[GigaCourse.Com] Udemy - A deep understanding of deep learning (with Python intro)

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[GigaCourse.Com] Udemy - A deep understanding of deep learning (with Python intro)

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文件列表

  • 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
  • 07 ANNs/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/009 Learning rates comparison.mp4 176.8 MB
  • 12 More on data/003 CodeChallenge_ unbalanced data.mp4 174.3 MB
  • 11 FFNs/003 FFN to classify digits.mp4 169.7 MB
  • 16 Autoencoders/005 The latent code of MNIST.mp4 169.7 MB
  • 07 ANNs/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/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/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
  • 25 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/007 CodeChallenge_ manipulate regression slopes.mp4 145.9 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
  • 09 Regularization/003 Dropout regularization.mp4 142.6 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/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/016 Depth vs. breadth_ number of parameters.mp4 138.5 MB
  • 18 Convolution and transformations/011 Image transforms.mp4 136.2 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
  • 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
  • 03 Concepts in deep learning/003 The role of DL in science and knowledge.mp4 127.5 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/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 120.1 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/010 Entropy and cross-entropy.mp4 111.1 MB
  • 15 Weight inits and investigations/004 CodeChallenge_ Weight variance inits.mp4 109.0 MB
  • 11 FFNs/002 The MNIST dataset.mp4 106.4 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 102.8 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
  • 11 FFNs/005 CodeChallenge_ Data normalization.mp4 100.9 MB
  • 05 Math, numpy, PyTorch/008 Softmax.mp4 100.6 MB
  • 06 Gradient descent/005 Gradient descent in 2D.mp4 100.6 MB
  • 09 Regularization/012 CodeChallenge_ Effects of mini-batch size.mp4 100.1 MB
  • 11 FFNs/007 CodeChallenge_ MNIST and breadth vs. depth.mp4 99.8 MB
  • 07 ANNs/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/017 Defining models using sequential vs. class.mp4 93.8 MB
  • 19 Understand and design CNNs/009 CodeChallenge_ AEs and occluded Gaussians.mp4 93.8 MB
  • 09 Regularization/010 Batch training in action.mp4 93.4 MB
  • 18 Convolution and transformations/008 Max_mean 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/011 Min_max and argmin_argmax.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
  • 05 Math, numpy, PyTorch/007 Matrix multiplication.mp4 89.8 MB
  • 05 Math, numpy, PyTorch/013 Random sampling and sampling variability.mp4 89.6 MB
  • 09 Regularization/006 Weight regularization (L1_L2)_ math.mp4 89.6 MB
  • 07 ANNs/001 The perceptron and ANN architecture.mp4 87.7 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/015 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/012 Mean and variance.mp4 84.5 MB
  • 05 Math, numpy, PyTorch/016 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/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
  • 24 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
  • 03 Concepts in deep learning/004 Running experiments to understand DL.mp4 78.5 MB
  • 11 FFNs/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
  • 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/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/002 A geometric view of ANNs.mp4 74.3 MB
  • 18 Convolution and transformations/002 Feature maps and convolution kernels.mp4 73.8 MB
  • 24 Ethics of deep learning/005 Accountability and making ethical AI.mp4 73.5 MB
  • 05 Math, numpy, PyTorch/014 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
  • 24 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
  • 24 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
  • 13 Measuring model performance/006 CodeChallenge_ MNIST with unequal groups.mp4 65.4 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/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/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
  • 07 ANNs/003 ANN math part 1 (forward prop).mp4 60.7 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/018 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
  • 24 Ethics of deep learning/002 Example case studies.mp4 55.5 MB
  • 07 ANNs/005 ANN math part 3 (backprop).mp4 55.5 MB
  • 13 Measuring model performance/003 APRF in code.mp4 54.3 MB
  • 07 ANNs/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/002 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/011 Linear solutions to linear problems.mp4 52.8 MB
  • 05 Math, numpy, PyTorch/006 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/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/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/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/017 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/009 Logarithms.mp4 46.0 MB
  • 12 More on data/008 Getting data into colab.mp4 45.9 MB
  • 31 Python intro_ Text and plots/007 Export plots in low and high resolution.mp4 45.7 MB
  • 25 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/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/003 Terms and datatypes in math and computers.mp4 39.9 MB
  • 05 Math, numpy, PyTorch/005 Vector and matrix transpose.mp4 39.5 MB
  • 27 Python intro_ Data types/006 Tuples.mp4 37.5 MB
  • 20 CNN milestone projects/003 Project 2_ CIFAR-autoencoder.mp4 35.0 MB
  • 05 Math, numpy, PyTorch/004 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
  • 15 Weight inits and investigations/010 Use default inits or apply your own_.mp4 29.4 MB
  • 07 ANNs/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/001 What are fully-connected and feedforward networks_.mp4 26.8 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
  • 06 Gradient descent/010 Tangent_ Notebook revision history.mp4 23.3 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
  • 05 Math, numpy, PyTorch/001 Introduction to this section.mp4 11.7 MB
  • 02 Download all course materials/002 My policy on code-sharing.mp4 10.7 MB
  • 02 Download all course materials/003 DUDL_PythonCode.zip 717.6 kB
  • 19 Understand and design CNNs/005 Examine feature map activations.en.srt 41.5 kB
  • 19 Understand and design CNNs/002 CNN to classify MNIST digits.en.srt 38.9 kB
  • 07 ANNs/013 Multi-output ANN (iris dataset).en.srt 38.4 kB
  • 07 ANNs/009 Learning rates comparison.en.srt 37.1 kB
  • 19 Understand and design CNNs/012 The EMNIST dataset (letter recognition).en.srt 37.0 kB
  • 07 ANNs/006 ANN for regression.en.srt 36.7 kB
  • 16 Autoencoders/006 Autoencoder with tied weights.en.srt 35.7 kB
  • 19 Understand and design CNNs/004 Classify Gaussian blurs.en.srt 35.1 kB
  • 07 ANNs/008 ANN for classifying qwerties.en.srt 34.8 kB
  • 09 Regularization/004 Dropout regularization in practice.en.srt 34.2 kB
  • 11 FFNs/003 FFN to classify digits.en.srt 33.7 kB
  • 15 Weight inits and investigations/009 Learning-related changes in weights.en.srt 33.6 kB
  • 18 Convolution and transformations/001 Convolution_ concepts.en.srt 33.3 kB
  • 22 Style transfer/004 Transferring the screaming bathtub.en.srt 33.1 kB
  • 23 Generative adversarial networks/002 Linear GAN with MNIST.en.srt 32.8 kB
  • 16 Autoencoders/005 The latent code of MNIST.en.srt 32.4 kB
  • 09 Regularization/003 Dropout regularization.en.srt 31.9 kB
  • 07 ANNs/018 Model depth vs. breadth.en.srt 31.6 kB
  • 29 Python intro_ Functions/005 Creating functions.en.srt 31.6 kB
  • 18 Convolution and transformations/003 Convolution in code.en.srt 31.3 kB
  • 08 Overfitting and cross-validation/005 Cross-validation -- scikitlearn.en.srt 31.2 kB
  • 19 Understand and design CNNs/010 CodeChallenge_ Custom loss functions.en.srt 30.6 kB
  • 07 ANNs/010 Multilayer ANN.en.srt 30.1 kB
  • 12 More on data/003 CodeChallenge_ unbalanced data.en.srt 30.0 kB
  • 16 Autoencoders/003 CodeChallenge_ How many units_.en.srt 29.6 kB
  • 21 Transfer learning/007 Pretraining with autoencoders.en.srt 29.4 kB
  • 08 Overfitting and cross-validation/006 Cross-validation -- DataLoader.en.srt 29.3 kB
  • 12 More on data/007 Data feature augmentation.en.srt 29.0 kB
  • 07 ANNs/007 CodeChallenge_ manipulate regression slopes.en.srt 29.0 kB
  • 30 Python intro_ Flow control/008 while loops.en.srt 28.6 kB
  • 14 FFN milestone projects/004 Project 2_ My solution.en.srt 28.4 kB
  • 27 Python intro_ Data types/007 Booleans.en.srt 28.4 kB
  • 05 Math, numpy, PyTorch/008 Softmax.en.srt 28.4 kB
  • 27 Python intro_ Data types/002 Variables.en.srt 27.9 kB
  • 06 Gradient descent/007 Parametric experiments on g.d.en.srt 27.8 kB
  • 09 Regularization/006 Weight regularization (L1_L2)_ math.en.srt 27.7 kB
  • 31 Python intro_ Text and plots/004 Making the graphs look nicer.en.srt 27.6 kB
  • 10 Metaparameters (activations, optimizers)/015 Loss functions in PyTorch.en.srt 27.5 kB
  • 07 ANNs/001 The perceptron and ANN architecture.en.srt 27.4 kB
  • 27 Python intro_ Data types/003 Math and printing.en.srt 27.4 kB
  • 18 Convolution and transformations/008 Max_mean pooling.en.srt 27.3 kB
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