Feature Selection with Lasso Regression

        Feature selection, a technique in feature engineering, plays a key role in building effective machine learning models. Lasso regression, short for Least Absolute Shrinkage and Selection Operator,  is a useful tool for selecting important features.  It helps reduce model complexity, prevent overfitting, and makes the model easier to understand.
    In this tutorial, we'll go through the steps for using Lasso regression to perform feature selection. This tutorial will cover:

1. Brief Explanation of Lasso
   
2. Preparing the data  
   
3. Training a Baseline Linear Regression Model 
4. Applying Lasso for Feature Selection
5. Evaluating a Model Using Selected Features
6. Conclusion 
7. Full source code listing
   

     Let's get started.

Using ResNet for Image Classification with PyTorch

      In this tutorial, we'll learn about ResNet model and how to use a pre-trained ResNet-50 model for image classification with PyTorch. We'll go through the steps of loading a pre-trained model, preprocessing image, and using the model to predict its class label, as well as displaying the results.The tutorial covers:

1. Introduction to ResNet model
   
2. Load a Pre-Trained ResNet-50 model
3. Define Image Preprocessing
4. Load ImageNet Class Labels
5. Make a Prediction
   
6. Conclusion
7. Full code listing
   

Using VGG for Image Classification with PyTorch

     In this tutorial, we'll learn how to use a pre-trained VGG model for image classification in PyTorch.  We'll go through the steps of loading a pre-trained model, preprocessing image, and using the model to predict its class label, as well as displaying the results.The tutorial covers:

1. Introduction to VGG networks
   
2. Load a Pre-Trained VGG16 Model
3. Define Image Preprocessing
4. Load ImageNet Class Labels
5. Make a Prediction
   
6. Conclusion
7. Full code listing
   

Hyperparameter Tuning of a PyTorch Model with Optuna

       Hyperparameter tuning can significantly improve the performance of machine learning models. In this tutorial, we'll use Optuna library to optimize the hyperparameters of a simple PyTorch neural network model. 

    For demonstration and simplicity, we'll use the Iris dataset for classification and optimize the model's hyperparameters. This tutorial will cover:

1. Introduction to Optuna
2. Preparing the data   
3. Defining the objective function
4. Creating study object and running
   
5. Conclusion
   

     Let's get started.

Hyperparameter Tuning with Grid Search in PyTorch

      Grid search is a technique for optimizing hyperparameters during model training. In this tutorial, I will explain how to use Grid Search to fine-tune the hyperparameters of neural network models in PyTorch. This tutorial will cover:

1. Introduction to Grid Search
2. Implementation and performance check
   
3. Conclusion
   

     Let's get started.

Implementing Learning Rate Schedulers in PyTorch

     In deep learning, optimizing the learning rate is an important for training neural networks effectively. Learning rate schedulers in PyTorch adjust the learning rate during training to improve convergence and performance. This tutorial will guide you through implementing and using various learning rate schedulers in PyTorch. The tutorial covers:

1. Introduction to learning rate
   
2. Setting Up the Environment
3. Initializing the Model, Loss Function, and Optimizer
4. Learning Rate Schedulers 
   
5. Using schedulers in training
6. Implementation and performance check
   
7. Conclusion
   

     Let's get started.