AI and ML for Coders in PyTorch

Index

A

• accelerators
  • Google Colab accelerators, Extending the Dataset
  • model.to(device) for accelerator, Implementing Convolutional Neural Networks, Optimizing the Load Phase
• accuracy
  • Fashion MNIST database model, Training the Neural Network-Training the Neural Network
    • training until desired accuracy, Early Stopping-Early Stopping
  • learning process, Getting Started with Machine Learning
  • speed versus accuracy of learning, Designing the Neural Network
• activation functions, Designing the Neural Network
  • ReLU, Designing the Neural Network
    • feedforward network, The feedforward network layer
• Adam optimizer, The Complete Code, The Complete Code, CNN Architecture for “Horses or Humans”
  • Amsgrad as alternative implementation, Adjusting the learning rate
  • learning rate parameter adjustment, Adjusting the learning rate, Optimizing stacked LSTMs, Tuning the Learning Rate
  • time series DNN, Creating and Training a DNN to Fit the Sequence Data
    • learning rate tuned, Tuning the Learning Rate
• Amsgrad optimizer, Adjusting the learning rate
• APIs
  • custom splits of datasets, Using Custom Splits
  • diffusers library (see diffusers library (Hugging Face))
  • Hugging Face API to sign in, Using Hugging Face Diffusers
  • RAG API by LangChain, Getting Started with RAG
  • transformers API from Hugging Face, Using a Model From Hugging Face Hub, The transformers API
    • (see also transformers library (Hugging Face))
• artificial understanding, Introduction to RAG, Introduction to RAG
• attention
  • cross-attention of encoder-decoder, The Encoder-Decoder Architecture ...