【深度学习】洛桑联邦理工学院-深度学习幻灯片

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配套的讲义下载地址：
https://bbs.pinggu.org/thread-9569883-1-1.html
第一部分
ee559-slides-all-part1.zip (68.9 MB, 需要: 5 个论坛币) 本附件包括：

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

2020-8-31 20:29:51 上传

Part1
需要: 5 个论坛币  [购买]

第二部分
ee559-slides-all-part2.zip (39 MB, 需要: 5 个论坛币) 本附件包括：

• ee559-slides-11-1-GAN.pdf
• ee559-slides-11-2-Wasserstein-GAN.pdf
• ee559-slides-11-3-conditional-GAN.pdf
• ee559-slides-11-4-persistence.pdf
• ee559-slides-12-1-RNN-basics.pdf
• ee559-slides-12-2-LSTM-and-GRU.pdf
• ee559-slides-12-3-word-embeddings-and-translation.pdf
• ee559-slides-13-1-attention-memory-translation.pdf
• ee559-slides-13-1-attention.pdf
• ee559-slides-13-2-attention-mechanisms.pdf
• ee559-slides-13-2-linear-attention.pdf
• ee559-slides-13-2-multiplicative-attention.pdf
• ee559-slides-13-2-transformers.pdf
• ee559-slides-13-3-attention-NLP.pdf
• ee559-slides-13-3-transformers.pdf

2020-8-31 20:30:18 上传

Part2
需要: 5 个论坛币  [购买]

目录：

1 – Introduction

2 – Machine learning fundamentals

3 – Multi-layer perceptron and back-propagation

4 – Graphs of operators, autograd, and convolutional layers

5 – Initialization and optimization

6 – Going deeper

7 – Autoencoders

8 – Computer vision

9 – Under the hood

10 – Generative models

11 – Generative adversarial models

12 – Recurrent models and NLP

13 – Attention models

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关键词：深度学习 理工学院 工学院 幻灯片 introduction

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