Deep Learning Clause Samples
The Deep Learning clause defines the terms and conditions under which deep learning technologies, models, or methods are used, shared, or developed within the context of the agreement. It typically outlines the scope of permitted use, such as whether parties can train, modify, or deploy deep learning models, and may address issues like data usage, intellectual property rights, and confidentiality related to deep learning outputs. This clause ensures that both parties have a clear understanding of their rights and obligations regarding deep learning, thereby reducing the risk of disputes and protecting proprietary technologies or data.
Deep Learning. The parties acknowledge that deep learning refers to a subset of ML based on artificial neural networks that have multiple layers of connected artificial neuron nodes processing data.
Deep Learning. Deep learning is a reflection of the increased complexity and advanced computing power which is now widely available (45,52,61,85). It has also been termed deep neural networks and deep neural learning and the terms are interchangeable. Deep learning is a system of ▇▇▇▇ applied in more complex and ‘fuzzy’ ways. This typically involved much larger data sets that previously used for ANNs, which due to its size is often unlabeled data. Deep learning mostly operates mostly through unsupervised learning, where there are no pre-defined outcomes for the ▇▇▇ other than to find significant associations. These large datasets often involved millions of individual data entries, often taken from web-based data mining sources, such as photo images on the internet. A significant advance in deep learning is where an unsupervised neural network with deep learning capabilities has learned to assess and identify images on the internet, which now enables Google image search. Often the trained unsupervised network will require validation at some point, and this becomes a huge task once the volume of data expands into millions of examples. One way that this has been circumnavigated is to recruit humans to test the network. When Google bought Captcha, an anti-bot security system, in 2009 it found that it could use the requirements of humans to pass an online Turing test (the ‘I am not a robot’ security programme) to test its own ▇▇▇ DeepMind (86). The DeepMind network had been tasked with looking at images on the internet, finding new ways to categorise, recognize and label the images. Unfortunately there needed to be external validation, and this came in the form of asking humans what was on certain images during security tests, and seeing if this matched with previous human replies and those of DeepMind itself (87).
Deep Learning. Whilst traditional ML methods do still have a limited role in medical imaging, they are not entirely suited or reliable in their ability to efficiently perform complex image analysis tasks, especially with the increasing amounts of data available to hand. Deep learning (DL), a subfield of ML has garnered great interest in this respect, rapidly becoming the technique of choice for computer vision and widely adopted for various tasks in medical imaging, including image classification, lesion detection, structural segmentation, and content-based image retrieval (18). Compared to traditional ML methods, DL automatically learns the important features from data, evading the need for hand-engineered feature extraction before the learning process. These systems have dramatically changed the workflow of the engineering process, by enabling end-to-end feature learning through incremental hierarchical models; in this way, simple features are incrementally uncovered and combined as components of more complex features. DL relies on a multi-layered interconnected structure of algorithms known as an artificial neural network (▇▇▇). The concept of ▇▇▇ was a wave of development in ML from the 1950s and stems from the hypothetical nervous system inspired by the biological processes within the brain that involve information exchange between neurons via synapses (290). The artificial neuron as depicted in Figure 2-10 below, is an elementary unit in an ▇▇▇, that mimics the mechanisms of the biological neuron, which exchanges information via synapses to neighbouring neurons. The basic unit of the ▇▇▇ consists of nodes that receive one or more input signals representing features. These are connected to the ensuing neural layer via weights to indicate the strength of that connection between nodes. In the simplest kind of ▇▇▇, the single-layer perceptron, one input layer of nodes is fed via their weights directly to an output layer, which is responsible for implementing a function. This basic model can only compute linearly separable classifications, on the contrary, multi-layer perceptron networks learn through non-linear functions contained within hidden layers of interconnected neurons to tackle more complex operations.