Build a Logistic Regression Model in PyTorch | 2024
Description:
Welcome to our channel! In this video, we’ll walk you through the process of building a Logistic Regression model using PyTorch, one of the most popular deep learning frameworks.
What You’ll Learn:
Understanding the fundamentals of Logistic Regression
Creating a synthetic dataset for binary classification
Building and training a Logistic Regression model in PyTorch
Evaluating model performance using accuracy
Visualizing the model architecture with torchviz
Topics Covered:
Introduction to Logistic Regression
Data generation and preprocessing
Implementing a Logistic Regression model in PyTorch
Training loop: forward pass, loss calculation, and backpropagation
Using Binary Cross-Entropy loss for model training
Visualizing the model graph
Who Is This Tutorial For? This tutorial is designed for beginners who want to learn about machine learning and deep learning using PyTorch. Whether you're a student, a developer, or just curious about AI, this step-by-step guide will help you get started!
Prerequisites:
Basic understanding of Python
Familiarity with PyTorch is a plus, but not required
Resources:
PyTorch Documentation
Keywords: Logistic Regression, PyTorch, Machine Learning, Deep Learning, Binary Classification, Neural Networks, Data Science, AI Tutorial, Python, torchviz, Binary Cross-Entropy, Model Visualization, Step-by-Step Guide
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