https://terms-en.ai-term-hub.com/en/tags/memory/Recent content in Memory on English AI Terms DictionaryHugoen-usSat, 18 Jul 2026 11:44:44 +0000https://terms-en.ai-term-hub.com/en/terms/feedback_neural_network/Sat, 18 Jul 2026 09:58:20 +0000https://terms-en.ai-term-hub.com/en/terms/feedback_neural_network/<h2 id="definition">Definition</h2> <p>Feedback neural networks, also known as recurrent neural networks (RNNs), contain loops that allow signals to propagate back into previous layers. This recurrence enables the network to maintain an internal state or memory of previous inputs, making it suitable for processing sequential data. Unlike feedforward networks, these models can exhibit dynamic temporal behavior and are essential for tasks involving time-series analysis or context-dependent patterns.</p> <h3 id="summary">Summary</h3> <p>A neural network architecture where connections form directed cycles, allowing information to persist over time.</p>https://terms-en.ai-term-hub.com/en/terms/batch_size/Sat, 18 Jul 2026 09:47:51 +0000https://terms-en.ai-term-hub.com/en/terms/batch_size/<h2 id="definition">Definition</h2> <p>Batch size is a critical hyperparameter that determines how many samples are processed before the model&rsquo;s internal parameters are updated. A larger batch size provides a more accurate estimate of the gradient, leading to stable convergence but requiring more memory and potentially generalizing poorly. Conversely, smaller batch sizes introduce noise into the gradient estimation, which can help escape local minima but may result in noisier convergence paths and longer training times due to frequent updates.</p>