activations, optimizers)/016 More practice with multioutput ANNs.mp4 99.8 MB
    10 Metaparameters (activations, optimizers)/017 Optimizers (minibatch, momentum).mp4 98.07 MB
    10 Metaparameters (activations, optimizers)/018 SGD with momentum.en.srt 11.58 KB
    10 Metaparameters (activations, optimizers)/018 SGD with momentum.mp4 62.1 MB
    10 Metaparameters (activations, optimizers)/019 Optimizers (RMSprop, Adam).en.srt 22.06 KB
    10 Metaparameters (activations, optimizers)/019 Optimizers (RMSprop, Adam).mp4 76.73 MB
    10 Metaparameters (activations, optimizers)/020 Optimizers comparison.en.srt 14.64 KB
    10 Metaparameters (activations, optimizers)/020 Optimizers comparison.mp4 86.88 MB
    10 Metaparameters (activations, optimizers)/021 CodeChallenge_ Optimizers and... something.en.srt 9.36 KB
    10 Metaparameters (activations, optimizers)/021 CodeChallenge_ Optimizers and... something.mp4 49.77 MB
    10 Metaparameters (activations, optimizers)/022 CodeChallenge_ Adam with L2 regularization.en.srt 10.3 KB
    10 Metaparameters (activations, optimizers)/022 CodeChallenge_ Adam with L2 regularization.mp4 53 MB
    10 Metaparameters (activations, optimizers)/023 Learning rate decay.en.srt 17.91 KB
    10 Metaparameters (activations, optimizers)/023 Learning rate decay.mp4 96.9 MB
    10 Metaparameters (activations, optimizers)/024 How to pick the right metaparameters.en.srt 16.65 KB
    10 Metaparameters (activations, optimizers)/024 How to pick the right metaparameters.mp4 61.74 MB
    11 FFNs/001 What are fully-connected and feedforward networks_.en.srt 6.94 KB
    11 FFNs/001 What are fully-connected and feedforward networks_.mp4 25.53 MB
    11 FFNs/002 The MNIST dataset.en.srt 18.71 KB
    11 FFNs/002 The MNIST dataset.mp4 101.46 MB
    11 FFNs/003 FFN to classify digits.en.srt 32.91 KB
    11 FFNs/003 FFN to classify digits.mp4 161.85 MB
    11 FFNs/004 CodeChallenge_ Binarized MNIST images.en.srt 7.37 KB
    11 FFNs/004 CodeChallenge_ Binarized MNIST images.mp4 40.78 MB
    11 FFNs/005 CodeChallenge_ Data normalization.en.srt 24.48 KB
    11 FFNs/005 CodeChallenge_ Data normalization.mp4 96.25 MB
    11 FFNs/006 Distributions of weights pre- and post-learning.en.srt 22.05 KB
    11 FFNs/006 Distributions of weights pre- and post-learning.mp4 116.26 MB
    11 FFNs/007 CodeChallenge_ MNIST and breadth vs. depth.en.srt 17.73 KB
    11 FFNs/007 CodeChallenge_ MNIST and breadth vs. depth.mp4 95.21 MB
    11 FFNs/008 CodeChallenge_ Optimizers and MNIST.en.srt 9.92 KB
    11 FFNs/008 CodeChallenge_ Optimizers and MNIST.mp4 46.26 MB
    11 FFNs/009 Scrambled MNIST.en.srt 11.24 KB
    11 FFNs/009 Scrambled MNIST.mp4 60.17 MB
    11 FFNs/010 Shifted MNIST.en.srt 16.47 KB
    11 FFNs/010 Shifted MNIST.mp4 77.91 MB
    11 FFNs/011 CodeChallenge_ The mystery of the missing 7.en.srt 15.78 KB
    11 FFNs/011 CodeChallenge_ The mystery of the missing 7.mp4 74.25 MB
    11 FFNs/012 Universal approximation theorem.en.srt 11.7 KB
    11 FFNs/012 Universal approximation theorem.mp4 49.18 MB
    12 More on data/001 Anatomy of a torch dataset and dataloader.en.srt 26.46 KB
    12 More on data/001 Anatomy of a torch dataset and dataloader.mp4 135.84 MB
    12 More on data/002 Data size and network size.en.srt 23.39 KB
    12 More on data/002 Data size and network size.mp4 135.67 MB
    12 More on data/003 CodeChallenge_ unbalanced data.en.srt 29.32 KB
    12 More on data/003 CodeChallenge_ unbalanced data.mp4 166.26 MB
    12 More on data/004 What to do about unbalanced designs_.mp4 54.21 MB
    12 More on data/005 Data oversampling in MNIST.en.srt 24.16 KB
    12 More on data/005 Data oversampling in MNIST.mp4 122.59 MB
    12 More on data/006 Data noise augmentation (with devset+test).en.srt 18.62 KB
    12 More on data/006 Data noise augmentation (with devset+test).mp4 106.09 MB
    12 More on data/007 Data feature augmentation.en.srt 28.32 KB
    12 More on data/007 Data feature augmentation.mp4 158.27 MB
    12 More on data/008 Getting data into colab.en.srt 8.85 KB
    12 More on data/008 Getting data into colab.mp4 43.75 MB
    12 More on data/009 Save and load trained models.en.srt 8.95 KB
    12 More on data/009 Save and load trained models.mp4 55.71 MB
    12 More on data/010 Save the best-performing model.en.srt 21.98 KB
    12 More on data/010 Save the best-performing model.mp4 126.5 MB
    12 More on data/011 Where to find online datasets.en.srt 8.19 KB
    12 More on data/011 Where to find online datasets.mp4 41.7 MB
    13 Measuring model performance/001 Two perspectives of the world.en.srt 10.32 KB
    13 Measuring model performance/001 Two perspectives of the world.mp4 40.01 MB
    13 Measuring model performance/002 Accuracy, precision, recall, F1.en.srt 18.08 KB
    13 Measuring model performance/002 Accuracy, precision, recall, F1.mp4 72.57 MB
    13 Measuring model performance/003 APRF in code.en.srt 9.38 KB
    13 Measuring model performance/003 APRF in code.mp4 51.79 MB
    13 Measuring model performance/004 APRF example 1_ wine quality.en.srt 19.25 KB
    13 Measuring model performance/004 APRF example 1_ wine quality.mp4 107.35 MB
    13 Measuring model performance/005 APRF example 2_ MNIST.en.srt 17.17 KB
    13 Measuring model performance/005 APRF example 2_ MNIST.mp4 98.62 MB
    13 Measuring model performance/006 CodeChallenge_ MNIST with unequal groups.en.srt 12.7 KB
    13 Measuring model performance/006 CodeChallenge_ MNIST with unequal groups.mp4 62.37 MB
    13 Measuring model performance/007 Computation time.en.srt 14.26 KB
    13 Measuring model performance/007 Computation time.mp4 81.73 MB
    13 Measuring model performance/008 Better performance in test than train_.en.srt 11.95 KB
    13 Measuring model performance/008 Better performance in test than train_.mp4 44.83 MB
    14 FFN milestone projects/001 Project 1_ A gratuitously complex adding machine.en.srt 10.75 KB
    14 FFN milestone projects/001 Project 1_ A gratuitously complex adding machine.mp4 48.55 MB
    14 FFN milestone projects/002 Project 1_ My solution.en.srt 16.97 KB
    14 FFN milestone projects/002 Project 1_ My solution.mp4 99.75 MB
    14 FFN milestone projects/003 Project 2_ Predicting heart disease.en.srt 10.99 KB
    14 FFN milestone projects/003 Project 2_ Predicting heart disease.mp4 50.61 MB
    14 FFN milestone projects/004 Project 2_ My solution.en.srt 27.78 KB
    14 FFN milestone projects/004 Project 2_ My solution.mp4 155.73 MB
    14 FFN milestone projects/005 Project 3_ FFN for missing data interpolation.en.srt 14.38 KB
    14 FFN milestone projects/005 Project 3_ FFN for missing data interpolation.mp4 45.39 MB
    14 FFN milestone projects/006 Project 3_ My solution.en.srt 11.88 KB
    14 FFN milestone projects/006 Project 3_ My solution.mp4 75.48 MB
    15 Weight inits and investigations/001 Explanation of weight matrix sizes.en.srt 17.18 KB
    15 Weight inits and investigations/001 Explanation of weight matrix sizes.mp4 68.98 MB
    15 Weight inits and investigations/002 A surprising demo of weight initializations.en.srt 23.89 KB
    15 Weight inits and investigations/002 A surprising demo of weight initializations.mp4 121.57 MB
    15 Weight inits and investigations/003 Theory_ Why and how to initialize weights.en.srt 18.23 KB
    15 Weight inits and investigations/003 Theory_ Why and how to initialize weights.mp4 79.41 MB
    15 Weight inits and investigations/004 CodeChallenge_ Weight variance inits.en.srt 18.42 KB
    15 Weight inits and investigations/004 CodeChallenge_ Weight variance inits.mp4 103.96 MB
    15 Weight inits and investigations/005 Xavier and Kaiming initializations.en.srt 22.52 KB
    15 Weight inits and investigations/005 Xavier and Kaiming initializations.mp4 134.08 MB
    15 Weight inits and investigations/006 CodeChallenge_ Xavier vs. Kaiming.en.srt 24.65 KB
    15 Weight inits and investigations/006 CodeChallenge_ Xavier vs. Kaiming.mp4 126.5 MB
    15 Weight inits and investigations/007 CodeChallenge_ Identically random weights.en.srt 17.92 KB
    15 Weight inits and investigations/007 CodeChallenge_ Identically random weights.mp4 88.17 MB
    15 Weight inits and investigations/008 Freezing weights during learning.en.srt 19.25 KB
    15 Weight inits and investigations/008 Freezing weights during learning.mp4 93.15 MB
    15 Weight inits and investigations/009 Learning-related changes in weights.en.srt 32.77 KB
    15 Weight inits and investigations/009 Learning-related changes in weights.mp4 146.78 MB
    15 Weight inits and investigations/010 Use default inits or apply your own_.en.srt 6.34 KB
    15 Weight inits and investigations/010 Use default inits or apply your own_.mp4 28.05 MB
    16 Autoencoders/001 What are autoencoders and what do they do_.en.srt 16.93 KB
    16 Autoencoders/001 What are autoencoders and what do they do_.mp4 49.04 MB
    16 Autoencoders/002 Denoising MNIST.en.srt 22.79 KB
    16 Autoencoders/002 Denoising MNIST.mp4 118.53 MB
    16 Autoencoders/003 CodeChallenge_ How many units_.en.srt 28.86 KB
    16 Autoencoders/003 CodeChallenge_ How many units_.mp4 135.38 MB
    16 Autoencoders/004 AEs for occlusion.en.srt 25.42 KB
    16 Autoencoders/004 AEs for occlusion.mp4 138.2 MB
    16 Autoencoders/005 The latent code of MNIST.en.srt 31.66 KB
    16 Autoencoders/005 The latent code of MNIST.mp4 161.81 MB
    16 Autoencoders/006 Autoencoder with tied weights.en.srt 34.83 KB
    16 Autoencoders/006 Autoencoder with tied weights.mp4 177.74 MB
    17 Running models on a GPU/001 What is a GPU and why use it_.en.srt 22.47 KB
    17 Running models on a GPU/001 What is a GPU and why use it_.mp4 88.73 MB
    17 Running models on a GPU/002 Implementation.en.srt 14.81 KB
    17 Running models on a GPU/002 Implementation.mp4 76.6 MB
    17 Running models on a GPU/003 CodeChallenge_ Run an experiment on the GPU.en.srt 9.81 KB
    17 Running models on a GPU/003 CodeChallenge_ Run an experiment on the GPU.mp4 52.99 MB
    18 Convolution and transformations/001 Convolution_ concepts.en.srt 32.5 KB
    18 Convolution and transformations/001 Convolution_ concepts.mp4 98.06 MB
    18 Convolution and transformations/002 Feature maps and convolution kernels.en.srt 13.96 KB
    18 Convolution and transformations/002 Feature maps and convolution kernels.mp4 70.41 MB
    18 Convolution and transformations/003 Convolution in code.en.srt 30.53 KB
    18 Convolution and transformations/003 Convolution in code.mp4 173.1 MB
    18 Convolution and transformations/004 Convolution parameters (stride, padding).en.srt 18.09 KB
    18 Convolution and transformations/004 Convolution parameters (stride, padding).mp4 66.93 MB
    18 Convolution and transformations/005 The Conv2 class in PyTorch.en.srt 18.9 KB
    18 Convolution and transformations/005 The Conv2 class in PyTorch.mp4 100.19 MB
    18 Convolution and transformations/006 CodeChallenge_ Choose the parameters.en.srt 10.12 KB
    18 Convolution and transformations/006 CodeChallenge_ Choose the parameters.mp4 58.71 MB
    18 Convolution and transformations/007 Transpose convolution.en.srt 19.91 KB
    18 Convolution and transformations/007 Transpose convolution.mp4 92.89 MB
    18 Convolution and transformations/008 Max_mean pooling.en.srt 26.69 KB
    18 Convolution and transformations/008 Max_mean pooling.mp4 89.07 MB
    18 Convolution and transformations/009 Pooling in PyTorch.en.srt 19.63 KB
    18 Convolution and transformations/009 Pooling in PyTorch.mp4 81.02 MB
    18 Convolution and transformations/010 To pool or to stride_.en.srt 14.28 KB
    18 Convolution and transformations/010 To pool or to stride_.mp4 55.51 MB
    18 Convolution and transformations/011 Image transforms.en.srt 23.92 KB
    18 Convolution and transformations/011 Image transforms.mp4 129.9 MB
    18 Convolution and transformations/012 Creating and using custom DataLoaders.en.srt 26.5 KB
    18 Convolution and transformations/012 Creating and using custom DataLoaders.mp4 139.53 MB
    19 Understand and design CNNs/001 The canonical CNN architecture.en.srt 15.68 KB
    19 Understand and design CNNs/001 The canonical CNN architecture.mp4 55.83 MB
    19 Understand and design CNNs/002 CNN to classify MNIST digits.en.srt 38.02 KB
    19 Understand and design CNNs/002 CNN to classify MNIST digits.mp4 200.33 MB
    19 Understand and design CNNs/003 CNN on shifted MNIST.en.srt 12.12 KB
    19 Understand and design CNNs/003 CNN on shifted MNIST.mp4 58.34 MB
    19 Understand and design CNNs/004 Classify Gaussian blurs.en.srt 34.26 KB
    19 Understand and design CNNs/004 Classify Gaussian blurs.mp4 185.14 MB
    19 Understand and design CNNs/005 Examine feature map activations.en.srt 40.51 KB
    19 Understand and design CNNs/005 Examine feature map activations.mp4 260.56 MB
    19 Understand and design CNNs/006 CodeChallenge_ Softcode internal parameters.en.srt 25.03 KB
    19 Understand and design CNNs/006 CodeChallenge_ Softcode internal parameters.mp4 120.1 MB
    19 Understand and design CNNs/007 CodeChallenge_ How wide the FC_.en.srt 16.49 KB
    19 Understand and design CNNs/007 CodeChallenge_ How wide the FC_.mp4 94.08 MB
    19 Understand and design CNNs/008 Do autoencoders clean Gaussians_.en.srt 24.39 KB
    19 Understand and design CNNs/008 Do autoencoders clean Gaussians_.mp4 147.88 MB
    19 Understand and design CNNs/009 CodeChallenge_ AEs and occluded Gaussians.en.srt 14.03 KB
    19 Understand and design CNNs/009 CodeChallenge_ AEs and occluded Gaussians.mp4 89.45 MB
    19 Understand and design CNNs/010 CodeChallenge_ Custom loss functions.en.srt 29.89 KB
    19 Understand and design CNNs/010 CodeChallenge_ Custom loss functions.mp4 132.89 MB
    19 Understand and design CNNs/011 Discover the Gaussian parameters.en.srt 23.25 KB
    19 Understand and design CNNs/011 Discover the Gaussian parameters.mp4 136.65 MB
    19 Understand and design CNNs/012 The EMNIST dataset (letter recognition).en.srt 36.14 KB
    19 Understand and design CNNs/012 The EMNIST dataset (letter recognition).mp4 201.31 MB
    19 Understand and design CNNs/013 Dropout in CNNs.en.srt 14.17 KB
    19 Understand and design CNNs/013 Dropout in CNNs.mp4 82.73 MB
    19 Understand and design CNNs/014 CodeChallenge_ How low can you go_.en.srt 9.95 KB
    19 Understand and design CNNs/014 CodeChallenge_ How low can you go_.mp4 55.36 MB
    19 Understand and design CNNs/015 CodeChallenge_ Varying number of channels.en.srt 19.61 KB
    19 Understand and design CNNs/015 CodeChallenge_ Varying number of channels.mp4 92.37 MB
    19 Understand and design CNNs/016 So many possibilities! How to create a CNN_.en.srt 6.51 KB
    19 Understand and design CNNs/016 So many possibilities! How to create a CNN_.mp4 21.04 MB
    20 CNN milestone projects/001 Project 1_ Import and classify CIFAR10.en.srt 10.56 KB
    20 CNN milestone projects/001 Project 1_ Import and classify CIFAR10.mp4 48.36 MB
    20 CNN milestone projects/002 Project 1_ My solution.en.srt 17.15 KB
    20 CNN milestone projects/002 Project 1_ My solution.mp4 118.6 MB
    20 CNN milestone projects/003 Project 2_ CIFAR-autoencoder.en.srt 7 KB
    20 CNN milestone projects/003 Project 2_ CIFAR-autoencoder.mp4 33.37 MB
    20 CNN milestone projects/004 Project 3_ FMNIST.en.srt 5.11 KB
    20 CNN milestone projects/004 Project 3_ FMNIST.mp4 26.45 MB
    20 CNN milestone projects/005 Project 4_ Psychometric functions in CNNs.en.srt 16.88 KB
    20 CNN milestone projects/005 Project 4_ Psychometric functions in CNNs.mp4 76.27 MB
    21 Transfer learning/001 Transfer learning_ What, why, and when_.en.srt 24.76 KB
    21 Transfer learning/001 Transfer learning_ What, why, and when_.mp4 96.61 MB
    21 Transfer learning/002 Transfer learning_ MNIST -_ FMNIST.en.srt 14.58 KB
    21 Transfer learning/002 Transfer learning_ MNIST -_ FMNIST.mp4 90.35 MB
    21 Transfer learning/003 CodeChallenge_ letters to numbers.en.srt 20.54 KB
    21 Transfer learning/003 CodeChallenge_ letters to numbers.mp4 118.74 MB
    21 Transfer learning/004 Famous CNN architectures.en.srt 8.7 KB
    21 Transfer learning/004 Famous CNN architectures.mp4 41.28 MB
    21 Transfer learning/005 Transfer learning with ResNet-18.en.srt 24.59 KB
    21 Transfer learning/005 Transfer learning with ResNet-18.mp4 148.46 MB
    21 Transfer learning/006 CodeChallenge_ VGG-16.en.srt 5.05 KB
    21 Transfer learning/006 CodeChallenge_ VGG-16.mp4 20.28 MB
    21 Transfer learning/007 Pretraining with autoencoders.en.srt 28.75 KB
    21 Transfer learning/007 Pretraining with autoencoders.mp4 156.58 MB
    21 Transfer learning/008 CIFAR10 with autoencoder-pretrained model.en.srt 25.89 KB
    21 Transfer learning/008 CIFAR10 with autoencoder-pretrained model.mp4 153.34 MB
    22 Style transfer/001 What is style transfer and how does it work_.en.srt 6.35 KB
    22 Style transfer/001 What is style transfer and how does it work_.mp4 40.57 MB
    22 Style transfer/002 The Gram matrix (feature activation covariance).en.srt 16.79 KB
    22 Style transfer/002 The Gram matrix (feature activation covariance).mp4 66.49 MB
    22 Style transfer/003 The style transfer algorithm.en.srt 15.1 KB
    22 Style transfer/003 The style transfer algorithm.mp4 67.31 MB
    22 Style transfer/004 Transferring the screaming bathtub.en.srt 32.28 KB
    22 Style transfer/004 Transferring the screaming bathtub.mp4 216.82 MB
    22 Style transfer/005 CodeChallenge_ Style transfer with AlexNet.en.srt 10.47 KB
    22 Style transfer/005 CodeChallenge_ Style transfer with AlexNet.mp4 53.47 MB
    23 Generative adversarial networks/001 GAN_ What, why, and how.en.srt 23.51 KB
    23 Generative adversarial networks/001 GAN_ What, why, and how.mp4 89.74 MB
    23 Generative adversarial networks/002 Linear GAN with MNIST.en.srt 31.99 KB
    23 Generative adversarial networks/002 Linear GAN with MNIST.mp4 169.9 MB
    23 Generative adversarial networks/003 CodeChallenge_ Linear GAN with FMNIST.en.srt 13.88 KB
    23 Generative adversarial networks/003 CodeChallenge_ Linear GAN with FMNIST.mp4 62.73 MB
    23 Generative adversarial networks/004 CNN GAN with Gaussians.en.srt 22.13 KB
    23 Generative adversarial networks/004 CNN GAN with Gaussians.mp4 135.7 MB
    23 Generative adversarial networks/005 CodeChallenge_ Gaussians with fewer layers.en.srt 8.94 KB
    23 Generative adversarial networks/005 CodeChallenge_ Gaussians with fewer layers.mp4 53.06 MB
    23 Generative adversarial networks/006 CNN GAN with FMNIST.en.srt 9.23 KB
    23 Generative adversarial networks/006 CNN GAN with FMNIST.mp4 54.58 MB
    23 Generative adversarial networks/007 CodeChallenge_ CNN GAN with CIFAR.en.srt 11.65 KB
    23 Generative adversarial networks/007 CodeChallenge_ CNN GAN with CIFAR.mp4 60.77 MB
    24 Ethics of deep learning/001 Will AI save us or destroy us_.en.srt 14.36 KB
    24 Ethics of deep learning/001 Will AI save us or destroy us_.mp4 65.92 MB
    24 Ethics of deep learning/002 Example case studies.en.srt 9.15 KB
    24 Ethics of deep learning/002 Example case studies.mp4 52.92 MB
    24 Ethics of deep learning/003 Some other possible ethical scenarios.en.srt 15.2 KB
    24 Ethics of deep learning/003 Some other possible ethical scenarios.mp4 66.25 MB
    24 Ethics of deep learning/004 Will deep learning take our jobs_.en.srt 14.9 KB
    24 Ethics of deep learning/004 Will deep learning take our jobs_.mp4 75.14 MB
    24 Ethics of deep learning/005 Accountability and making ethical AI.en.srt 16.71 KB
    24 Ethics of deep learning/005 Accountability and making ethical AI.mp4 70.06 MB
    25 Where to go from here_/001 How to learn topic _X_ in deep learning_.en.srt 12.32 KB
    25 Where to go from here_/001 How to learn topic _X_ in deep learning_.mp4 42.03 MB
    25 Where to go from here_/002 How to read academic DL papers.en.srt 25.43 KB
    25 Where to go from here_/002 How to read academic DL papers.mp4 141.85 MB
    26 Bonus section/001 Bonus content.html 4.45 KB
    27 Python intro_ Data types/001 How to learn from the Python tutorial.en.srt 4.83 KB
    27 Python intro_ Data types/001 How to learn from the Python tutorial.mp4 21.97 MB
    27 Python intro_ Data types/002 Variables.en.srt 27.29 KB
    27 Python intro_ Data types/002 Variables.mp4 77.58 MB
    27 Python intro_ Data types/003 Math and printing.en.srt 26.8 KB
    27 Python intro_ Data types/003 Math and printing.mp4 78.5 MB
    27 Python intro_ Data types/004 Lists (1 of 2).en.srt 20.48 KB
    27 Python intro_ Data types/004 Lists (1 of 2).mp4 55.04 MB
    27 Python intro_ Data types/005 Lists (2 of 2).en.srt 14.58 KB
    27 Python intro_ Data types/005 Lists (2 of 2).mp4 46.69 MB
    27 Python intro_ Data types/006 Tuples.en.srt 12.04 KB
    27 Python intro_ Data types/006 Tuples.mp4 35.75 MB
    27 Python intro_ Data types/007 Booleans.en.srt 27.78 KB
    27 Python intro_ Data types/007 Booleans.mp4 76.83 MB
    27 Python intro_ Data types/008 Dictionaries.en.srt 17.06 KB
    27 Python intro_ Data types/008 Dictionaries.mp4 50.67 MB
    28 Python intro_ Indexing, slicing/001 Indexing.en.srt 18.1 KB
    28 Python intro_ Indexing, slicing/001 Indexing.mp4 51.07 MB
    28 Python intro_ Indexing, slicing/002 Slicing.en.srt 17.97 KB
    28 Python intro_ Indexing, slicing/002 Slicing.mp4 48.45 MB
    29 Python intro_ Functions/001 Inputs and outputs.en.srt 10.56 KB
    29 Python intro_ Functions/001 Inputs and outputs.mp4 29.49 MB
    29 Python intro_ Functions/002 Python libraries (numpy).en.srt 20.01 KB
    29 Python intro_ Functions/002 Python libraries (numpy).mp4 63.39 MB
    29 Python intro_ Functions/003 Python libraries (pandas).en.srt 20.3 KB
    29 Python intro_ Functions/003 Python libraries (pandas).mp4 81.19 MB
    29 Python intro_ Functions/004 Getting help on functions.en.srt 11.05 KB
    29 Python intro_ Functions/004 Getting help on functions.mp4 48.6 MB
    29 Python intro_ Functions/005 Creating functions.en.srt 30.89 KB
    29 Python intro_ Functions/005 Creating functions.mp4 88.43 MB
    29 Python intro_ Functions/006 Global and local variable scopes.en.srt 19.67 KB
    29 Python intro_ Functions/006 Global and local variable scopes.mp4 65.96 MB
    29 Python intro_ Functions/007 Copies and referents of variables.en.srt 7.24 KB
    29 Python intro_ Functions/007 Copies and referents of variables.mp4 23.78 MB
    29 Python intro_ Functions/008 Classes and object-oriented programming.en.srt 26.6 KB
    29 Python intro_ Functions/008 Classes and object-oriented programming.mp4 108.18 MB
    30 Python intro_ Flow control/001 If-else statements.en.srt 21.71 KB
    30 Python intro_ Flow control/001 If-else statements.mp4 66.8 MB
    30 Python intro_ Flow control/002 If-else statements, part 2.en.srt 22.9 KB
    30 Python intro_ Flow control/002 If-else statements, part 2.mp4 91.12 MB
    30 Python intro_ Flow control/003 For loops.en.srt 25.24 KB
    30 Python intro_ Flow control/003 For loops.mp4 87.13 MB
    30 Python intro_ Flow control/004 Enumerate and zip.en.srt 16.03 KB
    30 Python intro_ Flow control/004 Enumerate and zip.mp4 58.59 MB
    30 Python intro_ Flow control/005 Continue.en.srt 10.09 KB
    30 Python intro_ Flow control/005 Continue.mp4 33.03 MB
    30 Python intro_ Flow control/006 Initializing variables.en.srt 25.61 KB
    30 Python intro_ Flow control/006 Initializing variables.mp4 91.05 MB
    30 Python intro_ Flow control/007 Single-line loops (list comprehension).en.srt 21.74 KB
    30 Python intro_ Flow control/007 Single-line loops (list comprehension).mp4 75.14 MB
    30 Python intro_ Flow control/008 while loops.en.srt 27.94 KB
    30 Python intro_ Flow control/008 while loops.mp4 91.1 MB
    30 Python intro_ Flow control/009 Broadcasting in numpy.en.srt 21.34 KB
    30 Python intro_ Flow control/009 Broadcasting in numpy.mp4 71.05 MB
    30 Python intro_ Flow control/010 Function error checking and handling.en.srt 25.36 KB
    30 Python intro_ Flow control/010 Function error checking and handling.mp4 99.87 MB
    31 Python intro_ Text and plots/001 Printing and string interpolation.en.srt 24.33 KB
    31 Python intro_ Text and plots/001 Printing and string interpolation.mp4 94.83 MB
    31 Python intro_ Text and plots/002 Plotting dots and lines.en.srt 17.7 KB
    31 Python intro_ Text and plots/002 Plotting dots and lines.mp4 53.87 MB
    31 Python intro_ Text and plots/003 Subplot geometry.en.srt 23.14 KB
    31 Python intro_ Text and plots/003 Subplot geometry.mp4 86.78 MB
    31 Python intro_ Text and plots/004 Making the graphs look nicer.en.srt 26.98 KB
    31 Python intro_ Text and plots/004 Making the graphs look nicer.mp4 107.66 MB
    31 Python intro_ Text and plots/005 Seaborn.en.srt 15.73 KB
    31 Python intro_ Text and plots/005 Seaborn.mp4 59.72 MB
    31 Python intro_ Text and plots/006 Images.en.srt 25.75 KB
    31 Python intro_ Text and plots/006 Images.mp4 93.56 MB
    31 Python intro_ Text and plots/007 Export plots in low and high resolution.en.srt 11.37 KB
    31 Python intro_ Text and plots/007 Export plots in low and high resolution.mp4 43.57 MB

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