感觉用这玩意越来越多,所以想学学。不过没想好怎么学,也没有提纲,买了两本书,一本是深度学习入门,小日子写的。还有一本就是花书。还有就是回Gatech参加线上课程,提纲大概是这样的。
https://omscs.gatech.edu/sites/default/files/documents/2024/Syllabi-CS%207643%202024-1.pdf
Week1:
Module 1: Introduction to Neural Networks Go through Welcome/Getting Started Lesson 1: Linear Classifiers and Gradient Descent Readings: DL book: Linear Algebra background DL book: Probability background DL book: ML Background LeCun et al., Nature '15 Shannon, 1956
Week2:
Lesson 2: Neural Networks Readings: DL book: Deep Feedforward Nets Matrix calculus for deep learning Automatic Differentiation Survey, Baydin et al.
Week3:
Lesson 3: Optimization of Deep Neural Networks Readings: DL book: Regularization for DL DL book: Optimization for Training Deep Models
Week4:
Module 2: Convolutional Neural Networks (OPTIONAL) Lesson 6: Data Wrangling Lesson 5: Convolution and Pooling Layers Readings: Preprocessing for deep learning: from covariance matrix to image whitening cs231n on preprocessing DL book: Convolutional Networks Optional: Khetarpal, Khimya, et al. Reevaluate: Reproducibility in evaluating reinforcement learning algorithms." (2018). See related blog post
Week5:
Lesson 6: Convolutional Neural Network Architectures
Week6:
Lesson 7: Visualization Lesson 8: PyTorch and Scalable Training Readings: Understanding Neural Networks Through Deep Visualization Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
Week7:
Lesson 9: Advanced Computer Vision Architectures Lesson 10: Bias and Fairness Readings: Fully Convolutional Networks for Semantic Segmentation
Week8:
Module 3: Structured Neural Representations Lesson 11: Introduction to Structured Representations Lesson 12: Language Models Readings: DL Book: Sequential Modeling and Recurrent Neural Networks (RNNs)
Week9:
Lesson 13: Embeddings Readings: word2vec tutorial word2vec paper StarSpace paper
Week10:
Lesson 14: Neural Attention Models Readings: Attention is all you need BERT Paper The Illustrated Transformer
Week11:
Lesson 15: Neural Machine Translation Lesson 16: Automated Speech Recognition (ASR)
Week12:
Module 4: Advanced Topics Lesson 17: Deep Reinforcement Learning Readings: MDP Notes (courtesy Byron Boots) Notes on Q-learning (courtesy Byron Boots) Policy iteration notes (courtesy Byron Boots) Policy gradient notes (courtesy Byron Boots)
Week13:
Lesson 18: Unsupervised and Semi-Supervised Learning
Week14:
Lesson 19: Generative Models Readings: Tutorial on Variational Autoencoder NIPS 2016 Tutorial: Generative Adversarial Networks
从提纲可以看到,核心还是神经网络。
然后就是网络的几种架构。卷积神经网络(CNN):主要用于图像处理和计算机视觉任务。**循环神经网络(RNN)**及其变种(如LSTM、GRU):主要用于处理序列数据,如时间序列分析和自然语言处理。生成对抗网络(GAN):用于生成逼真的数据样本,如图像生成。自编码器(Autoencoder):用于无监督学习和特征提取。
大概就是这些,看起来也不是太多。。。
待续。。。