By Medium -
2020-12-02
Deep Learning harnesses the power of Big Data by building deep neural architectures that try to approximate a function f(x) that can map an input, x to its corresponding label, y. The Universal…
By datasciencecentral -
2020-12-27
Deep Learning is a new area of Machine Learning research that has been gaining significant media interest owing to the role it is playing in artificial intel…
By Medium -
2020-10-05
Multi-layer Perceptrons (MLPs) are standard neural networks with fully connected layers, where each input unit is connected with each output unit. We rarely use these kind of layers when working with…
By DeepAI -
2019-05-17
Batch Normalization is a supervised learning technique that converts selected inputs in a neural network layer into a standard format, called normalizing.
By RStudio AI Blog -
2020-12-22
In forecasting spatially-determined phenomena (the weather, say, or the next frame in a movie), we want to model temporal evolution, ideally using recurrence relations. At the same time, we'd like to ...
By TOPBOTS -
2021-02-04
An overview of the architecture and the implementation details of the most important Deep Learning algorithms for Time Series Forecasting.
