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

19 - Understand and design CNNs/177 - Examine feature map activations.mp4

22 - Style transfer/205 - Transferring the screaming bathtub.mp4

19 - Understand and design CNNs/176 - Classify Gaussian blurs.mp4

24 - RNNs Recurrent Neural Networks and GRULSTM/218 - CodeChallenge sine wave extrapolation.mp4

18 - Convolution and transformations/163 - Convolution in code.mp4

24 - RNNs Recurrent Neural Networks and GRULSTM/217 - Predicting alternating sequences.mp4

26 - Where to go from here/229 - How to read academic DL papers.mp4

19 - Understand and design CNNs/184 - The EMNIST dataset letter recognition.mp4

19 - Understand and design CNNs/174 - CNN to classify MNIST digits.mp4

24 - RNNs Recurrent Neural Networks and GRULSTM/222 - Lorem ipsum.mp4

7 - ANNs Artificial Neural Networks/52 - Multioutput ANN iris dataset.mp4

23 - Generative adversarial networks/210 - CNN GAN with Gaussians.mp4

21 - Transfer learning/200 - Pretraining with autoencoders.mp4

19 - Understand and design CNNs/180 - Do autoencoders clean Gaussians.mp4

9 - Regularization/72 - Dropout regularization in practice.mp4

21 - Transfer learning/198 - Transfer learning with ResNet18.mp4

16 - Autoencoders/157 - Autoencoder with tied weights.mp4

7 - ANNs Artificial Neural Networks/47 - ANN for classifying qwerties.mp4

10 - Metaparameters activations optimizers/82 - The wine quality dataset.mp4

18 - Convolution and transformations/171 - Image transforms.mp4

8 - Overfitting and crossvalidation/66 - Crossvalidation DataLoader.mp4

23 - Generative adversarial networks/208 - Linear GAN with MNIST.mp4

12 - More on data/119 - CodeChallenge unbalanced data.mp4

16 - Autoencoders/156 - The latent code of MNIST.mp4

11 - FFNs FeedForward Networks/107 - FFN to classify digits.mp4

7 - ANNs Artificial Neural Networks/57 - Model depth vs breadth.mp4

12 - More on data/123 - Data feature augmentation.mp4

19 - Understand and design CNNs/178 - CodeChallenge Softcode internal parameters.mp4

15 - Weight inits and investigations/147 - CodeChallenge Xavier vs Kaiming.mp4

7 - ANNs Artificial Neural Networks/48 - Learning rates comparison.mp4

21 - Transfer learning/201 - CIFAR10 with autoencoderpretrained model.mp4

13 - Measuring model performance/131 - APRF example 1 wine quality.mp4

15 - Weight inits and investigations/150 - Learningrelated changes in weights.mp4

10 - Metaparameters activations optimizers/83 - CodeChallenge Minibatch size in the wine dataset.mp4

7 - ANNs Artificial Neural Networks/49 - Multilayer ANN.mp4

8 - Overfitting and crossvalidation/65 - Crossvalidation scikitlearn.mp4

14 - FFN milestone projects/139 - Project 2 My solution.mp4

19 - Understand and design CNNs/182 - CodeChallenge Custom loss functions.mp4

10 - Metaparameters activations optimizers/95 - Loss functions in PyTorch.mp4

18 - Convolution and transformations/172 - Creating and using custom DataLoaders.mp4

9 - Regularization/71 - Dropout regularization.mp4

12 - More on data/117 - Anatomy of a torch dataset and dataloader.mp4

6 - Gradient descent/36 - Parametric experiments on gd.mp4

13 - Measuring model performance/132 - APRF example 2 MNIST.mp4

7 - ANNs Artificial Neural Networks/46 - CodeChallenge manipulate regression slopes.mp4

12 - More on data/118 - Data size and network size.mp4

7 - ANNs Artificial Neural Networks/55 - Depth vs breadth number of parameters.mp4

15 - Weight inits and investigations/146 - Xavier and Kaiming initializations.mp4

16 - Autoencoders/154 - CodeChallenge How many units.mp4

19 - Understand and design CNNs/179 - CodeChallenge How wide the FC.mp4