Build Your First PyTorch Model (Linear Regression) скачать в хорошем качестве

Build Your First PyTorch Model (Linear Regression)

🧠 Don’t miss out! Get FREE access to my Skool community — packed with resources, tools, and support to help you with Data, Machine Learning, and AI Automations! 📈 https://www.skool.com/data-and-ai-aut... In this step-by-step tutorial, we dive into the fundamentals of building your first PyTorch model, focusing on Linear Regression. Whether you're new to PyTorch or looking to solidify your understanding, this video is designed to guide beginners through the process of creating a simple yet powerful machine learning model. 🚀 Hire me for Data Work: https://ryanandmattdatascience.com/da... 👨‍💻 Mentorships: https://ryanandmattdatascience.com/me... 📧 Email: ryannolandata@gmail.com 🌐 Website & Blog: https://ryanandmattdatascience.com/ 🖥️ Discord:   / discord   📚 *Practice SQL & Python Interview Questions: https://stratascratch.com/?via=ryan 📖 *SQL and Python Courses: https://datacamp.pxf.io/XYD7Qg 🍿 WATCH NEXT PyTorch for Beginners Playlist:    • PyTorch for Beginners (Deep Learning)   PyTorch Logistic Regression:    • Mastering Logistic Regression in PyTorch: ...   PyToch Multiclass Classification:    • Build an EASY Multiclass Classification Mo...   Want to Learn PyTorch? Start here:    • Want to learn PyTorch? Start here   In this comprehensive PyTorch tutorial, I walk you through building your very first linear regression model from scratch. This is the perfect starting point if you're new to deep learning and PyTorch. I go line-by-line through every step, explaining data preparation, normalization, model architecture, training loops, and making predictions. We start by generating synthetic data and transforming it into tensors, then build a custom LinearRegressionModel class using nn.Module. You'll learn about forward passes, loss functions (MSE), optimizers (SGD), and the backward pass with gradient descent. I also cover essential PyTorch concepts like tensor shapes, data normalization, and denormalization for real predictions. By the end of this video, you'll understand how to create a complete PyTorch training loop, evaluate your model, and visualize results using matplotlib. This is the first video in my new PyTorch series, so subscribe to follow along as we build more complex deep learning models together. If you're transitioning from data analysis to data science or machine learning, this tutorial series is designed specifically for you. TIMESTAMPS 00:00 Introduction to PyTorch Linear Regression 01:00 Importing Libraries & Setup 02:20 Generating Training Data 03:40 Reshaping Data for PyTorch 05:40 Visualizing Initial Data 07:40 Normalizing Data 10:20 Creating Tensors from NumPy Arrays 11:40 Building the Linear Regression Model Class 14:00 Defining Criterion and Optimizer 16:00 Training Loop Setup 18:40 Forward Pass and Loss Calculation 20:40 Backward Pass and Optimization 23:40 Making Predictions with New Data 26:20 Denormalizing Predictions 28:40 Visualizing PyTorch Results 31:00 Full Recap and Explanation OTHER SOCIALS: Ryan’s LinkedIn:   / ryan-p-nolan   Matt’s LinkedIn:   / matt-payne-ceo   Twitter/X: https://x.com/RyanMattDS Who is Ryan Ryan is a Data Scientist at a fintech company, where he focuses on fraud prevention in underwriting and risk. Before that, he worked as a Data Analyst at a tax software company. He holds a degree in Electrical Engineering from UCF. Who is Matt Matt is the founder of Width.ai, an AI and Machine Learning agency. Before starting his own company, he was a Machine Learning Engineer at Capital One. *This is an affiliate program. We receive a small portion of the final sale at no extra cost to you.