MNIST Image Classification with PyTorch

    In this tutorial, we'll learn how to build a convolutional neural network (CNN) using PyTorch to classify handwritten digits from the MNIST dataset. The MNIST dataset consists of 28x28 pixel grayscale images of handwritten digits (0-9), and the task is to correctly identify which digit is represented in each image. The tutorial covers:

  1. Preparing data
  2. Model definition
  3. Model training
  4. Model evaluation
  5. Prediction
  6. Conclusion

Understanding PyTorch Autograd

    Autograd is a key component for implementing  automatic differentiation. It allows us to compute gradients automatically for tensor operations, which is crucial for training neural networks efficiently using techniques like backpropagation. This tutorial will provide an overview of PyTorch Autograd, covering the following topics:

  1. Introduction to Autograd
  2. Autograd in model training
  3. Conclusion 

     Let's get started.

Data Loading in PyTorch with DataLoader

    In PyTorch, a DataLoader is a tool that efficiently manages and loads data during the training or evaluation of machine learning models. It acts as a bridge between datasets and models, facilitating seamless data handling throughout the process. In this tutorial, we'll explore how to utilize PyTorch's DataLoader with synthetic and classical MNIST datasets, covering the following topics:

  1. Understanding DataLoader
  2. Usage with simple data
  3. Usage with MNIST Dataset
  4. Conclusion 

     Let's get started.

Binary Classification with Logistic Regression using PyTorch

     Logistic regression is a fundamental machine learning algorithm used for binary classification tasks. In this tutorial, we'll explore how to classify binary data with logistic regression using PyTorch deep learning framework. We'll cover the following topics:

  1. Introduction to logistic regression
  2. Preparing data
  3. Building the classifier model
  4. Training the model
  5. Prediction and accuracy check
  6. Conclusion 
  7. Source code listing

     Let's get started.

Classification Example with PyTorch

     Classification tasks are fundamental in machine learning, involving the categorization of input data into distinct classes or categories based on their features. In this tutorial, we'll learn how to implement data classification using PyTorch deep learning framework.

    We'll cover the following topics:

  1. Introduction to classification
  2. Preparing data
  3. Building the classifier model
  4. Training the model
  5. Prediction and accuracy check
  6. Conclusion 
  7. Source code listing

     Let's get started.

Linear Regression with PyTorch

      Linear regression is a fundamental supervised learning technique used for predicting a continuous target variable based on one or more input features. In this tutorial, we'll learn how to implement linear regression using PyTorch deep learning framework. We'll cover the following topics:

  1. Introduction to linear regression 
  2. Preparing data
  3. Building the linear regression model
  4. Training the model
  5. Evaluating the model
  6. Conclusion 
  7. Source code listing

     Let's get started.