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1. Neural Networks / Deep Learning

No. Neural Networks Neural Networks and Deep Learning
1 Introduction to Deep Learning 了解现今深度学习在哪里应用、如何应用
2 Neural Networks Basics logistic 损失函数、导数、计算图、m个样本的梯度下降、向量化
3 Shallow Neural Networks NN Representation、向量化、Activation functionsRandom init、Python 实现 NN
4 Deep Neural Networks 深网的前向传播、核对矩阵维数、反向传播、参数VS超参数、Python 实现 NN

对于中间层来说, 往往是 ReLU 的效果最好.
虽然 z < 0, 的时候,斜率为0, 但在实践中,有足够多的隐藏单元 令 z > 0, 对大多数训练样本来说是很快的.

so the one place you might use as linear activation function others usually in the output layer.

2. Improving Deep Neural Networks

No. Improving Improving Deep Neural Networks
5 Deep Learning Action Train/Dev/Test、Bias/Variance、L1 L2 Dropout、梯度消失\爆炸、Weight init、Gradient checking
6 Optimization mini-batch、指数加权平均-偏差修正、Momentum、RMSprop、Adam、α decay、局部优
7 超参数调试、Batch Hyperparameter、Normalizing Activations、Batch Norm [Fitting NN]、Softmax

3. Structured Machine Learning

No. Structured Structured Machine Learning Projects
8 ML Strategy 1 正交化、Satisficing and optimizing metrics、Train/dev/test 改变、可避免偏差、人的表现
9 ML Strategy 2 误差分析、标注错误数据、定位数据不匹配偏差与方差、迁移学习、多任务学习、端到端学习

4. Convolutional Neural Networks

No. CNN Convolutional Neural Networks
10 Convolutional Neural Networks Edge detection、Padding、Strided convolutions、Convolutions Over Volumes、Pooling
11   Deep CNN   Classic Nets、ResNets、1×1 convolutions、Inception、Transfer Learning、Data augmentation
12 Object detection Object Localization、Landmark Detection、Sliding Windows、Bounding Box Predictions、Intersection Over Union、Non-max Suppression、Anchor Boxes、YOLO
13 Face recognition One-Shot、Siamese、Triplet Loss、Face Verification、deep ConvNets learning?

LeNet-5、AlexNet、VGG、ResNet (有152层)、Inception。 目标定位、特征点检测、Bounding Box预测、Anchor Boxes

5. Sequence Models

No. Sequence Models Sequence Models
14 Recurrent Sequence Models Notation、RNN、Vanishing gradients、GRU、LSTM、BRNN、Deep RNNs
15 NLP & Word Embeddings Matrix、Word2Vec、Negative Sampling、GloVe、Debiasing Word Embeddings
16 Sequence Models & Attention Greedy Search、Beam Search、Error analysis on beam search、Attention

1. Machine learning Coursera

2. Machine learning roc auc

No. Machine Learning Title Machine Learning Toc Content
11. L1、L2 正则化小记 奥卡姆剃刀、贝叶斯估计、结构风险最小化、L1、L2 范数
12. 模型评估总结 Precision、Recall、ROC、AUC
13. Native Bayes 1 条件独立假设、垃圾邮件识别、多项式/伯努利/混合 模型、平滑
14. Native Bayes 2 独立假设、贝叶斯分类器

3. Machine learning tree model

No. Machine Learning Tree Model Machine Learning Decision Tree & Ensemble
15. Decision Tree 1 ID3 Information gain & C4.5 Gain ratio
16. Decision Tree 2 CART : 回归树: 最小二乘 & 分类树: 基尼指数 Gini index
17. Gradient Boosting 三个臭皮匠,顶个诸葛亮
18. Xgboost @陈天奇怪 提供了 Graident Boosting 算法框架,给出了GBDT,GBRT,GBM 具体实现
19. Ensemble 1 Bootstraping、Bagging (Random Forest)
19. Ensemble 2 概率可学习性 (PAC)、Boosting算法代表 :Adaboost(Adaptive Boosting)
19. Ensumble 集成学习小记 Bagging、Boosting、Stacking、Blending

NLP 基础知识

Contents

  1. 1. Neural Networks / Deep Learning
  2. 2. Improving Deep Neural Networks
  3. 3. Structured Machine Learning
  4. 4. Convolutional Neural Networks
  5. 5. Sequence Models
  6. Friends link
  7. 1. Machine learning Coursera
  8. 2. Machine learning roc auc
  9. 3. Machine learning tree model
    1. NLP 基础知识