Abstract: A parallel processing method and apparatus for a neural network model. The parallel processing method includes extracting metadata of a target layer included in a target model, measuring a similarity between the target layer and each of reference layers by comparing the metadata of the target layer to reference metadata of each of the reference layers, selecting a corresponding layer among the reference layers based on the similarities, and generating a parallelization strategy for the target layer based on a reference parallelization strategy matching the corresponding layer.

Abstract: A model training method and apparatus for image recognition, and a non-transitory storage medium are provided. The model training method includes: obtaining a multi-label image training set including a plurality of training images each annotated with a plurality of sample labels; selecting target training images from the multi-label image training set for training a current model; performing label prediction on each target training image using the current model, to obtain a plurality of predicted labels of the each target training image; obtaining a cross-entropy loss function corresponding to the plurality of sample labels of the each target training image, a positive label loss being greater than a negative label loss and having a weight greater than 1; converging the predicted labels and the sample labels of the each target training image according to the cross-entropy loss function, and updating parameters of the current model, to obtain a trained model.

Abstract: The Parallelizable Distributed Data Preservation Apparatuses, Methods and Systems (“PDDP”) transforms an ad impression event, a bidding invite, original data set, original data distribution estimation, symetry ML BET table inputs via PDDP components into real-time mobile bid, mobile ad placement, pseudo random datastet, build classifier structure, build regression structure outputs. In one example embodiment, the PDDP includes an apparatus. The PDDP's apparatus' instructions include obtaining original data set and determine appropriate symmetry ML basic element table, generating original data distribution estimation structure and generate new dataset random generation structure, generating new random dataset transformation structure and transforming original data with the symmetry ML basic element table into pseudo random dataset. The PDDP also provides pseudo random dataset to machine learning component and to generate build classifier and build regression structures from the machine learning component.