Together with the weights, all inputs also get a bias value. Think of this as an additional tunable parameter that helps make the network more efficient and accurate. Once the two values, weights, and bias, are added, the result is fed to an activation function. It is this function that...
A convolutional neural network (CNN) is a category ofmachine learningmodel. Specifically, it is a type ofdeep learningalgorithm that is well suited to analyzing visual data. CNNs are commonly used to process image and video tasks. And, because CNNs are so effective at identifying objects, the...
Another distinguishing characteristic of recurrent networks is that they share parameters across each layer of the network. While feedforward networks have different weights across each node, recurrent neural networks share the same weight parameter within each layer of the network. That said, these wei...
Wenmang is not a superman, a superman is still a human being, and there are people's bad habits. Superman is only an elite of people, while Wenmang is not a known person (nor a superman), but outside and above people. In this doomsday, it can't be a traditional person or a ...
know-how. In many cases, this knowledgediffers from that needed to build non-AI software. For example, building and deploying a machine learning application involves a complex, multistage and highly technical process, from data preparation to algorithm selection to parameter tuning and model testing...
An epoch in machine learning refers to one complete pass of the training dataset through a neural network, helping to improve its accuracy and performance.
Moving deeper into the network, feature maps may represent more complex features, such as shapes, textures, or even whole objects: The number of feature maps in a convolutional layer is a hyperparameter that can be tuned during the network design. Increasing the number of feature maps can ...
such as terabytes or petabytes of data text or images or video from the internet. The training yields aneural networkof billions ofparametersencoded representations of the entities, patterns and relationships in the data that can generate content autonomously in response to prompts. This is the foun...
Created by the Applied Deep Learning Research team at NVIDIA, Megatron provides an 8.3 billion parameter transformer language model with 8-way model parallelism and 64-way data parallelism, according to NVIDIA. To execute this model, which is generally pre-trained on a dataset of 3.3 billion ...
Scaling up the parameter count and training dataset size of a generative AI model generally improves performance. Model parameters transform the input (or prompt) into an output (e.g., the next word in a sentence); training a model means tuning its parameters so that the output is more accu...