How do Deep Neural Networks work?¶
Deep neural networks (DNNs) are computational models that mimic the human brain's interconnected neuron structure to process complex data patterns. They consist of multiple layers of artificial neurons, each receiving inputs, applying weights, summing the results, and passing them through an activation function to produce an output. This layered architecture enables DNNs to model intricate relationships within data, making them effective for tasks such as image and speech recognition.
The resurgence of interest in DNNs is largely due to advancements in computational power, particularly through the use of Graphics Processing Units (GPUs). Originally designed for rendering graphics, GPUs excel at performing rapid matrix multiplications, a fundamental operation in training neural networks. Frameworks like CUDA and cuDNN have further facilitated the deployment of neural network computations on GPUs, significantly reducing training times and enhancing performance.
A critical component of DNNs is the activation function, which introduces non-linearity into the model, enabling it to capture complex patterns beyond linear relationships. While early neural networks often utilized sigmoid functions, modern architectures typically employ Rectified Linear Units (ReLUs) or their variants. ReLUs output zero for negative inputs and pass positive inputs unchanged, allowing models to learn complex data representations more effectively.
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