Validation in the context of machine learning and data science refers to the process of evaluating a model's performance using a separate dataset that was not used during the training phase. This process helps to ensure that the model generalizes well to new, unseen data and does not simply memorize the training data (a problem known as overfitting). Validation is a crucial step in the model development lifecycle, providing insights into how well a model is likely to perform in real-world applications.
Vanishing and exploding gradients are issues that occur during the training of deep neural networks, particularly in networks with many layers. These problems arise when gradients, the values used to update the weights of the network during backpropagation, become either too small (vanishing) or too large (exploding). Vanishing gradients slow down learning and can cause the network to stop training altogether while exploding gradients can cause the model to diverge and fail to learn effectively.
Variance is a statistical measure that quantifies the spread or dispersion of a set of data points in a dataset. It represents the average of the squared differences between each data point and the mean of the dataset. A higher variance indicates that the data points are spread out widely around the mean, while a lower variance indicates that they are closer to the mean. Variance is a fundamental concept in statistics and is crucial for understanding the variability within a dataset.
A variational autoencoder (VAE) is a type of generative model in machine learning that combines principles from deep learning and Bayesian inference. VAEs are designed to learn the underlying distribution of data and generate new data points that are similar to the original dataset. They consist of two main components: an encoder, which compresses the data into a latent space, and a decoder, which reconstructs the data from this latent space. Unlike traditional autoencoders, VAEs incorporate randomness into the encoding process, allowing them to generate diverse outputs from the learned distribution.
A virtual machine (VM) is a software emulation of a physical computer that runs an operating system and applications just like a physical computer. VMs allow multiple operating systems to coexist on a single physical machine, enabling efficient use of hardware resources. Each VM operates independently, with its own virtualized hardware, including CPU, memory, storage, and network interfaces, creating an isolated environment for running software.
A vision processing unit (VPU) is a specialized processor designed to accelerate the processing of visual data, such as images and videos, in real-time. VPUs are optimized for tasks in computer vision, deep learning, and artificial intelligence (AI), providing efficient performance while consuming less power compared to general-purpose processors like CPUs or GPUs. They are commonly used in applications requiring real-time visual data processing, such as robotics, drones, smart cameras, augmented reality (AR), and autonomous vehicles.