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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.
Neural Networks Fundamentals
Perceptron, multilayer networks, backpropagation, activation functions (ReLU, sigmoid, tanh), optimizers (SGD, Adam), and universal approximation.
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