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
Center["Keyloggers"]:::main
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🤓 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).