Udemy - A deep understanding of deep learning (with Python intro)

磁力链接/BT种子名称

Udemy - A deep understanding of deep learning (with Python intro)

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收录时间：2023-12-18
最近下载：2024-10-04

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文件列表

  • 19 - Understand and design CNNs/177 - Examine feature map activations.mp4 432.2 MB
  • 22 - Style transfer/205 - Transferring the screaming bathtub.mp4 361.5 MB
  • 19 - Understand and design CNNs/176 - Classify Gaussian blurs.mp4 293.3 MB
  • 24 - RNNs Recurrent Neural Networks and GRULSTM/218 - CodeChallenge sine wave extrapolation.mp4 272.5 MB
  • 18 - Convolution and transformations/163 - Convolution in code.mp4 270.8 MB
  • 24 - RNNs Recurrent Neural Networks and GRULSTM/217 - Predicting alternating sequences.mp4 259.1 MB
  • 26 - Where to go from here/229 - How to read academic DL papers.mp4 232.8 MB
  • 19 - Understand and design CNNs/184 - The EMNIST dataset letter recognition.mp4 230.7 MB
  • 19 - Understand and design CNNs/174 - CNN to classify MNIST digits.mp4 228.4 MB
  • 24 - RNNs Recurrent Neural Networks and GRULSTM/222 - Lorem ipsum.mp4 226.2 MB
  • 7 - ANNs Artificial Neural Networks/52 - Multioutput ANN iris dataset.mp4 225.5 MB
  • 23 - Generative adversarial networks/210 - CNN GAN with Gaussians.mp4 224.6 MB
  • 21 - Transfer learning/200 - Pretraining with autoencoders.mp4 218.8 MB
  • 19 - Understand and design CNNs/180 - Do autoencoders clean Gaussians.mp4 216.1 MB
  • 9 - Regularization/72 - Dropout regularization in practice.mp4 211.2 MB
  • 21 - Transfer learning/198 - Transfer learning with ResNet18.mp4 210.9 MB
  • 16 - Autoencoders/157 - Autoencoder with tied weights.mp4 210.4 MB
  • 7 - ANNs Artificial Neural Networks/47 - ANN for classifying qwerties.mp4 205.8 MB
  • 10 - Metaparameters activations optimizers/82 - The wine quality dataset.mp4 203.8 MB
  • 18 - Convolution and transformations/171 - Image transforms.mp4 202.3 MB
  • 8 - Overfitting and crossvalidation/66 - Crossvalidation DataLoader.mp4 197.7 MB
  • 23 - Generative adversarial networks/208 - Linear GAN with MNIST.mp4 197.3 MB
  • 12 - More on data/119 - CodeChallenge unbalanced data.mp4 192.3 MB
  • 16 - Autoencoders/156 - The latent code of MNIST.mp4 190.9 MB
  • 11 - FFNs FeedForward Networks/107 - FFN to classify digits.mp4 187.0 MB
  • 7 - ANNs Artificial Neural Networks/57 - Model depth vs breadth.mp4 186.0 MB
  • 12 - More on data/123 - Data feature augmentation.mp4 184.8 MB
  • 19 - Understand and design CNNs/178 - CodeChallenge Softcode internal parameters.mp4 184.6 MB
  • 15 - Weight inits and investigations/147 - CodeChallenge Xavier vs Kaiming.mp4 177.3 MB
  • 7 - ANNs Artificial Neural Networks/48 - Learning rates comparison.mp4 176.8 MB
  • 21 - Transfer learning/201 - CIFAR10 with autoencoderpretrained model.mp4 175.0 MB
  • 13 - Measuring model performance/131 - APRF example 1 wine quality.mp4 170.6 MB
  • 15 - Weight inits and investigations/150 - Learningrelated changes in weights.mp4 169.4 MB
  • 10 - Metaparameters activations optimizers/83 - CodeChallenge Minibatch size in the wine dataset.mp4 168.2 MB
  • 7 - ANNs Artificial Neural Networks/49 - Multilayer ANN.mp4 168.0 MB
  • 8 - Overfitting and crossvalidation/65 - Crossvalidation scikitlearn.mp4 167.3 MB
  • 14 - FFN milestone projects/139 - Project 2 My solution.mp4 163.3 MB
  • 19 - Understand and design CNNs/182 - CodeChallenge Custom loss functions.mp4 162.4 MB
  • 10 - Metaparameters activations optimizers/95 - Loss functions in PyTorch.mp4 162.2 MB
  • 18 - Convolution and transformations/172 - Creating and using custom DataLoaders.mp4 161.6 MB
  • 9 - Regularization/71 - Dropout regularization.mp4 159.3 MB
  • 12 - More on data/117 - Anatomy of a torch dataset and dataloader.mp4 159.2 MB
  • 6 - Gradient descent/36 - Parametric experiments on gd.mp4 158.8 MB
  • 13 - Measuring model performance/132 - APRF example 2 MNIST.mp4 157.6 MB
  • 7 - ANNs Artificial Neural Networks/46 - CodeChallenge manipulate regression slopes.mp4 157.5 MB
  • 12 - More on data/118 - Data size and network size.mp4 156.8 MB
  • 7 - ANNs Artificial Neural Networks/55 - Depth vs breadth number of parameters.mp4 156.3 MB
  • 15 - Weight inits and investigations/146 - Xavier and Kaiming initializations.mp4 156.1 MB
  • 16 - Autoencoders/154 - CodeChallenge How many units.mp4 155.6 MB
  • 19 - Understand and design CNNs/179 - CodeChallenge How wide the FC.mp4 151.7 MB
  • 11 - FFNs FeedForward Networks/110 - Distributions of weights pre and postlearning.mp4 148.7 MB
  • 11 - FFNs FeedForward Networks/111 - CodeChallenge MNIST and breadth vs depth.mp4 147.2 MB
  • 12 - More on data/126 - Save the bestperforming model.mp4 146.7 MB
  • 16 - Autoencoders/155 - AEs for occlusion.mp4 144.9 MB
  • 15 - Weight inits and investigations/149 - Freezing weights during learning.mp4 144.5 MB
  • 24 - RNNs Recurrent Neural Networks and GRULSTM/220 - GRU and LSTM.mp4 144.2 MB
  • 19 - Understand and design CNNs/183 - Discover the Gaussian parameters.mp4 143.3 MB
  • 12 - More on data/121 - Data oversampling in MNIST.mp4 142.7 MB
  • 11 - FFNs FeedForward Networks/106 - The MNIST dataset.mp4 142.3 MB
  • 24 - RNNs Recurrent Neural Networks and GRULSTM/216 - The RNN class in PyTorch.mp4 141.2 MB
  • 10 - Metaparameters activations optimizers/93 - CodeChallenge Predict sugar.mp4 140.8 MB
  • 16 - Autoencoders/153 - Denoising MNIST.mp4 140.7 MB
  • 15 - Weight inits and investigations/143 - A surprising demo of weight initializations.mp4 139.2 MB
  • 24 - RNNs Recurrent Neural Networks and GRULSTM/221 - The LSTM and GRU classes.mp4 138.9 MB
  • 21 - Transfer learning/196 - CodeChallenge letters to numbers.mp4 138.3 MB
  • 6 - Gradient descent/32 - Gradient descent in 1D.mp4 137.8 MB
  • 9 - Regularization/80 - CodeChallenge Effects of minibatch size.mp4 136.7 MB
  • 20 - CNN milestone projects/190 - Project 1 My solution.mp4 136.3 MB
  • 19 - Understand and design CNNs/181 - CodeChallenge AEs and occluded Gaussians.mp4 134.3 MB
  • 7 - ANNs Artificial Neural Networks/53 - CodeChallenge more qwerties.mp4 133.3 MB
  • 3 - Concepts in deep learning/7 - The role of DL in science and knowledge.mp4 133.3 MB
  • 6 - Gradient descent/37 - CodeChallenge fixed vs dynamic learning rate.mp4 132.3 MB
  • 18 - Convolution and transformations/161 - Convolution concepts.mp4 132.2 MB
  • 9 - Regularization/75 - L2 regularization in practice.mp4 129.3 MB
  • 21 - Transfer learning/195 - Transfer learning MNIST FMNIST.mp4 127.2 MB
  • 10 - Metaparameters activations optimizers/89 - Activation functions.mp4 126.9 MB
  • 9 - Regularization/78 - Batch training in action.mp4 126.1 MB
  • 12 - More on data/122 - Data noise augmentation with devsettest.mp4 123.4 MB
  • 18 - Convolution and transformations/165 - The Conv2 class in PyTorch.mp4 118.9 MB
  • 15 - Weight inits and investigations/145 - CodeChallenge Weight variance inits.mp4 118.3 MB
  • 10 - Metaparameters activations optimizers/91 - Activation functions comparison.mp4 118.2 MB
  • 30 - Python intro Flow control/257 - Function error checking and handling.mp4 117.1 MB
  • 13 - Measuring model performance/134 - Computation time.mp4 115.8 MB
  • 9 - Regularization/76 - L1 regularization in practice.mp4 115.4 MB
  • 10 - Metaparameters activations optimizers/96 - More practice with multioutput ANNs.mp4 115.3 MB
  • 14 - FFN milestone projects/137 - Project 1 My solution.mp4 114.9 MB
  • 8 - Overfitting and crossvalidation/64 - Crossvalidation manual separation.mp4 114.0 MB
  • 15 - Weight inits and investigations/144 - Theory Why and how to initialize weights.mp4 113.2 MB
  • 10 - Metaparameters activations optimizers/103 - Learning rate decay.mp4 112.4 MB
  • 7 - ANNs Artificial Neural Networks/45 - ANN for regression.mp4 110.4 MB
  • 19 - Understand and design CNNs/185 - Dropout in CNNs.mp4 109.3 MB
  • 11 - FFNs FeedForward Networks/109 - CodeChallenge Data normalization.mp4 109.2 MB
  • 31 - Python intro Text and plots/263 - Images.mp4 109.2 MB
  • 18 - Convolution and transformations/167 - Transpose convolution.mp4 107.8 MB
  • 5 - Math numpy PyTorch/19 - Softmax.mp4 106.3 MB
  • 10 - Metaparameters activations optimizers/90 - Activation functions in PyTorch.mp4 106.2 MB
  • 19 - Understand and design CNNs/187 - CodeChallenge Varying number of channels.mp4 104.7 MB
  • 7 - ANNs Artificial Neural Networks/56 - Defining models using sequential vs class.mp4 102.6 MB
  • 10 - Metaparameters activations optimizers/100 - Optimizers comparison.mp4 102.0 MB
  • 6 - Gradient descent/34 - Gradient descent in 2D.mp4 101.1 MB
  • 10 - Metaparameters activations optimizers/94 - Loss functions.mp4 100.9 MB
  • 15 - Weight inits and investigations/148 - CodeChallenge Identically random weights.mp4 100.8 MB
  • 24 - RNNs Recurrent Neural Networks and GRULSTM/219 - More on RNNs Hidden states embeddings.mp4 98.8 MB
  • 24 - RNNs Recurrent Neural Networks and GRULSTM/214 - Leveraging sequences in deep learning.mp4 96.1 MB
  • 13 - Measuring model performance/133 - CodeChallenge MNIST with unequal groups.mp4 95.9 MB
  • 29 - Python intro Functions/242 - Python libraries pandas.mp4 95.7 MB
  • 13 - Measuring model performance/129 - Accuracy precision recall F1.mp4 95.1 MB
  • 15 - Weight inits and investigations/142 - Explanation of weight matrix sizes.mp4 93.9 MB
  • 9 - Regularization/69 - Regularization Concept and methods.mp4 92.9 MB
  • 29 - Python intro Functions/247 - Classes and objectoriented programming.mp4 92.8 MB
  • 25 - Ethics of deep learning/227 - Accountability and making ethical AI.mp4 92.7 MB
  • 5 - Math numpy PyTorch/26 - The ttest.mp4 92.5 MB
  • 31 - Python intro Text and plots/261 - Making the graphs look nicer.mp4 91.3 MB
  • 8 - Overfitting and crossvalidation/67 - Splitting data into train devset test.mp4 91.2 MB
  • 23 - Generative adversarial networks/209 - CodeChallenge Linear GAN with FMNIST.mp4 90.7 MB
  • 11 - FFNs FeedForward Networks/114 - Shifted MNIST.mp4 90.6 MB
  • 6 - Gradient descent/33 - CodeChallenge unfortunate starting value.mp4 90.1 MB
  • 5 - Math numpy PyTorch/21 - Entropy and crossentropy.mp4 89.5 MB
  • 3 - Concepts in deep learning/9 - Are artificial neurons like biological neurons.mp4 89.3 MB
  • 25 - Ethics of deep learning/225 - Some other possible ethical scenarios.mp4 88.4 MB
  • 23 - Generative adversarial networks/211 - CodeChallenge Gaussians with fewer layers.mp4 88.4 MB
  • 14 - FFN milestone projects/141 - Project 3 My solution.mp4 87.6 MB
  • 18 - Convolution and transformations/162 - Feature maps and convolution kernels.mp4 87.3 MB
  • 9 - Regularization/79 - The importance of equal batch sizes.mp4 86.1 MB
  • 22 - Style transfer/206 - CodeChallenge Style transfer with AlexNet.mp4 85.4 MB
  • 11 - FFNs FeedForward Networks/115 - CodeChallenge The mystery of the missing 7.mp4 84.8 MB
  • 8 - Overfitting and crossvalidation/61 - What is overfitting and is it as bad as they say.mp4 80.7 MB
  • 7 - ANNs Artificial Neural Networks/60 - Reflection Are DL models understandable yet.mp4 80.5 MB
  • 20 - CNN milestone projects/193 - Project 4 Psychometric functions in CNNs.mp4 80.2 MB
  • 30 - Python intro Flow control/249 - Ifelse statements part 2.mp4 80.0 MB
  • 5 - Math numpy PyTorch/25 - Reproducible randomness via seeding.mp4 79.2 MB
  • 3 - Concepts in deep learning/8 - Running experiments to understand DL.mp4 78.5 MB
  • 23 - Generative adversarial networks/212 - CNN GAN with FMNIST.mp4 78.3 MB
  • 10 - Metaparameters activations optimizers/85 - The importance of data normalization.mp4 76.3 MB
  • 18 - Convolution and transformations/170 - To pool or to stride.mp4 74.2 MB
  • 31 - Python intro Text and plots/260 - Subplot geometry.mp4 73.8 MB
  • 10 - Metaparameters activations optimizers/87 - Batch normalization in practice.mp4 73.4 MB
  • 23 - Generative adversarial networks/213 - CodeChallenge CNN GAN with CIFAR.mp4 72.9 MB
  • 18 - Convolution and transformations/168 - Maxmean pooling.mp4 72.7 MB
  • 17 - Running models on a GPU/158 - What is a GPU and why use it.mp4 72.6 MB
  • 31 - Python intro Text and plots/258 - Printing and string interpolation.mp4 72.4 MB
  • 5 - Math numpy PyTorch/22 - Minmax and argminargmax.mp4 72.3 MB
  • 30 - Python intro Flow control/255 - while loops.mp4 71.7 MB
  • 8 - Overfitting and crossvalidation/62 - Crossvalidation.mp4 71.6 MB
  • 30 - Python intro Flow control/253 - Initializing variables.mp4 70.7 MB
  • 5 - Math numpy PyTorch/18 - Matrix multiplication.mp4 70.0 MB
  • 22 - Style transfer/203 - The Gram matrix feature activation covariance.mp4 69.7 MB
  • 9 - Regularization/74 - Weight regularization L1L2 math.mp4 68.6 MB
  • 10 - Metaparameters activations optimizers/88 - CodeChallenge Batchnormalize the qwerties.mp4 68.0 MB
  • 27 - Python intro Data types/236 - Booleans.mp4 67.7 MB
  • 5 - Math numpy PyTorch/13 - Spectral theories in mathematics.mp4 67.6 MB
  • 30 - Python intro Flow control/250 - For loops.mp4 67.5 MB
  • 18 - Convolution and transformations/169 - Pooling in PyTorch.mp4 67.4 MB
  • 10 - Metaparameters activations optimizers/92 - CodeChallenge Compare relu variants.mp4 67.1 MB
  • 19 - Understand and design CNNs/175 - CNN on shifted MNIST.mp4 66.6 MB
  • 5 - Math numpy PyTorch/24 - Random sampling and sampling variability.mp4 66.3 MB
  • 10 - Metaparameters activations optimizers/84 - Data normalization.mp4 65.5 MB
  • 25 - Ethics of deep learning/224 - Example case studies.mp4 65.4 MB
  • 10 - Metaparameters activations optimizers/98 - SGD with momentum.mp4 65.1 MB
  • 30 - Python intro Flow control/254 - Singleline loops list comprehension.mp4 64.9 MB
  • 12 - More on data/125 - Save and load trained models.mp4 64.6 MB
  • 10 - Metaparameters activations optimizers/102 - CodeChallenge Adam with L2 regularization.mp4 64.1 MB
  • 13 - Measuring model performance/130 - APRF in code.mp4 64.0 MB
  • 9 - Regularization/73 - Dropout example 2.mp4 63.7 MB
  • 17 - Running models on a GPU/159 - Implementation.mp4 63.6 MB
  • 19 - Understand and design CNNs/186 - CodeChallenge How low can you go.mp4 63.2 MB
  • 11 - FFNs FeedForward Networks/113 - Scrambled MNIST.mp4 63.1 MB
  • 10 - Metaparameters activations optimizers/97 - Optimizers minibatch momentum.mp4 62.3 MB
  • 17 - Running models on a GPU/160 - CodeChallenge Run an experiment on the GPU.mp4 62.0 MB
  • 30 - Python intro Flow control/251 - Enumerate and zip.mp4 61.4 MB
  • 21 - Transfer learning/194 - Transfer learning What why and when.mp4 61.0 MB
  • 27 - Python intro Data types/231 - Variables.mp4 60.6 MB
  • 23 - Generative adversarial networks/207 - GAN What why and how.mp4 60.3 MB
  • 29 - Python intro Functions/244 - Creating functions.mp4 60.1 MB
  • 7 - ANNs Artificial Neural Networks/58 - CodeChallenge convert sequential to class.mp4 59.9 MB
  • 31 - Python intro Text and plots/264 - Export plots in low and high resolution.mp4 59.0 MB
  • 1 - Introduction/1 - How to learn from this course.mp4 57.6 MB
  • 29 - Python intro Functions/245 - Global and local variable scopes.mp4 57.5 MB
  • 7 - ANNs Artificial Neural Networks/50 - Linear solutions to linear problems.mp4 57.2 MB
  • 10 - Metaparameters activations optimizers/86 - Batch normalization.mp4 57.1 MB
  • 10 - Metaparameters activations optimizers/101 - CodeChallenge Optimizers and something.mp4 57.0 MB
  • 6 - Gradient descent/30 - Overview of gradient descent.mp4 57.0 MB
  • 20 - CNN milestone projects/189 - Project 1 Import and classify CIFAR10.mp4 55.8 MB
  • 25 - Ethics of deep learning/226 - Will deep learning take our jobs.mp4 55.6 MB
  • 2 - Download all course materials/3 - Downloading and using the code.mp4 55.1 MB
  • 10 - Metaparameters activations optimizers/99 - Optimizers RMSprop Adam.mp4 55.1 MB
  • 30 - Python intro Flow control/256 - Broadcasting in numpy.mp4 55.0 MB
  • 7 - ANNs Artificial Neural Networks/43 - ANN math part 2 errors loss cost.mp4 54.7 MB
  • 27 - Python intro Data types/232 - Math and printing.mp4 53.6 MB
  • 3 - Concepts in deep learning/6 - How models learn.mp4 53.6 MB
  • 31 - Python intro Text and plots/262 - Seaborn.mp4 53.4 MB
  • 7 - ANNs Artificial Neural Networks/40 - The perceptron and ANN architecture.mp4 53.2 MB
  • 11 - FFNs FeedForward Networks/112 - CodeChallenge Optimizers and MNIST.mp4 53.2 MB
  • 7 - ANNs Artificial Neural Networks/54 - Comparing the number of hidden units.mp4 51.8 MB
  • 12 - More on data/124 - Getting data into colab.mp4 51.1 MB
  • 5 - Math numpy PyTorch/23 - Mean and variance.mp4 49.3 MB
  • 5 - Math numpy PyTorch/27 - Derivatives intuition and polynomials.mp4 48.5 MB
  • 12 - More on data/127 - Where to find online datasets.mp4 48.3 MB
  • 24 - RNNs Recurrent Neural Networks and GRULSTM/215 - How RNNs work.mp4 47.9 MB
  • 20 - CNN milestone projects/191 - Project 2 CIFARautoencoder.mp4 47.4 MB
  • 11 - FFNs FeedForward Networks/108 - CodeChallenge Binarized MNIST images.mp4 46.7 MB
  • 7 - ANNs Artificial Neural Networks/42 - ANN math part 1 forward prop.mp4 46.1 MB
  • 6 - Gradient descent/35 - CodeChallenge 2D gradient ascent.mp4 45.5 MB
  • 30 - Python intro Flow control/248 - Ifelse statements.mp4 45.3 MB
  • 7 - ANNs Artificial Neural Networks/41 - A geometric view of ANNs.mp4 45.0 MB
  • 8 - Overfitting and crossvalidation/68 - Crossvalidation on regression.mp4 43.1 MB
  • 22 - Style transfer/204 - The style transfer algorithm.mp4 42.7 MB
  • 3 - Concepts in deep learning/5 - What is an artificial neural network.mp4 42.0 MB
  • 31 - Python intro Text and plots/259 - Plotting dots and lines.mp4 42.0 MB
  • 28 - Python intro Indexing slicing/239 - Slicing.mp4 41.9 MB
  • 29 - Python intro Functions/241 - Python libraries numpy.mp4 41.8 MB
  • 18 - Convolution and transformations/164 - Convolution parameters stride padding.mp4 40.8 MB
  • 6 - Gradient descent/31 - What about local minima.mp4 39.9 MB
  • 1 - Introduction/2 - Using Udemy like a pro.mp4 39.9 MB
  • 7 - ANNs Artificial Neural Networks/44 - ANN math part 3 backprop.mp4 39.4 MB
  • 14 - FFN milestone projects/136 - Project 1 A gratuitously complex adding machine.mp4 39.2 MB
  • 29 - Python intro Functions/243 - Getting help on functions.mp4 39.1 MB
  • 25 - Ethics of deep learning/223 - Will AI save us or destroy us.mp4 39.0 MB
  • 5 - Math numpy PyTorch/29 - Derivatives product and chain rules.mp4 38.8 MB
  • 10 - Metaparameters activations optimizers/104 - How to pick the right metaparameters.mp4 38.6 MB
  • 14 - FFN milestone projects/138 - Project 2 Predicting heart disease.mp4 37.3 MB
  • 9 - Regularization/77 - Training in minibatches.mp4 37.1 MB
  • 27 - Python intro Data types/233 - Lists 1 of 2.mp4 36.8 MB
  • 11 - FFNs FeedForward Networks/116 - Universal approximation theorem.mp4 35.9 MB
  • 27 - Python intro Data types/234 - Lists 2 of 2.mp4 35.1 MB
  • 19 - Understand and design CNNs/173 - The canonical CNN architecture.mp4 34.8 MB
  • 27 - Python intro Data types/237 - Dictionaries.mp4 34.6 MB
  • 21 - Transfer learning/197 - Famous CNN architectures.mp4 34.6 MB
  • 28 - Python intro Indexing slicing/238 - Indexing.mp4 34.5 MB
  • 6 - Gradient descent/38 - Vanishing and exploding gradients.mp4 33.2 MB
  • 18 - Convolution and transformations/166 - CodeChallenge Choose the parameters.mp4 32.5 MB
  • 12 - More on data/120 - What to do about unbalanced designs.mp4 31.2 MB
  • 16 - Autoencoders/152 - What are autoencoders and what do they do.mp4 30.8 MB
  • 5 - Math numpy PyTorch/20 - Logarithms.mp4 30.6 MB
  • 20 - CNN milestone projects/192 - Project 3 FMNIST.mp4 30.2 MB
  • 5 - Math numpy PyTorch/17 - OMG its the dot product.mp4 30.1 MB
  • 22 - Style transfer/202 - What is style transfer and how does it work.mp4 29.5 MB
  • 14 - FFN milestone projects/140 - Project 3 FFN for missing data interpolation.mp4 28.7 MB
  • 7 - ANNs Artificial Neural Networks/51 - Why multilayer linear models dont exist.mp4 28.5 MB
  • 13 - Measuring model performance/128 - Two perspectives of the world.mp4 28.1 MB
  • 13 - Measuring model performance/135 - Better performance in test than train.mp4 27.5 MB
  • 5 - Math numpy PyTorch/16 - Vector and matrix transpose.mp4 27.4 MB
  • 5 - Math numpy PyTorch/28 - Derivatives find minima.mp4 27.3 MB
  • 26 - Where to go from here/228 - How to learn topic X in deep learning.mp4 26.5 MB
  • 6 - Gradient descent/39 - Tangent Notebook revision history.mp4 26.4 MB
  • 9 - Regularization/70 - train and eval modes.mp4 23.9 MB
  • 5 - Math numpy PyTorch/14 - Terms and datatypes in math and computers.mp4 23.8 MB
  • 27 - Python intro Data types/235 - Tuples.mp4 23.3 MB
  • 30 - Python intro Flow control/252 - Continue.mp4 21.7 MB
  • 21 - Transfer learning/199 - CodeChallenge VGG16.mp4 21.3 MB
  • 5 - Math numpy PyTorch/15 - Converting reality to numbers.mp4 20.7 MB
  • 29 - Python intro Functions/240 - Inputs and outputs.mp4 20.1 MB
  • 8 - Overfitting and crossvalidation/63 - Generalization.mp4 19.9 MB
  • 10 - Metaparameters activations optimizers/81 - What are metaparameters.mp4 19.6 MB
  • 11 - FFNs FeedForward Networks/105 - What are fullyconnected and feedforward networks.mp4 18.7 MB
  • 27 - Python intro Data types/230 - How to learn from the Python tutorial.mp4 18.4 MB
  • 15 - Weight inits and investigations/151 - Use default inits or apply your own.mp4 17.6 MB
  • 29 - Python intro Functions/246 - Copies and referents of variables.mp4 15.8 MB
  • 4 - About the Python tutorial/10 - Should you watch the Python tutorial.mp4 14.5 MB
  • 19 - Understand and design CNNs/188 - So many possibilities How to create a CNN.mp4 13.6 MB
  • 5 - Math numpy PyTorch/12 - Introduction to this section.mp4 6.9 MB
  • 2 - Download all course materials/4 - My policy on codesharing.mp4 5.9 MB
  • 2 - Download all course materials/3 - DUDL-PythonCode.zip 1.4 MB
  • 19 - Understand and design CNNs/177 - Examine feature map activations Vietnamese.vtt 45.1 kB
  • 7 - ANNs Artificial Neural Networks/52 - Multioutput ANN iris dataset Vietnamese.vtt 43.7 kB
  • 24 - RNNs Recurrent Neural Networks and GRULSTM/218 - CodeChallenge sine wave extrapolation Vietnamese.vtt 42.8 kB
  • 19 - Understand and design CNNs/174 - CNN to classify MNIST digits Vietnamese.vtt 41.7 kB
  • 24 - RNNs Recurrent Neural Networks and GRULSTM/222 - Lorem ipsum Vietnamese.vtt 40.7 kB
  • 7 - ANNs Artificial Neural Networks/45 - ANN for regression Vietnamese.vtt 40.4 kB
  • 7 - ANNs Artificial Neural Networks/48 - Learning rates comparison Vietnamese.vtt 40.3 kB
  • 19 - Understand and design CNNs/184 - The EMNIST dataset letter recognition Vietnamese.vtt 39.5 kB
  • 16 - Autoencoders/157 - Autoencoder with tied weights Vietnamese.vtt 39.3 kB
  • 7 - ANNs Artificial Neural Networks/47 - ANN for classifying qwerties Vietnamese.vtt 38.4 kB
  • 19 - Understand and design CNNs/176 - Classify Gaussian blurs Vietnamese.vtt 37.4 kB
  • 9 - Regularization/72 - Dropout regularization in practice Vietnamese.vtt 37.2 kB
  • 24 - RNNs Recurrent Neural Networks and GRULSTM/220 - GRU and LSTM Vietnamese.vtt 36.4 kB
  • 11 - FFNs FeedForward Networks/107 - FFN to classify digits Vietnamese.vtt 36.3 kB
  • 9 - Regularization/71 - Dropout regularization Vietnamese.vtt 36.1 kB
  • 19 - Understand and design CNNs/177 - Examine feature map activations English.vtt 35.8 kB
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