Tag: machine-learning
Topic 15 – 4 points about Python Data Science and Machine Learning
9 (A)- Automatic evaluation metrics (BLEU, ROUGE, METEOR)
7 (E)- Code generation and programming assistance
6 (A)- Model compression and quantization
5 (A)- Pre-training techniques (self-supervised learning, masked language modeling)
4 (C)- TensorFlow and PyTorch for LLMs
3 (B)- Bidirectional Encoder Representations from Transformers (BERT)
2 (E)- Transformer Architecture
2 (D)- Attention Mechanisms
2 (C)- Convolutional Neural Networks for NLP
2 (B)- Recurrent Neural Networks (RNNs, LSTMs, GRUs)
2 (A)- Feedforward Neural Networks
1 (C) NLP tasks (sentiment analysis, named entity recognition, text classification)
1 (B) – Feature extraction (bag-of-words, TF-IDF, word embeddings)
1 (A)- Text preprocessing (tokenization, stemming, lemmatization)
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