The Turing Test, proposed by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
Supervised learning uses labeled data to train algorithms, whereas unsupervised learning uses unlabeled data to find hidden patterns or intrinsic structures in the data.
Backpropagation is a key algorithm in training neural networks, where it calculates the gradient of the loss function with respect to the weights of the network and adjusts the weights to minimize the error.
Reinforcement learning is an area of machine learning where an agent learns by interacting with an environment, receiving rewards or penalties, and aiming to maximize cumulative rewards. Its key components include the agent, environment, actions, state, and reward.
Overfitting occurs when a model learns the noise in the training data, making it perform poorly on unseen data. It can be prevented by techniques like cross-validation, regularization, pruning, and using more training data.
A GAN consists of two neural networks, a generator and a discriminator, which work in opposition. The generator creates synthetic data, while the discriminator evaluates it against real data. Through this adversarial process, the generator improves its output to fool the discriminator.
CNNs are primarily used for spatial data like images, employing convolutional layers to detect patterns. RNNs are designed for sequential data, using feedback loops to process temporal sequences and maintain memory of past inputs.
The curse of dimensionality refers to the exponential increase in computational complexity and data sparsity as the number of features or dimensions in a dataset increases, making it harder to model and interpret.
Transfer learning involves using a pre-trained model on one task as the starting point for a new task, saving time and resources. A common example is using a model trained on a large image dataset like ImageNet to fine-tune it for a specific image recognition task.
Activation functions introduce non-linearity into the model, enabling neural networks to learn complex patterns. The ReLU function is popular due to its simplicity, efficiency, and ability to mitigate the vanishing gradient problem by allowing positive outputs and setting negative values to zero.