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  3. 21. Fine-Tuning

21. Fine-Tuning

  • See this blog post.
The Engineer's Guide
  To  Deep Learning
  • Home
  • Part 1: Neural Networks
    • 1. Perceptron
    • 2. Neural Network
      • 2.1. Formulation of NNs
      • 2.2. Overview of NN Training
      • 2.3. Back Propagation
      • 2.4. Implementation
    • 3. Activation Functions
    • 4. Neural Network ... revisited
      • 4.1. Dense Layer
      • 4.2. Softmax Function
      • 4.3. Optimization
      • 4.4. Exploding and Vanishing Gradients Problems
    • 5. Tensorflow, PyTorch and Keras
  • Part 2: RNNs
    • 6. Dataset and Task
    • 7. Simple RNN
      • 7.1. Formulation
      • 7.2. Back Propagation Through Time
      • 7.3. Implementation
      • 7.4. TensorFlow, PyTorch and Keras
      • 7.5. BPTT in Many-to-Many type
      • 7.6. Exploding and Vanishing Gradients Problems
    • 8. Long Short-Term Memory: LSTM
      • 8.1. Formulation
      • 8.2. Back Propagation Through Time
      • 8.3. Implementation
      • 8.4. TensorFlow, PyTorch and Keras
      • 8.5. BPTT in Many-to-Many type
    • 9. Gated Recurrent Unit: GRU
      • 9.1. Formulation
      • 9.2. Back Propagation Through Time
      • 9.3. Implementation
      • 9.4. TensorFlow, PyTorch and Keras
      • 9.5. BPTT in Many-to-Many type
  • Part 3: NLP and Attentions
    • 10. Dataset and Tokenizer
      • 10.1. Datasets
      • 10.2. Helper Modules
      • 10.3. Word Embedding
    • 11. Language Models
      • 11.1. N-Gram Model
      • 11.2. RNN Based Language Model Architectures
      • 11.3. Language Modeling with RNN
    • 12. Sequence Classification
    • 13. Machine Translation
      • 13.1. Training and Translation
      • 13.2. Implementation
    • 14. Attention Mechanism
      • 14.1. Definition
      • 14.2. Implementations
      • 14.3. Sentiment Analysis
      • 14.4. Encoder-Decoder with Attention
  • Part 4: Transformer
    • 15. Overview
      • 15.1. Positional Encoding
      • 15.2. Multi-Head Attention
      • 15.3. Position-Wise FFN
    • 16. Implementation
      • 16.1. Create Model
      • 16.2. Training
      • 16.3. Translation
    • 17. Dig into the components
      • 17.1. Multi-Head Attention
      • 17.2. Positional Encoding
      • 17.3. Position-wise FFN
    • 18. Related Topics
  • Part 5: GPT-1 (Work in progress)
    • 19. Overview
    • 20. Pre-Training
      • 20.1. Pre-Training DataSet
      • 20.2. Implementation
    • 21. Fine-Tuning
    • 22. After GPT-1
  • Appendix: Basic Knowledge
    • 1. Python
    • 2. Mathematics
      • 2.1. Linear Algebra
      • 2.2. Differential Calculus
      • 2.3. Gradient Descent Algorithms
      • 2.4. Probability
More
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  • The Internals of PostgreSQL
  • GitHub repo
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