[Udemy] A deep understanding of deep learning (with Python intro) (08.2021)
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File Size: 21.60 GB
Creat Time: 2024-06-06
Active Degree: 61
Last Active: 2024-11-20
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File List
- 19 Understand and design CNNs/005 Examine feature map activations.mp4 266.81 MB
- 22 Style transfer/004 Transferring the screaming bathtub.mp4 222.02 MB
- 19 Understand and design CNNs/012 The EMNIST dataset (letter recognition).mp4 206.14 MB
- 19 Understand and design CNNs/002 CNN to classify MNIST digits.mp4 205.13 MB
- 07 ANNs/013 Multi-output ANN (iris dataset).mp4 191.25 MB
- 19 Understand and design CNNs/004 Classify Gaussian blurs.mp4 189.59 MB
- 09 Regularization/004 Dropout regularization in practice.mp4 187.63 MB
- 16 Autoencoders/006 Autoencoder with tied weights.mp4 182.01 MB
- 18 Convolution and transformations/003 Convolution in code.mp4 177.26 MB
- 08 Overfitting and cross-validation/006 Cross-validation -- DataLoader.mp4 176.46 MB
- 23 Generative adversarial networks/002 Linear GAN with MNIST.mp4 173.98 MB
- 07 ANNs/009 Learning rates comparison.mp4 172.68 MB
- 12 More on data/003 CodeChallenge_ unbalanced data.mp4 170.25 MB
- 11 FFNs/003 FFN to classify digits.mp4 165.74 MB
- 16 Autoencoders/005 The latent code of MNIST.mp4 165.69 MB
- 07 ANNs/018 Model depth vs. breadth.mp4 162.73 MB
- 12 More on data/007 Data feature augmentation.mp4 162.06 MB
- 21 Transfer learning/007 Pretraining with autoencoders.mp4 160.34 MB
- 14 FFN milestone projects/004 Project 2_ My solution.mp4 159.46 MB
- 21 Transfer learning/008 CIFAR10 with autoencoder-pretrained model.mp4 157.02 MB
- 07 ANNs/008 ANN for classifying qwerties.mp4 154.75 MB
- 21 Transfer learning/005 Transfer learning with ResNet-18.mp4 152.03 MB
- 19 Understand and design CNNs/008 Do autoencoders clean Gaussians_.mp4 151.43 MB
- 15 Weight inits and investigations/009 Learning-related changes in weights.mp4 150.30 MB
- 07 ANNs/010 Multilayer ANN.mp4 148.17 MB
- 10 Metaparameters (activations, optimizers)/002 The _wine quality_ dataset.mp4 146.94 MB
- 08 Overfitting and cross-validation/005 Cross-validation -- scikitlearn.mp4 146.31 MB
- 25 Where to go from here_/002 How to read academic DL papers.mp4 145.26 MB
- 18 Convolution and transformations/012 Creating and using custom DataLoaders.mp4 142.88 MB
- 07 ANNs/007 CodeChallenge_ manipulate regression slopes.mp4 142.46 MB
- 16 Autoencoders/004 AEs for occlusion.mp4 141.52 MB
- 10 Metaparameters (activations, optimizers)/015 Loss functions in PyTorch.mp4 141.42 MB
- 19 Understand and design CNNs/011 Discover the Gaussian parameters.mp4 139.93 MB
- 09 Regularization/003 Dropout regularization.mp4 139.30 MB
- 12 More on data/001 Anatomy of a torch dataset and dataloader.mp4 139.10 MB
- 23 Generative adversarial networks/004 CNN GAN with Gaussians.mp4 138.96 MB
- 12 More on data/002 Data size and network size.mp4 138.92 MB
- 06 Gradient descent/007 Parametric experiments on g.d.mp4 138.87 MB
- 07 ANNs/006 ANN for regression.mp4 138.75 MB
- 16 Autoencoders/003 CodeChallenge_ How many units_.mp4 138.63 MB
- 15 Weight inits and investigations/005 Xavier and Kaiming initializations.mp4 137.30 MB
- 19 Understand and design CNNs/010 CodeChallenge_ Custom loss functions.mp4 136.08 MB
- 07 ANNs/016 Depth vs. breadth_ number of parameters.mp4 135.23 MB
- 18 Convolution and transformations/011 Image transforms.mp4 133.02 MB
- 15 Weight inits and investigations/006 CodeChallenge_ Xavier vs. Kaiming.mp4 129.54 MB
- 12 More on data/010 Save the best-performing model.mp4 129.54 MB
- 12 More on data/005 Data oversampling in MNIST.mp4 125.54 MB
- 10 Metaparameters (activations, optimizers)/013 CodeChallenge_ Predict sugar.mp4 125.03 MB
- 15 Weight inits and investigations/002 A surprising demo of weight initializations.mp4 124.48 MB
- 03 Concepts in deep learning/003 The role of DL in science and knowledge.mp4 124.47 MB