3.6C Uses of artificial neural networks

Speech Recognition

• Converts spoken language into text.

• Translates written text into speech.

• Identifies speakers based on their voice.

• Records audio for later playback.

Output Layer

• Final layer where the result or prediction is given.

• Initial layer that processes input data.

• Layer that adjusts weights during training.

• Layer that combines features from previous layers.

Stock Market Prediction

• Forecasts stock movements using historical data.

• Analyzes current market trends without historical context.

• Predicts stock prices based on social media sentiment.

• Evaluates company performance through financial statements.

Autonomous Vehicles

• Use ANNs to interpret sensor data for navigation.

• Rely solely on GPS for movement.

• Require constant human supervision.

• Utilize traditional driving techniques.

Hidden Layer

• Layer that receives input directly from the output layer.

• Layer that processes features between input and output.

• Layer that connects the input layer to the output layer without processing.

• Layer that stores data temporarily before processing.

Neural Net Training

• The process of feeding data to a neural network to improve accuracy.

• The method of designing neural network architectures.

• The technique of optimizing hyperparameters in machine learning.

• The evaluation of a neural network's performance on unseen data.

Forward Propagation

• Data moves from input to output layer.

• Data moves from output to input layer.

• Data is stored in the hidden layer.

• Data is randomly shuffled during processing.

Deep Neural Network

• A neural network with multiple hidden layers.

• A type of neural network with a single layer.

• A neural network that only processes images.

• A neural network designed for reinforcement learning.

Bias

• An additional parameter that adjusts the output along with weights.

• A type of error that occurs in statistical analysis.

• A method for normalizing data in machine learning.

• A technique for increasing the complexity of a model.

Art and Music Generation

• Neural networks generate art, music, and poetry.

• Only humans can create art and music.

• Art and music can only be generated by traditional methods.

• Neural networks are used solely for data analysis.