Using well-known frameworks like TensorFlow, Keras, or PyTorch is common when training neural networks in Python. Data is first preprocessed and divided into test and training sets. Layers, activation functions, and other parameters are then specified in the neural network architecture. After compiling the model, an optimiser, loss function, and metrics are chosen for assessment. Model.fit() is used to start training, in which the network iteratively changes weights by employing backpropagation to minimise error. Test data is used to assess the model's performance following training. The Python certification course offered by The IoT Academy offers practical experience and necessary abilities for an organised learning process.
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