PyTorch 101 Crash Course For Beginners in 2025!

Want to master PyTorch? This crash course by ML Engineer Daniel Bourke is the most up-to-date PyTorch tutorial on YouTube! If you like this, you’ll LOVE Dan's full course! Dive into advanced topics and complete the FoodVision project 👇 🔥 Full PyTorch Bootcamp Course: https://zerotomastery.io/courses/learn-pytorch/ 🎁 Use YTPYTORCH10 to get 10% OFF 👨‍💻 Source Code: https://github.com/mrdbourke/pytorch-deep-learning 📓 Course Materials: https://www.learnpytorch.io/ 🐍 Free Python 101 Crash Course: https://youtu.be/4uBbCUjJ_G8 ========== ⏰ Timestamps ⏰ Ch. 0 – Fundamentals 00:00 Intro 01:34 What is Deep Learning? 07:38 Why Machine Learning? 11:21 Rule of ML 17:11 ML vs. DL 23:27 Neural Network Anatomy 32:58 Learning Paradigms 37:38 DL Applications 44:10 PyTorch Intro 54:32 Tensors 58:57 Course Overview 1:05:13 Best Practices 1:10:32 Resources 1:16:04 PyTorch Setup 1:23:53 Intro to Tensors 1:37:28 Random Tensors 1:47:36 Zeros & Ones 1:50:55 Ranges 1:56:22 Data Types 2:05:56 Attributes 2:14:29 Operations 2:20:38 Matrix Multiplication Pt. 1 2:30:23 Matrix Multiplication Pt. 2 2:38:24 Shape Errors 2:51:31 Aggregation 2:57:51 Min/Max 3:01:17 Reshaping 3:15:08 Squeeze/Unsqueeze 3:27:13 Indexing 3:36:55 Tensors & NumPy 3:46:13 Reproducibility 3:57:10 Accessing GPUs 4:09:10 Device-Agnostic Code 4:17:03 Exercises Ch. 1 – Workflow 4:22:03 Workflow Intro 4:24:59 Setup 4:32:23 Dataset Creation 4:42:14 Data Splitting 4:50:44 Data Visualization 4:58:39 Linear Model 5:12:59 Model Breakdown 5:19:19 Key PyTorch Classes 5:25:55 Inspect Model 5:35:56 Predictions 5:47:18 Training Intuition 5:55:43 Optimizer Setup 6:08:44 Training Loop 6:22:48 Write Training Loop 6:31:45 Training Steps Review 6:46:52 Run Training Loop 6:56:28 Testing Code 7:08:15 Testing Steps Review 7:23:07 Model Save/Load 7:37:02 Device-Agnostic Practice 7:52:09 Full Workflow: Data 7:58:27 Model 8:08:44 Training 8:21:34 Predictions 8:27:02 Save/Load 8:36:22 Exercises Ch. 2 – Neural Network Classification 8:40:30 Intro 8:50:21 Example 8:59:38 Architecture 9:06:19 Dataset Creation 9:18:47 Splitting Data 9:30:53 Modelling Steps 9:35:22 Small Network 9:46:29 Visualize Model 9:53:37 Using nn.Sequential 10:07:04 Functions Setup 10:22:05 From Logits to Labels 10:38:21 Training Loops 10:53:58 Predictions 11:08:21 Model Improvement 11:16:34 New Model 11:25:50 Test New Model 11:38:45 Straight Line Dataset 11:47:03 Fit Straight Line 11:57:14 Predictions Evaluation 12:02:47 Adding Non-Linearity 12:12:57 Non-Linear Model 12:23:32 Training Non-Linear Model 12:38:55 Evaluate Non-Linear Model 12:44:52 Activation Functions 12:54:37 Multi-Class Dataset 13:06:11 Multi-Class Model 13:18:49 Multi-Class Loss Function 13:25:39 Logits to Labels (Multi-Class) 13:36:51 Train Multi-Class Model 13:53:18 Evaluate Multi-Class Model 14:01:27 Classification Metrics 14:10:54 Exercises Ch. 3 – Computer Vision 14:14:02 Intro 14:26:01 Input/Output Shapes 14:36:19 What is CNN? 14:41:32 CV Libraries 14:51:02 Dataset Overview 15:05:43 Visualizing Samples 15:15:45 DataLoader Overview 15:23:13 DataLoaders Creation 15:35:46 Baseline Model 15:50:35 Loss Function & Optimizer 16:01:14 Timing Code 16:06:59 Training & Testing Loops 16:28:35 Evaluation Function 16:41:43 Device-Agnostic Code 16:45:40 Model 1: Non-Linear 16:54:53 Loss Function 16:58:07 Refactor Training Loop 17:06:46 Refactor Testing Loop 17:13:32 Train Model 1 17:25:35 Model 1 Results 17:29:54 Model 2: CNN Overview 17:38:28 Build CNN 17:58:26 Conv2D Explanation 18:13:36 Layer Shapes 18:27:32 CNN Loss Function 18:30:21 Train CNN 18:38:25 Compare Results 18:45:59 Best Model Predictions 18:57:48 Plot Predictions 19:06:09 Confusion Matrix Setup 19:21:40 Evaluate with Confusion Matrix 19:28:45 Save Best Model 19:40:22 Summary/Exercises Ch. 4 – Custom Datasets 19:46:34 Intro 19:56:38 Device-Agnostic Code 20:02:43 Download Images 20:16:58 Explore Format 20:25:50 Visualize Images 20:37:40 Transform Images 20:42:38 Data Augmentation 21:03:23 ImageFolder Loading 21:12:51 Visualize Loaded Images 21:20:20 DataLoader Creation 21:29:33 Custom Dataset Class 21:37:43 Helper Function 21:47:01 Write Custom Dataset 22:04:57 Dataset Class Comparison 22:12:21 Visualize Custom Dataset 22:26:50 Datasets to DataLoaders 22:33:59 Advanced Augmentation 22:48:33 Baseline Model Overview 22:56:59 Build Tiny VGG 23:08:34 Forward Pass 23:16:54 Torchinfo Summary 23:23:41 Training Functions 23:36:55 Train Model 0 23:47:19 Plot Loss Curves 24:06:36 Overfitting vs. Underfitting 24:20:59 Augmented Datasets 24:32:13 Train Model 1 24:39:34 Compare Loss Curves 24:54:12 Custom Data Predictions 25:38:56 Summary/Exercises Ch. 5 – Going Modular 25:48:17 Intro 26:00:02 Notebook Pt. 1 26:07:52 Dataset Download 26:12:52 Python Script Outline 26:26:53 PyTorch DataLoaders Script 26:37:40 Model Building Script 26:53:35 Save Model Script 26:59:52 Training Script 27:15:49 Summary/Exercises 27:21:58 Final Takeaway Full PyTorch Bootcamp 👉 https://zerotomastery.io/courses/learn-pytorch/