Building a Neural Network

Requirements Clarification & Assessment

1. Define the Problem:

   • Clearly articulate the problem you aim to solve with the neural network, such as classification, regression, or clustering.
   • Identify the expected input data and the desired output.
   • Determine if the problem is supervised, unsupervised, or semi-supervised.

2. Data Collection:

   • Gather a comprehensive dataset that is representative of the problem domain.
   • Ensure data diversity to capture all potential scenarios the model will encounter.

3. Preprocessing Needs:

   • Assess the quality of the data: Check for missing values, outliers, and data imbalance.
   • Determine the need for normalization or standardization.
   • Identify categorical variables that require encoding.

4. Model Requirements:

   • Decide on the appropriate neural network architecture: Feedforward, Convolutional, Recurrent, etc.
   • Establish performance metrics and loss functions relevant to the problem.

5. Hardware and Software Constraints:

   • Evaluate computational resources available for training and deployment.
   • Choose a suitable framework (TensorFlow, PyTorch, Keras) based on team expertise and project requirements.