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Explore the architecture, training, and capabilities of large language models – how they generate text and what limits them.
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Hands-on tutorial to build and train neural networks using PyTorch – from tensor operations to custom models and training loops.
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Understand attention mechanisms, the transformer architecture, BERT, and GPT that power ChatGPT, Claude, and modern NLP systems.
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Deep dive into convolutional neural networks – convolution layers, pooling, feature maps, and applications in image classification and detection.
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Explore the building blocks of deep learning – perceptrons, activation functions, backpropagation, and how neural networks approximate any function.
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