Deep Learning has proven its ability to solve various problems, including handwriting recognition, speech recognition, and computer vision. The algorithms are a reproduction of the human brain, which is the most powerful engine. The network may capture the latent structure in any dataset better than a human being possibly could. However, the results seem somehow magical for someone who is not familiar with this class of algorithms. Randomness adds to the dimensionality of a model. This software uses a new model to predict lottery numbers using the history of past draws as a training set. Lotto is a very popular and widespread game based on guessing numbers. The lottery principle is simple: people buy tickets that contain a list of combinations that bet over a finite set of numbers. A draw happens eventually at a fixed date and time. The gains depend upon how well the ticket combination matches the winning numbers. The jackpot is when the ticket has the winning combination.
Deep Learning
Deep Learning has proven its ability to solve various problems, including handwriting recognition, speech recognition, and computer vision. The algorithms are a reproduction of the human brain, which is the most powerful engine. The network may capture the latent structure in any dataset better than a human being possibly could. However, the results seem somehow magical for someone who is not familiar with this class of algorithms. Randomness adds to the dimensionality of a model. This software uses a new model to predict lottery numbers using the history of past draws as a training set. Lotto is a very popular and widespread game based on guessing numbers. The lottery principle is simple: people buy tickets that contain a list of combinations that bet over a finite set of numbers. A draw happens eventually at a fixed date and time. The gains depend upon how well the ticket combination matches the winning numbers. The jackpot is when the ticket has the winning combination.
Model we use
Inputs for an AI lotto prediction are the former rounds' drawn numbers.
Simply put, input for current round numbers prediction are the numbers drawn in the last draw (or several of the last draws, as per settings).
The result that the network is returning is the probability of each number appearing in a winning combination in the future draw.
In addition to this network, the software features another neural network. Based on a history of past draws, this network can reveal to us how well any combination of numbers relates to previous winning combinations.
Remember, artificial intelligence is better than natural intelligence (ours) because it is not distracted but knowledge and understanding of the problem as it simply analyzes input and output data. That approach is preferred when dealing with inherently stochastic processes as random events certainly are.
