machine-learning - Java Programmatic Universe

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Topic 11- Working with 4 Python Libraries

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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)