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Gen AI

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Description
Introduction and foundational concepts:
  • Statistics
  • Probability and Matrices
  • Data Mining and Embedding DS with SDLC
  • Unsupervised and Supervised Learning
  • Regression and Evaluation
  • Classification and Evaluation
  • Fundamental concepts of Deep Learning (Neural Networks, Forward/Backward propagation, Epoch, Learning Rate, Activation Function, Loss Function
  • ANN, CNN, RNN
  • LSTM
  • Tokenization (BOW) and regex
  • Vectorization (TF-IDF, Word2Vec, GloVe)
  • NLP Libraries
  • NLP use cases
  • Advanced DL (CV, Encoders, GANs), Advanced NLP (Transformers, Attention, BERT, T5)
  • LLM and foundational concepts, Different types and Key players
  • Prompt Engineering
  • RAG and components, Fine Tuning, Implementation Architectures
Practical Applications, Capstone Project and bonus.
AWS S3
⁠AWS Q
Claude 3
Prompt Engineering
AWS Bedrock
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