soundtrack nor elle

1. Press the "Choose File" button to select your dataset as CSV or Excel file. 

2. Choose the outcome from your dataset that you want to predict and press "Submit."

3. Choose the predictors from your dataset that should predict the outcome and press "Submit."

4. Press the "Train" button. That's it! Your ML model is ready to work. If you are not happy with the performance, press the "Train" button again to retrain the model.

5. You can now predict the outcome by inputting new predictors using the "Predict" button. 

6. Press "Download Model" to save your model for Deployment as Web Application or retraining with new data.

7. To use a saved model, press "Upload Model." You can retrain the model by adding new data with the "Add New Data" button, followed by the "Retrain with New Data" button. For prediction, use the "Predict" button.

CAVE: The model currently performs only supervised ML regression tasks; classification functionalities are not available at this time.

P.S. The machine is preset to find the best performing result. Thus, utilize training parameters only if you are unhappy with the results.

Version Update 2.0 (28.06.2024): Added batch normalization, L2 regularization, and Leaky ReLU activation function.

Version Update 1.31 (26.06.2024): Extended metadata.json. Expanded classical regression table.

Version Update 1.3 (25.06.2024): Added validation test with metrics (Validation MAE and MSE). Added R² metrics.

Version Update 1.21 (24.06.2024): Added Mean Absolute Error (MAE) metrics.

Version Update 1.2 (24.06.2024): Added saving and restoring of the best weights (lowest loss). Added early stopping.

Version Update 1.1 (23.06.2024): Added optional training from scratch or fine-tuning. Added an additional dropout layer.

Version Update 1.05 (22.06.2024): Extended metadata saving to include training settings and performance.

Version Update 1.04 (20.06.2024): Enabled deployment of the model as a web app.

Version Update 1.03 (15.06.2024): Added metadata.json.

Version Update 1.02 (13.06.2024): Saving weightSpecs within model.json instead of separately.

Version Update 1.01 (12.06.2024): Added function to upload Excel files.

Architecture: Feedforward Neural Network (Multi-Layer Perceptron) with an input layer for numerical data, followed by two dense layers— the first with units double the number of predictors using ReLU activation, and the second with half the units of the first (minimum of 4), also using ReLU activation, culminating in a single-unit output layer for predictions. Tools: The implementation leverages TensorFlow.js for building, training, and saving the neural network model, TensorFlow.js Visualization (tfjs-vis) for visualizing model performance, PapaParse for parsing CSV files, Math.js for mathematical computations, JStat for classical regression analysis, and JSZip for compressing and managing model files. Incremental Learning: The retrain function allows the model to be updated with new data. Fine-Tuning: When re-training, the model attempts to load previously saved weights, allowing it to fine-tune from the existing state. Privacy: The model supports local data processing to ensure privacy. Data management: Data standardization for training and saving in the downloaded model, and destandardization for predictions. Users can handle missing values through imputation or removal and facilitate outcome and predictor selection.

soundtrack bitwvlf haunting you

1. Press "Add Class" and upload the first image dataset. 

2. Press "Add Class" again to add the next dataset. You need at least two classes to build a model, with an optimal dataset containing more than 50 images.

3. Press the "Train" button. That's all! Your AI is ready to work. If you are not happy with the performance, press the "Retrain" button.

4. You can now classify new images using the "Predict" button.

5. Press "Download Model" to save your model save your model for Deployment as Web Application or retraining with new data. 

6. To use a saved model, press "Upload Model." You can retrain the model by adding new data with the "Choose File" button, followed by the "Retrain" button. For prediction, use the "Predict" button.

P.S. The machine is preset to find the best performing result. Thus, utilize training parameters only if you are unhappy with the results.

Version Update 2.0 (07.07.2024): Switching to the MobileNetV2 layers model as the base, with the last layer being "global_average_pooling2d_1". The trained model is now stored as a combined model (custom + MobileNet base), eliminating the need to upload MobileNet for prediction purposes.

Version Update 1.21 (04.07.2024): Added augmentations: Convert to Grayscale and Add Color Channel Noise.

Version Update 1.2 (03.07.2024): Added a checkbox to choose the type of image augmentation for training.

Version Update 1.1 (26.06.2024): Added early stopping. Added class weights. Optimized the logging process. Extended metadata saving.

Version Update 1.05 (22.06.2024): Extended metadata saving to include training settings and performance.

Version Update 1.03 (20.06.2024): Added metadata.json. Enabled deployment of the model as a web app.

Version Update 1.02 (19.06.2024): Included "Add class" in uploaded models. Added an LR scheduler. Included ELU (Exponential Linear Unit) activation function in one layer to provide diversity in activation functions. Improved normalization.

Version Update 1.01 (18.06.2024): Added: Gaussian Noise, Advanced Activation: LeakyReLU, and additional dense layers

Architecture: Transfer Learning with MobileNetV2 layers model as a base, with the last layer being "global_average_pooling2d_1". Library: TensorFlow.js. Resizing: Images for training and predictions will be resized to 224x224 px using bilinear interpolation. Normalization: Division by 255. Regularization: Dropout to prevent overfitting. Augmentation: Rotation and flip left-right. Validation: Data split for calculation of accuracy and confusion matrix: 85% training, 15% validation. Final model utilizes 100% of the data.

1. Press the 'Left' button to start recording Sample 0. For example, repeat 'Yes.' Press the button again to stop.

2. Press the 'Right' button to start recording Sample 1. For example, repeat 'No.' Press the button again to stop.

3. Press the 'Noise' button to start recording Sample 2. Say nothing; your background noise will be recorded. Press the button again to stop.

4. Press the 'Train' button to train the model. That's all! Your AI is ready to work.

5. After that, you can classify new audio with the 'Listen' button. You can say your sample words (for example, 'Yes' or 'No') and move the circle indicator with your voice to the 'Left' or 'Right'.

Note: You can train not only words but also intonation and mood, such as happiness or anger.

Architecture: Sound classification with @tensorflow-models/speech-command