Intermediate Topics in PyTorch
After getting into the basics of PyTorch. Let us now discuss some intermediate topics. These intermediate topics will help you get mastery over the PyTorch and build advanced Machine Learning Models.
Optimizers
Optimizers are algorithms that aims to minimize the loss function. We can use these them to update the parameters of a model during the training process. You might think what parameters may affect the performance of the model. The parameters generally include weights and biases of the neural network. These parameters help us to improve the model’s accuracy. Various Optimizers help us to improve the PyTorch Model which are:
- Stochastic Gradient Descent (SGD): It updates parameters in the direction opposite to the gradient of the loss function with respect to the parameters.
- Adam: It is based on the adaptive learning rate optimization that computes adaptive learning rates for each parameter. It combines the advantages of AdaGrad and RMSProp.
- Adagrad: This algorithm adapts the learning rate of each parameter based on the historical gradients.
Loss Functions
Using Loss Function, we can quantify the difference between the predicted output and of a model and actual target labels. Hence, Loss Functions have to be minimized so that the model can predict the accurate outputs. The following are the most commonly used Loss Function:
- Mean Squared Error (MSE): It calculates the average squared difference between predicted and actual values.
- Cross-Entropy Loss: This measures the dissimilarity between the predicted probability distribution and the actual distribution of class labels.
- Binary Cross-Entropy Loss: This function is the special case of the cross-entropy loss used for binary classification tasks.
- Categorical Cross-Entropy Loss: This loss function calculates the cross-entropy loss between the predicted class probabilities and the one-hot encoded target labels.
Start learning PyTorch for Beginners
Machine Learning helps us to extract meaningful insights from the data. But now, it is capable of mimicking the human brain. This is done using neural networks, which contain the various interconnected layers of nodes containing the data. This data is passed to forward layers. Subsequently, the model learns from the data and predicts output for the new data.
PyTorch helps us to create and train these neural networks that act like our brains and learn from the data.
Table of Content
- What is Pytorch?
- Why use PyTorch?
- How to install Pytorch ?
- PyTorch Basics
- Autograd: Automatic Differentiation in PyTorch
- Neural Networks in PyTorch
- Working with Data in PyTorch
- Intermediate Topics in PyTorch
- Validation and Testing
- Frequently Asked Questions
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