{'fraction_non_alphanumeric': 0.024355300859598854, 'fraction_numerical': 0.0064469914040114614, 'mean_word_length': 4.869747899159663, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Unrestricted Random Sampling of data batch to improve the efficiency of neural networks

One of the main sources of the overfitting problem is the co-adaptation of the machine learning algorithm with the training data set [1]. Several techniques were proposed in order to avoid or minimize the probability that a machine learning algorithm may overfit. Those approaches can be classified into two main categories: the first category is the data based approaches, in general those approaches tries to diversify the training dataset as much as possible, the most known technique is data augmentation [2] [10]. The second category is the learning algorithm based approaches like dropout [3], drop connect [4], drop weak [5] or L2 penalty [6]. In this paper we propose a novel data based approach to avoid the overfitting. This approach is inspired by the human brain's learning process, and it consist of training the machine learning model on a dynamic dataset. By a dynamic dataset we mean that at each training iteration the learning machine will use a new subset of data of size n. The samples of this subset of data are drawn randomly from the original dataset. This mechanism adds a dynamic characteristic to the training dataset. This dynamicity prevents the learning algorithm to co-adapts with the training dataset, which means it helps the learning algorithm to improve its generalization.