Super Machine Learning Revision Notes

 博文   |  2025年01月04日

涉及到机器学习中的基本概念、不同的算法以及主流的模型。

  • Activation Functions
  • Gradient Descent
    • Computation Graph
    • Backpropagation
    • Gradients for L2 Regularization (weight decay)
    • Vanishing/Exploding Gradients
    • Mini-Batch Gradient Descent
    • Stochastic Gradient Descent
    • Choosing Mini-Batch Size
    • Gradient Descent with Momentum (always faster than SGD)
    • Gradient Descent with RMSprop
    • Adam (put Momentum and RMSprop together)
    • Learning Rate Decay Methods
    • Batch Normalization
  • Parameters
    • Learnable and Hyper Parameters
    • Parameters Initialization
    • Hyper Parameter Tuning
  • Regularization
    • L2 Regularization (weight decay)
    • L1 Regularization
    • Dropout (inverted dropout)
    • Early Stopping
  • Models
    • Logistic Regression
    • Multi-Class Classification (Softmax Regression)
    • Transfer Learning
    • Multi-Task Learning
    • Convolutional Neural Network (CNN)
      • Filter/Kernel
      • Stride
      • Padding (valid and same convolutions)
      • A Convolutional Layer
      • 1*1 Convolution
      • Pooling Layer (Max and Average Pooling)
      • LeNet-5
      • AlexNet
      • VGG-16
      • ResNet (More Advanced and Powerful)
      • Inception Network
      • Object Detection
        • Classification with Localisation
        • Landmark Detection
        • Sliding Windows Detection Algorithm
        • Region Proposal (R-CNN)
        • YOLO Algorithm
          • Bounding Box Predictions (Basics of YOLO)
          • Intersection Over Union
          • Non-max Suppression
        • Anchor Boxes
      • Face Verification
        • One-Shot Learning (Learning a “similarity” function)
          • Siamese Network
          • Triplet Loss
        • Face Recognition/Verification and Binary Classification
      • Neural Style Transfer
      • 1D and 3D Convolution Generalisations
    • Sequence Models
      • Recurrent Neural Network Model
      • Gated Recurrent Unit (GRU)
        • GRU (Simplified)
        • GRU (Full)
      • Long Short Term Memory (LSTM)
      • Bidirectional RNN
      • Deep RNN Example
      • Word Embedding
        • One-Hot
        • Embedding Matrix
        • Learning Word Embedding
        • Word2Vec & Skip-gram
        • Negative Sampling
        • GloVe Vector
        • Deep Contextualized Word Representations (ELMo, Embeddings from Language Models)
      • Sequence to Sequence Model Example: Translation
        • Pick the most likely sentence (Beam Search)
          • Beam Search
          • Length Normalisation
          • Error Analysis in Beam Search (heuristic search algorithm)
        • Bleu Score
        • Combined Bleu
        • Attention Model
    • Transformer (Attention Is All You Need)
    • Bidirectional Encoder Representations from Transformers (BERT)
  • Practical Tips
    • Train/Dev/Test Dataset
    • Over/UnderFitting, Bias/Variance, Comparing to Human-Level Performance, Solutions
    • Mismatched Data Distribution
    • Input Normalization
    • Use a Single Number Model Evaluation Metric
    • Error Analysis (Prioritize Next Steps)

网页链接：
https://createmomo.github.io/2018/01/23/Super-Machine-Learning-Revision-Notes