Artificial intelligence is increasingly relevant to many realms of human experience. Learn what it is, how it works, and the pros and cons of its rollout.
Those smart machines are also getting faster and more complex. Some computers have now crossed theexascalethreshold, meaning they can perform as many calculations in a single second as an individual could in31,688,765,000 years. And beyond computation, which machines have long been faster at tha...
Theactivation layeris a commonly added and equally important layer in a CNN. The activation layer enables nonlinearity -- meaning the network can learn more complex (nonlinear) patterns. This is crucial for solving complex tasks. This layer often comes after the convolutional or fully connected lay...
Text-to-speech is a form of speech synthesis that converts any string of text characters into spoken output.
A convolutional neural network is also known as a ConvNet. Techopedia Explains Convolutional Neural Network Like other kinds of artificial neural networks, a convolutional neural network has an input layer, an output layer and various hidden layers. Some of these layers are convolutional, using a ...
While AI and ML are often used as synonyms, the artificial intelligence meaning is an umbrella term, and machine learning is a subset of artificial intelligence. Essentially, every ML application can be referred to as AI, but not all artificial intelligence applications use machine learning. For ...
Big data and deep learning techniques spark AI revival.Groundbreaking work in 1989 by Yann LeCun, Yoshua Bengio and Patric Haffner showed thatconvolutional neural networks(CNNs) could be applied to real-world problems, propelling an AI renaissance that continues to this...
Deep learning approaches like convolutional neural network models produce faster and more accurate object predictions. Of course, you need a higher GPU and larger datasets for that to happen! Deep learning is used for a variety of object detection tasks. Modern-day video surveillance cameras or ...
Many ZSL methods represent both classes and samples assemantic embeddings: vector representations that can be used to reflect the features or meaning of (and relationship between) different data points. Classification is then determined by measuring similarity between the semantic embedding of a given ...
One obstacle to using KL divergence for variational inference is that the denominator of the equation isintractable, meaning it would take a theoretically infinite amount of time to compute directly. To work around that problem, and integrate both key loss functions, VAEs approximate theminimization...