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Deep learning courseDocuments
Dlc-video-3-5-gradient-descent |
Dlc-video-3-6-backprop |
Dlc-video-3-1-perceptron |
Dlc-video-3-2-LDA |
Dlc-video-3-3-features |
Dlc-video-3-4-MLP |
Dlc-video-1-1-from-anns-to-deep-learning |
Dlc-video-1-2-current-success |
Dlc-video-1-3-what-is-happening |
Dlc-video-1-4-tensors-and-linear-regression |
Dlc-video-1-5-high-dimension-tensors |
Dlc-video-1-6-tensor-internals |
Dlc-video-2-1-loss-and-risk |
Dlc-video-2-2-overfitting |
Dlc-video-2-3-bias-variance-dilemma |
Dlc-video-2-4-evaluation-protocols |
Dlc-video-2-5-basic-embeddings |
Dlc-video-4-1-DAG-networks |
Dlc-video-4-2-autograd |
Dlc-video-4-3-modules-and-batch-processing |
Dlc-video-4-4-convolutions |
Dlc-video-4-5-pooling |
Dlc-video-4-6-writing-a-module |
Dlc-video-5-1-cross-entropy-loss |
Dlc-video-5-2-SGD |
Dlc-video-5-3-optim |
Dlc-video-5-4-l2-l1-penalties |
Dlc-video-5-5-initialization |
Dlc-video-5-6-architecture-and-training |
Dlc-video-5-7-writing-an-autograd-function |
Dlc-video-6-1-benefits-of-depth |
Dlc-video-6-2-rectifiers |
Dlc-video-6-3-dropout |
Dlc-video-6-4-batch-normalization |
Dlc-video-6-5-residual-networks |
Dlc-video-6-6-using-GPUs |
Dlc-video-7-1-transposed-convolutions |
Dlc-video-7-2-autoencoders |
Dlc-video-7-3-denoising-autoencoders |
Dlc-video-7-4-VAE |
Dlc-video-8-1-CV-tasks |
Dlc-video-8-2-image-classification |
Dlc-video-8-3-object-detection |
Dlc-video-8-4-segmentation |
Dlc-video-8-5-dataloader-and-surgery |
Dlc-video-9-1-looking-at-parameters |
Dlc-video-9-2-looking-at-activations |
Dlc-video-9-3-visualizing-in-input |
Dlc-video-9-4-optimizing-inputs |
Dlc-video-10-1-autoregression |
Dlc-video-10-2-causal-convolutions |
Dlc-video-10-3-NVP |
Dlc-video-11-1-GAN |
Dlc-video-11-2-Wasserstein-GAN |
Dlc-video-11-3-conditional-GAN |
Dlc-video-11-4-persistence |
Dlc-video-12-1-RNN-basics |
Dlc-video-12-2-LSTM-and-GRU |
Dlc-video-12-3-word-embeddings-and-translation |
Dlc-video-13-1-attention-memory-translation |
Dlc-video-13-2-attention-mechanisms |
Dlc-video-13-3-transformers |