키로거 (Keyloggers)
High-quality technical overview of Keyloggers in the context of blockchain security.
Layers: Input, Hidden (multiple), Output. Optimization: Loss functions, Optimizers (Adam, RMSprop). Hardware: GPUs (NVIDIA A100/H100), TPUs. Frameworks: PyTorch, TensorFlow, Keras, JAX.
graph LR
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🧒 5살도 이해할 수 있게 설명
당신의 키보드만 지켜보는 '숨겨진 카메라'와 같아요. 당신이 누르는 모든 글자를 기록하기 때문에, 아무에게도 알려주지 않은 비밀번호를 나쁜 사람들이 몰래 훔쳐갈 수 있습니다.
🤓 Expert Deep Dive
Technically, Deep Learning is characterized by the use of 'Deep Architectures' (networks with many hidden layers). The fundamental training mechanism is 'Backpropagation' combined with 'Stochastic Gradient Descent' (SGD). By calculating the 'Gradient' of the loss function with respect to the weights, the network can adjust its parameters to reduce error. Crucial components include 'Activation Functions' (like ReLU) which introduce non-linearity, and 'Regularization' techniques (like Dropout) to prevent overfitting. Modern breakthroughs are driven by specific architectures: 'Convolutional Neural Networks' (CNNs) for vision, 'Recurrent Neural Networks' (RNNs) for sequences, and 'Transformers' for the attention-based language modeling used in Large Language Models (LLMs).