Hello dear supporters! New Lottery Ai is available for download for members.
Whats new in version 2.0 2024/12/03
Fixed bug where sometimes a duplicated draw appeared.
Fixed bug where, in some cases, a generated draw was already in data.txt.
Added a new feature to check if a draw is in data.txt.
Other Minor bug fixes
Created comprehensive documentation.
Added a new powerful (Prometheus) AI model and optimized Hera (formerly Jupiter).
Created an eBook with tips.
Download/Buy here: https://buymeacoffee.com/corvuscodex/e/155047
Not a member? Join: https://buymeacoffee.com/corvuscodex/membership
Prometheus Model
Overview: The Prometheus model is an advanced AI random number generator designed to predict lottery numbers based on past draws. It leverages sophisticated neural network architectures to provide accurate and diverse predictions, ensuring a high level of variability and uniqueness in each run.
Architecture:
Embedding Layer: Converts input sequences into dense vectors of fixed size (128 dimensions).
Bidirectional LSTM Layer: Processes the input sequences in both forward and backward directions, capturing more context (512 units).
Dropout Layers: Applied after each LSTM layer to prevent overfitting (0.3 dropout rate).
Dense Layer: Fully connected layer with ReLU activation (1024 units).
Output Layer: Softmax activation for multi-class classification, outputting probabilities for each possible class.
Key Features:
High Dimensional Embeddings: Provides rich representations of input sequences.
Bidirectional LSTM: Captures context from both past and future sequences.
Dropout Regularization: Reduces overfitting and improves generalization.
Enhanced Randomness: Introduces noise and shuffling to ensure diverse predictions on each run.
Usage:
Suitable for tasks requiring high variability and complex sequence predictions.
Ideal for applications where diverse and unique outputs are essential, such as predicting lottery numbers based on past draws.
Hera Model (formerly Jupiter)
Overview: The Hera model, formerly known as Jupiter, is a highly efficient and accurate sequence prediction model designed for tasks that require precise next predictions. It is optimized for quick and reliable outputs, making it ideal for applications where consistency and accuracy are paramount.
Architecture:
Embedding Layer: Converts input sequences into dense vectors of fixed size (64 dimensions).
LSTM Layer: Processes the input sequences to capture temporal dependencies (256 units).
Output Layer: Softmax activation for multi-class classification, outputting probabilities for each possible class.
Key Features:
Efficient Embeddings: Provides compact representations of input sequences.
Single LSTM Layer: Captures essential temporal dependencies with lower computational cost.
Quick Training: Faster training times due to simpler architecture.
Stable Predictions: Consistent and reliable outputs without additional randomness.
Usage:
Suitable for tasks requiring quick and efficient sequence predictions.