A muggles guide to deep learning wizardry

Artificial intelligence in veterinary medicine

“…Artificial neural networks are computer systems composed of layers of connected nodes. 3 They are named as such because they mimic the biological process of neurons, which have many input and output synapses and interconnect with other neurons with many input and output synapses. As this is an in-depth concept, it may be beneficial to consider the example of making a diagnosis of "pulmonary nodules" on a medical image both from the perspective of a human observer and an AI (Figure 2).…”

“…2 The term artificial intelligence was coined by John McCarthy in 1955. 2,3 Artificial intelligence was heavily researched in the second half of the 20th century but experienced little advancement in its application and scope due to limitations in available computing power. However, with major advances in computer processing power in the past decade and the digitization and availability of large amounts of data, AI has taken off.…”

A novel deep machine learning algorithm with dimensionality and size reduction approaches for feature elimination: thyroid cancer diagnoses with randomly missing data

“…Missing or sparse data in medical informatics is common because screening, diagnosis, and treatment choices mostly vary among the physicians [ 5 ]. Lack in data imposes challenges in both the mathematical and machine learning optimization problems as its omittance causes biased, misinformed, and even unsTable estimates due to singularity in the information bases [ 6 ]. Emmanuel et al .…”

Machine learning classification of retinal imaging of neuropsychiatric and healthy cases in a subset of the UK Biobank cohort

“…However, a welltrained model can receive a set of inputs about the problem or task and compute an output of an unknown variable that serves to complete the task accurately. A well-trained model is generalizable to new sets of data that were not within the original input-output training examples (G. Currie, 2022;G. M. Currie, 2021).…”

“…The backpropagation of errors algorithm uses this error value to adjust each of the parameters (i.e., the weights and biases) in a DNN to better model the mapping of inputs to outputs, and therefore generate more accurate predictions (Lillicrap et al, 2020;Munro, 2017) The preceding paragraphs are a much-simplified overview of the data training process in DL. Readers should review several reference entries, review articles, and book chapters for significantly more comprehensive descriptions about DNNs and DL (G. Currie, 2019Currie, , 2022Dastres & Soori, 2021;R. V. Li Baoxin, 2017;Lillicrap et al, 2020;Montesinos López et al, 2022b;Munro, 2017;Zaras et al, 2022).…”