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Machine Learning & Deep Learning
Core ML/DL concepts: supervised and unsupervised learning, neural networks (CNN, RNN/LSTM, GAN, Transformers), training techniques, model optimization, and real-world applications in software engineering and NLP.
Recurrent Neural Networks & LSTMs
RNN fundamentals, vanishing gradient problem, LSTM (forget/input/output gates), GRU, sequence-to-sequence models, and applications (NLP, time series).
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