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.

Text Classification with BERT in PyTorch

       Text classification is a fundamental task in NLP that involves categorizing text into predefined categories or labels. With the advent of deep learning and transformer-based models like BERT (Bidirectional Encoder Representations from Transformers), text classification has witnessed significant advancements in accuracy and performance. 

    In this tutorial, we will explore how to perform text classification using BERT in PyTorch, covering data preparation, model training, and prediction. The tutorial covers:

  1. Preparing data for text classification
  2. Overview of BERT
  3. Tokeniziation and encoding
  4. Loading the pre-trained BERT model
  5. Training (fine-tuning) the model
  6. Making predictions on new sentences
  7. Conclusion 
  8. Source code listing

     Let's get started.

Sentiment Analysis with BERT in PyTorch

      Sentiment analysis involves determining the sentiment (positive, negative, or neutral) expressed in a piece of text, making it a valuable tool for understanding user opinions, customer feedback, and social media sentiment. In this tutorial, we'll explore how to perform sentiment analysis using BERT (Bidirectional Encoder Representations from Transformers), one of the most powerful models in NLP.

  1. Loading the Pre-trained BERT Model
  2. Performing Sentiment Analysis
  3. Conclusion 
  4. Source code listing

     Let's get started.