While designing a Neural Network, we can use Dataset & Data Loader for efficient memory management. Think of them as a backbone for providing data to a machine learning model. Optuna package available in the PyTorch library will be used to calculate the hyper parameters. Agenda of this blog : How we are making the model memory efficient by using Dataset, Data Loader How to use Optuna package to select accurate Hyper Parameters Hyper parameters in a Neural Network : Learning rate Batch size No. of epochs Drop out ratio No. of hidden layers No. of hidden units Normalization - Batch norm etc. Optimizers - Adam, Momentum, NAG, RMS Prop etc. Generally, we manually try above hyper parameters with different reasonable values and try testing a model and finalize the accurate paramters which are best suitable for our model. But manual way takes time as we need to see different possibilities by using different values for these parameters and then come up with correct parameters and values....
Lets understand how to identify the type of flower based on the given input data using Neural Networks. Below are the 3 types of flowers named as: iris setosa iris versicolor iris virginica and each flower have a different area called as petal and sepal, our ML need to predict the flower type based on this data if we provide an unseen data to our ML model once training is done(during testing phase). Feature values that we are considering were : Petal length Petal width Sepal length Sepal width Problem statement : Based on above features, ML model should predict whether the given data belongs to any one of above flower. Before digging into building/programming above neural network we need to understand about one of the important python library called sklearn . Scikit-learn : Scikit-learn is one of the famous machine learning libraries in Python. It is build for Machine ...