Training Neural Networks to Predict Noisy Sinusoidal Waveform with Flask and JavaScript
Hi everyone, in this video, I walk you through a neural network training application built with Flask and JavaScript. We explore how to set up the application, train neural networks on noisy sinusoids, and tweak various parameters to observe their effects on performance. This tool provides insights into optimizing neural network training by adjusting the training set size, hidden layers, noise levels, optimizer, and activation functions. As we proceed, you'll see how different settings impact the loss function and overall convergence of the network. If you have any suggestions for improvement or encounter any issues, please let me know in the comments.
Try the prototype yourself: https://bionichaos.com/Neural_Net
This project utilizes a Flask backend to manage and serve the web application, with JavaScript handling the client-side processing. The neural network is designed to predict noisy sinusoids, using a mean square error loss function and stochastic gradient descent for optimization. The network features two hidden layers with adjustable sizes, and supports various activation functions and optimizers. We examine the effects of different noise levels on training performance and explore the repeatability of results. The application's flexibility allows for in-depth experimentation with hyperparameters, providing valuable insights for machine learning enthusiasts.
The tools I develop are available on https://bionichaos.com
You can support my work on / bionichaos
#NeuralNetworks #MachineLearning #Flask #JavaScript #AI #DeepLearning #NoisySinusoids #Optimization #TechTutorial #DataScience #sinusoidalwaveform
0:00 Introduction to the neural network training application
0:06 Explanation of the parameters and training process
0:15 Training a neural network to predict noisy sinusoids
0:23 Adjusting the training set size
0:32 Observing convergence with different training sizes
0:47 Details about the hidden layers configuration
0:54 Impact of adding noise to the system
1:02 Analyzing performance drop with increased noise
1:12 Tweaking optimizer and activation function
1:25 Effects of noise on training loss
1:39 Reduced noise level and improved convergence
1:49 Final thoughts on the tool's performance
1:59 Explanations of parameter adjustments
2:07 Request for suggestions and ideas for improvement
2:14 Disclaimer about using GPT and Flask
2:27 Details on backend reliance
2:34 Experimenting with hidden layer sizes
2:45 Effects of optimizer and activation function changes
3:08 Issues with sinusoid convergence using RELU
3:14 Tweaking learning rate
3:22 Problems with optimizer causing jumping behavior
3:30 Learning rate adjustments and convergence
3:47 Default settings upon page restart
3:59 Using stochastic gradient descent (SGD)
4:08 Issues with repeatability due to randomness
4:16 Variability in results with each run
4:24 Testing with noise removal
4:34 Slight differences in results
4:47 Convergence issues and troubleshooting
4:58 Setting up Flask application
5:07 Backend considerations and future potential
5:23 Making style adjustments for better layout
5:36 Adjusting CSS for project consistency
5:56 Ensuring responsiveness on different devices
6:01 Simplifying CSS usage
6:17 JavaScript adjustments for better UI
6:28 Testing local deployment issues
6:37 Addressing style inconsistencies
6:52 Local vs. deployed testing
7:01 Closing thoughts and FLASK app details