Neural networks frequently asked questions

Neural networks frequently asked questions

What types of neural networks can be implemented?

Sigma Magic allows users to build Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) for different use cases like classification, regression, and time-series forecasting.
How does Sigma Magic optimize neural networks during training?

Sigma Magic supports gradient descent optimization techniques like Stochastic Gradient Descent (SGD), Adam, RMSprop, and Momentum-based methods to improve learning efficiency.
What loss functions are available for training neural network?

For classification: Cross-Entropy Loss

For regression: Mean Squared Error (MSE), Mean Absolute Error (MAE)
How do I improve neural network performance?
You can optimize hyperparameters, increase dataset size, apply data augmentation, use deeper architectures, and fine-tune learning rates.
How do I prevent overfitting while training a neural network?

You can use regularization techniques like L1/L2 (Ridge & Lasso), dropout layers, early stopping, and data augmentation to prevent overfitting in neural networks.
What are applications of neural networks?
  • Image Recognition (Face detection, medical imaging)
  • Natural Language Processing (NLP) (Chatbots, language translation)
  • Financial Forecasting (Stock prediction, fraud detection)
  • Autonomous Vehicles (Self-driving cars)
  • Robotics (AI-powered automation)
What activation functions are supported in Sigma Magic for neural networks?

Sigma Magic supports activation functions such as ReLU (Rectified Linear Unit), Sigmoid, Tanh, Softmax, and Leaky ReLU, depending on the neural network layer type.

Reference: Some of the text in this article has been generated using AI tools such as ChatGPT and edited for content and accuracy.

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