Directory Listing: ee559-Deeplearning

• ⬅ Back to Home
• 📄 by-nc-sa-4.0.txt
• 📄 ee559-handout-1-1-from-anns-to-deep-learning.pdf
• 📄 ee559-handout-1-2-current-success.pdf
• 📄 ee559-handout-1-3-what-is-happening.pdf
• 📄 ee559-handout-1-4-tensors-and-linear-regression.pdf
• 📄 ee559-handout-1-5-high-dimension-tensors.pdf
• 📄 ee559-handout-1-6-tensor-internals.pdf
• 📄 ee559-handout-10-1-GAN.pdf
• 📄 ee559-handout-10-2-Wasserstein-GAN.pdf
• 📄 ee559-handout-10-3-conditional-GAN.pdf
• 📄 ee559-handout-10-4-persistence.pdf
• 📄 ee559-handout-11-1-RNN-basics.pdf
• 📄 ee559-handout-11-2-LSTM-and-GRU.pdf
• 📄 ee559-handout-11-3-word-embeddings-and-translation.pdf
• 📄 ee559-handout-2-1-loss-and-risk.pdf
• 📄 ee559-handout-2-2-overfitting.pdf
• 📄 ee559-handout-2-3-bias-variance-dilemma.pdf
• 📄 ee559-handout-2-4-evaluation-protocols.pdf
• 📄 ee559-handout-2-5-basic-embeddings.pdf
• 📄 ee559-handout-3-1-perceptron.pdf
• 📄 ee559-handout-3-2-LDA.pdf
• 📄 ee559-handout-3-3-features.pdf
• 📄 ee559-handout-3-4-MLP.pdf
• 📄 ee559-handout-3-5-gradient-descent.pdf
• 📄 ee559-handout-3-6-backprop.pdf
• 📄 ee559-handout-4-1-DAG-networks.pdf
• 📄 ee559-handout-4-2-autograd.pdf
• 📄 ee559-handout-4-3-modules-and-batch-processing.pdf
• 📄 ee559-handout-4-4-convolutions.pdf
• 📄 ee559-handout-4-5-pooling.pdf
• 📄 ee559-handout-4-6-writing-a-module.pdf
• 📄 ee559-handout-5-1-cross-entropy-loss.pdf
• 📄 ee559-handout-5-2-SGD.pdf
• 📄 ee559-handout-5-3-optim.pdf
• 📄 ee559-handout-5-4-l2-l1-penalties.pdf
• 📄 ee559-handout-5-5-initialization.pdf
• 📄 ee559-handout-5-6-architecture-and-training.pdf
• 📄 ee559-handout-5-7-writing-an-autograd-function.pdf
• 📄 ee559-handout-6-1-benefits-of-depth.pdf
• 📄 ee559-handout-6-2-rectifiers.pdf
• 📄 ee559-handout-6-3-dropout.pdf
• 📄 ee559-handout-6-4-batch-normalization.pdf
• 📄 ee559-handout-6-5-residual-networks.pdf
• 📄 ee559-handout-6-6-using-GPUs.pdf
• 📄 ee559-handout-7-1-CV-tasks.pdf
• 📄 ee559-handout-7-2-image-classification.pdf
• 📄 ee559-handout-7-3-object-detection.pdf
• 📄 ee559-handout-7-4-segmentation.pdf
• 📄 ee559-handout-7-5-dataloader-and-surgery.pdf
• 📄 ee559-handout-8-1-looking-at-parameters.pdf
• 📄 ee559-handout-8-2-looking-at-activations.pdf
• 📄 ee559-handout-8-3-visualizing-in-input.pdf
• 📄 ee559-handout-8-4-optimizing-inputs.pdf
• 📄 ee559-handout-9-1-transposed-convolutions.pdf
• 📄 ee559-handout-9-2-autoencoders.pdf
• 📄 ee559-handout-9-3-denoising-and-variational-autoencoders.pdf
• 📄 ee559-handout-9-4-NVP.pdf