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Language:
English
Total Size:
29.7 GB
Info Hash:
AA903D0091C7B89352F43773CCCD1D86586998E3
Added By:
Added:
Aug. 22, 2023, 8:21 a.m.
Stats:
|
(Last updated: May 21, 2025, 6:50 p.m.)
| File | Size |
|---|---|
| [CourseClub.Me].url | 122 bytes |
| [FreeCourseSite.com].url | 127 bytes |
| [GigaCourse.Com].url | 49 bytes |
| 1. PyTorch for Deep Learning.mp4 | 75.3 MB |
| 1. PyTorch for Deep Learning.srt | 5.2 KB |
| 2. Course Welcome and What Is Deep Learning.mp4 | 39.0 MB |
| 2. Course Welcome and What Is Deep Learning.srt | 8.6 KB |
| 3. Join Our Online Classroom!.mp4 | 75.3 MB |
| 3. Join Our Online Classroom!.srt | 6.0 KB |
| 4. Exercise Meet Your Classmates + Instructor.html | 3.8 KB |
| 5. Free Course Book + Code Resources + Asking Questions + Getting Help.html | 2.4 KB |
| 6. ZTM Resources.mp4 | 44.6 MB |
| 6. ZTM Resources.srt | 6.3 KB |
| 6.1 LinkedIn Group.html | 102 bytes |
| 6.2 zerotomastery.io.html | 86 bytes |
| 6.3 ZTM Youtube.html | 99 bytes |
| 7. Machine Learning + Python Monthly Newsletters.html | 2.0 KB |
| 1. What Is a Machine Learning Research Paper.mp4 | 93.9 MB |
| 1. What Is a Machine Learning Research Paper.srt | 11.7 KB |
| 10. Breaking Down Figure 1 of the ViT Paper.mp4 | 87.1 MB |
| 10. Breaking Down Figure 1 of the ViT Paper.srt | 16.9 KB |
| 11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.mp4 | 140.9 MB |
| 11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.srt | 16.2 KB |
| 12. Breaking Down Equation 1.mp4 | 103.2 MB |
| 12. Breaking Down Equation 1.srt | 12.0 KB |
| 13. Breaking Down Equation 2 and 3.mp4 | 125.0 MB |
| 13. Breaking Down Equation 2 and 3.srt | 14.8 KB |
| 14. Breaking Down Equation 4.mp4 | 92.4 MB |
| 14. Breaking Down Equation 4.srt | 10.1 KB |
| 15. Breaking Down Table 1.mp4 | 122.1 MB |
| 15. Breaking Down Table 1.srt | 15.1 KB |
| 16. Calculating the Input and Output Shape of the Embedding Layer by Hand.mp4 | 160.6 MB |
| 16. Calculating the Input and Output Shape of the Embedding Layer by Hand.srt | 20.6 KB |
| 17. Turning a Single Image into Patches (Part 1 Patching the Top Row).mp4 | 150.2 MB |
| 17. Turning a Single Image into Patches (Part 1 Patching the Top Row).srt | 20.3 KB |
| 18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).mp4 | 130.7 MB |
| 18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).srt | 16.2 KB |
| 19. Creating Patch Embeddings with a Convolutional Layer.mp4 | 142.6 MB |
| 19. Creating Patch Embeddings with a Convolutional Layer.srt | 18.6 KB |
| 2. Why Replicate a Machine Learning Research Paper.mp4 | 23.3 MB |
| 2. Why Replicate a Machine Learning Research Paper.srt | 4.9 KB |
| 20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.mp4 | 129.1 MB |
| 20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.srt | 17.9 KB |
| 21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.mp4 | 89.6 MB |
| 21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.srt | 13.2 KB |
| 22. Visualizing a Single Sequence Vector of Patch Embeddings.mp4 | 50.4 MB |
| 22. Visualizing a Single Sequence Vector of Patch Embeddings.srt | 6.9 KB |
| 23. Creating the Patch Embedding Layer with PyTorch.mp4 | 170.0 MB |
| 23. Creating the Patch Embedding Layer with PyTorch.srt | 22.8 KB |
| 24. Creating the Class Token Embedding.mp4 | 132.0 MB |
| 24. Creating the Class Token Embedding.srt | 17.5 KB |
| 25. Creating the Class Token Embedding - Less Birds.mp4 | 131.9 MB |
| 25. Creating the Class Token Embedding - Less Birds.srt | 17.7 KB |
| 26. Creating the Position Embedding.mp4 | 109.2 MB |
| 26. Creating the Position Embedding.srt | 16.7 KB |
| 27. Equation 1 Putting it All Together.mp4 | 134.8 MB |
| 27. Equation 1 Putting it All Together.srt | 18.5 KB |
| 28. Equation 2 Multihead Attention Overview.mp4 | 144.1 MB |
| 28. Equation 2 Multihead Attention Overview.srt | 21.6 KB |
| 29. Equation 2 Layernorm Overview.mp4 | 111.7 MB |
| 29. Equation 2 Layernorm Overview.srt | 12.8 KB |
| 3. Where Can You Find Machine Learning Research Papers and Code.mp4 | 110.8 MB |
| 3. Where Can You Find Machine Learning Research Papers and Code.srt | 13.3 KB |
| 30. Turning Equation 2 into Code.mp4 | 163.9 MB |
| 30. Turning Equation 2 into Code.srt | 20.8 KB |
| 31. Checking the Inputs and Outputs of Equation.mp4 | 53.7 MB |
| 31. Checking the Inputs and Outputs of Equation.srt | 7.8 KB |
| 32. Equation 3 Replication Overview.mp4 | 88.7 MB |
| 32. Equation 3 Replication Overview.srt | 12.2 KB |
| 33. Turning Equation 3 into Code.mp4 | 107.1 MB |
| 33. Turning Equation 3 into Code.srt | 14.9 KB |
| 34. Transformer Encoder Overview.mp4 | 82.9 MB |
| 34. Transformer Encoder Overview.srt | 10.8 KB |
| 35. Combining equation 2 and 3 to Create the Transformer Encoder.mp4 | 84.9 MB |
| 35. Combining equation 2 and 3 to Create the Transformer Encoder.srt | 12.7 KB |
| 36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.mp4 | 188.7 MB |
| 36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.srt | 21.3 KB |
| 37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces.mp4 | 190.8 MB |
| 37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces.srt | 26.3 KB |
| 38. Bringing Our Own Vision Transformer to Life - Part 2 The Forward Method.mp4 | 111.4 MB |
| 38. Bringing Our Own Vision Transformer to Life - Part 2 The Forward Method.srt | 14.9 KB |
| 39. Getting a Visual Summary of Our Custom Vision Transformer.mp4 | 84.9 MB |
| 39. Getting a Visual Summary of Our Custom Vision Transformer.srt | 10.9 KB |
| 4. What We Are Going to Cover.mp4 | 87.8 MB |
| 4. What We Are Going to Cover.srt | 13.1 KB |
| 40. Creating a Loss Function and Optimizer from the ViT Paper.mp4 | 118.3 MB |
| 40. Creating a Loss Function and Optimizer from the ViT Paper.srt | 16.2 KB |
| 41. Training our Custom ViT on Food Vision Mini.mp4 | 53.5 MB |
| 41. Training our Custom ViT on Food Vision Mini.srt | 7.0 KB |
| 42. Discussing what Our Training Setup Is Missing.mp4 | 101.2 MB |
| 42. Discussing what Our Training Setup Is Missing.srt | 12.7 KB |
| 43. Plotting a Loss Curve for Our ViT Model.mp4 | 63.4 MB |
| 43. Plotting a Loss Curve for Our ViT Model.srt | 8.7 KB |
| 44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.mp4 | 164.7 MB |
| 44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.srt | 19.9 KB |
| 45. Preparing Data to Be Used with a Pretrained ViT.mp4 | 57.2 MB |
| 45. Preparing Data to Be Used with a Pretrained ViT.srt | 7.2 KB |
| 46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.mp4 | 76.3 MB |
| 46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.srt | 10.3 KB |
| 47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.mp4 | 40.4 MB |
| 47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.srt | 6.4 KB |
| 48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.mp4 | 41.8 MB |
| 48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.srt | 5.5 KB |
| 49. Making Predictions on a Custom Image with Our Pretrained ViT.mp4 | 37.1 MB |
| 49. Making Predictions on a Custom Image with Our Pretrained ViT.srt | 5.1 KB |
| 5. Getting Setup for Coding in Google Colab.mp4 | 99.1 MB |
| 5. Getting Setup for Coding in Google Colab.srt | 11.9 KB |
| 50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.mp4 | 85.5 MB |
| 50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.srt | 10.7 KB |
| 6. Downloading Data for Food Vision Mini.mp4 | 43.8 MB |
| 6. Downloading Data for Food Vision Mini.srt | 6.2 KB |
| 7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.mp4 | 89.7 MB |
| 7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.srt | 13.7 KB |
| 8. Visualizing a Single Image.mp4 | 36.4 MB |
| 8. Visualizing a Single Image.srt | 5.3 KB |
| 9. Replicating a Vision Transformer - High Level Overview.mp4 | 77.8 MB |
| 9. Replicating a Vision Transformer - High Level Overview.srt | 13.6 KB |
| 1. What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.mp4 | 73.8 MB |
| 1. What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.srt | 14.2 KB |
| 10. Creating an EffNetB2 Feature Extractor Model.mp4 | 92.1 MB |
| 10. Creating an EffNetB2 Feature Extractor Model.srt | 13.1 KB |
| 11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.mp4 | 57.6 MB |
| 11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.srt | 8.8 KB |
| 12. Creating DataLoaders for EffNetB2.mp4 | 31.4 MB |
| 12. Creating DataLoaders for EffNetB2.srt | 4.7 KB |
| 13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.mp4 | 97.0 MB |
| 13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.srt | 13.9 KB |
| 14. Saving Our EffNetB2 Model to File.mp4 | 26.7 MB |
| 14. Saving Our EffNetB2 Model to File.srt | 4.3 KB |
| 15. Getting the Size of Our EffNetB2 Model in Megabytes.mp4 | 55.5 MB |
| 15. Getting the Size of Our EffNetB2 Model in Megabytes.srt | 7.1 KB |
| 16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.mp4 | 63.3 MB |
| 16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.srt | 8.9 KB |
| 17. Creating a Vision Transformer Feature Extractor Model.mp4 | 78.5 MB |
| 17. Creating a Vision Transformer Feature Extractor Model.srt | 10.5 KB |
| 18. Creating DataLoaders for Our ViT Feature Extractor Model.mp4 | 19.7 MB |
| 18. Creating DataLoaders for Our ViT Feature Extractor Model.srt | 3.8 KB |
| 19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.mp4 | 62.0 MB |
| 19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.srt | 9.4 KB |
| 2. Three Questions to Ask for Machine Learning Model Deployment.mp4 | 46.9 MB |
| 2. Three Questions to Ask for Machine Learning Model Deployment.srt | 11.6 KB |
| 20. Saving Our ViT Feature Extractor and Inspecting Its Size.mp4 | 43.8 MB |
| 20. Saving Our ViT Feature Extractor and Inspecting Its Size.srt | 6.7 KB |
| 21. Collecting Stats About Our-ViT Feature Extractor.mp4 | 45.8 MB |
| 21. Collecting Stats About Our-ViT Feature Extractor.srt | 8.5 KB |
| 22. Outlining the Steps for Making and Timing Predictions for Our Models.mp4 | 93.4 MB |
| 22. Outlining the Steps for Making and Timing Predictions for Our Models.srt | 14.0 KB |
| 23. Creating a Function to Make and Time Predictions with Our Models.mp4 | 185.8 MB |
| 23. Creating a Function to Make and Time Predictions with Our Models.srt | 24.1 KB |
| 24. Making and Timing Predictions with EffNetB2.mp4 | 97.6 MB |
| 24. Making and Timing Predictions with EffNetB2.srt | 13.7 KB |
| 25. Making and Timing Predictions with ViT.mp4 | 72.5 MB |
| 25. Making and Timing Predictions with ViT.srt | 9.7 KB |
| 26. Comparing EffNetB2 and ViT Model Statistics.mp4 | 89.6 MB |
| 26. Comparing EffNetB2 and ViT Model Statistics.srt | 14.4 KB |
| 27. Visualizing the Performance vs Speed Trade-off.mp4 | 134.7 MB |
| 27. Visualizing the Performance vs Speed Trade-off.srt | 21.6 KB |
| 28. Gradio Overview and Installation.mp4 | 95.1 MB |
| 28. Gradio Overview and Installation.srt | 13.1 KB |
| 29. Gradio Function Outline.mp4 | 79.9 MB |
| 29. Gradio Function Outline.srt | 11.5 KB |
| 3. Where Is My Model Going to Go.mp4 | 139.8 MB |
| 3. Where Is My Model Going to Go.srt | 21.4 KB |
| 30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.mp4 | 95.2 MB |
| 30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.srt | 13.8 KB |
| 31. Creating a List of Examples to Pass to Our Gradio Demo.mp4 | 53.3 MB |
| 31. Creating a List of Examples to Pass to Our Gradio Demo.srt | 6.8 KB |
| 32. Bringing Food Vision Mini to Life in a Live Web Application.mp4 | 135.4 MB |
| 32. Bringing Food Vision Mini to Life in a Live Web Application.srt | 18.7 KB |
| 33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.mp4 | 64.8 MB |
| 33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.srt | 8.6 KB |
| 34. Outlining the File Structure of Our Deployed App.mp4 | 89.5 MB |
| 34. Outlining the File Structure of Our Deployed App.srt | 11.0 KB |
| 35. Creating a Food Vision Mini Demo Directory to House Our App Files.mp4 | 39.1 MB |
| 35. Creating a Food Vision Mini Demo Directory to House Our App Files.srt | 5.7 KB |
| 36. Creating an Examples Directory with Example Food Vision Mini Images.mp4 | 92.4 MB |
| 36. Creating an Examples Directory with Example Food Vision Mini Images.srt | 12.9 KB |
| 37. Writing Code to Move Our Saved EffNetB2 Model File.mp4 | 71.9 MB |
| 37. Writing Code to Move Our Saved EffNetB2 Model File.srt | 10.1 KB |
| 38. Turning Our EffNetB2 Model Creation Function Into a Python Script.mp4 | 44.8 MB |
| 38. Turning Our EffNetB2 Model Creation Function Into a Python Script.srt | 5.3 KB |
| 39. Turning Our Food Vision Mini Demo App Into a Python Script.mp4 | 137.6 MB |
| 39. Turning Our Food Vision Mini Demo App Into a Python Script.srt | 18.7 KB |
| 4. How Is My Model Going to Function.mp4 | 67.4 MB |
| 4. How Is My Model Going to Function.srt | 12.2 KB |
| 40. Creating a Requirements File for Our Food Vision Mini App.mp4 | 37.5 MB |
| 40. Creating a Requirements File for Our Food Vision Mini App.srt | 6.2 KB |
| 41. Downloading Our Food Vision Mini App Files from Google Colab.mp4 | 112.2 MB |
| 41. Downloading Our Food Vision Mini App Files from Google Colab.srt | 16.2 KB |
| 42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.mp4 | 143.6 MB |
| 42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.srt | 20.8 KB |
| 43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.mp4 | 91.6 MB |
| 43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.srt | 12.5 KB |
| 44. Food Vision Big Project Outline.mp4 | 39.1 MB |
| 44. Food Vision Big Project Outline.srt | 5.6 KB |
| 45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.mp4 | 96.5 MB |
| 45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.srt | 13.5 KB |
| 46. Downloading the Food 101 Dataset.mp4 | 71.7 MB |
| 46. Downloading the Food 101 Dataset.srt | 11.0 KB |
| 47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.mp4 | 119.7 MB |
| 47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.srt | 18.0 KB |
| 48. Turning Our Food 101 Datasets into DataLoaders.mp4 | 61.5 MB |
| 48. Turning Our Food 101 Datasets into DataLoaders.srt | 9.6 KB |
| 49. Training Food Vision Big Our Biggest Model Yet!.mp4 | 184.2 MB |
| 49. Training Food Vision Big Our Biggest Model Yet!.srt | 28.0 KB |
| 5. Some Tools and Places to Deploy Machine Learning Models.mp4 | 65.4 MB |
| 5. Some Tools and Places to Deploy Machine Learning Models.srt | 8.8 KB |
| 50. Outlining the File Structure for Our Food Vision Big.mp4 | 52.8 MB |
| 50. Outlining the File Structure for Our Food Vision Big.srt | 8.2 KB |
| 51. Downloading an Example Image and Moving Our Food Vision Big Model File.mp4 | 36.6 MB |
| 51. Downloading an Example Image and Moving Our Food Vision Big Model File.srt | 5.2 KB |
| 52. Saving Food 101 Class Names to a Text File and Reading them Back In.mp4 | 66.8 MB |
| 52. Saving Food 101 Class Names to a Text File and Reading them Back In.srt | 9.3 KB |
| 53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.mp4 | 23.9 MB |
| 53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.srt | 3.2 KB |
| 54. Creating an App Script for Our Food Vision Big Model Gradio Demo.mp4 | 104.8 MB |
| 54. Creating an App Script for Our Food Vision Big Model Gradio Demo.srt | 14.6 KB |
| 55. Zipping and Downloading Our Food Vision Big App Files.mp4 | 39.8 MB |
| 55. Zipping and Downloading Our Food Vision Big App Files.srt | 5.2 KB |
| 56. Deploying Food Vision Big to Hugging Face Spaces.mp4 | 162.5 MB |
| 56. Deploying Food Vision Big to Hugging Face Spaces.srt | 19.7 KB |
| 57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.mp4 | 81.8 MB |
| 57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.srt | 9.4 KB |
| 6. What We Are Going to Cover.mp4 | 40.8 MB |
| 6. What We Are Going to Cover.srt | 7.2 KB |
| 7. Getting Setup to Code.mp4 | 62.9 MB |
| 7. Getting Setup to Code.srt | 8.8 KB |
| 8. Downloading a Dataset for Food Vision Mini.mp4 | 39.3 MB |
| 8. Downloading a Dataset for Food Vision Mini.srt | 4.8 KB |
| 9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.mp4 | 58.6 MB |
| 9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.srt | 10.8 KB |
| [CourseClub.Me].url | 122 bytes |
| [FreeCourseSite.com].url | 127 bytes |
| [GigaCourse.Com].url | 49 bytes |
| 1. Introduction to PyTorch 2.0.mp4 | 82.2 MB |
| 1. Introduction to PyTorch 2.0.srt | 8.5 KB |
| 10. Creating a Function to Setup Our Model and Transforms.mp4 | 99.6 MB |
| 10. Creating a Function to Setup Our Model and Transforms.srt | 14.3 KB |
| 11. Discussing How to Get Better Relative Speedups for Training Models.mp4 | 70.1 MB |
| 11. Discussing How to Get Better Relative Speedups for Training Models.srt | 10.3 KB |
| 12. Setting the Batch Size and Data Size Programmatically.mp4 | 71.0 MB |
| 12. Setting the Batch Size and Data Size Programmatically.srt | 10.0 KB |
| 13. Getting More Potential Speedups with TensorFloat-32.mp4 | 83.8 MB |
| 13. Getting More Potential Speedups with TensorFloat-32.srt | 13.6 KB |
| 14. Downloading the CIFAR10 Dataset.mp4 | 67.6 MB |
| 14. Downloading the CIFAR10 Dataset.srt | 10.2 KB |
| 15. Creating Training and Test DataLoaders.mp4 | 67.8 MB |
| 15. Creating Training and Test DataLoaders.srt | 10.9 KB |
| 16. Preparing Training and Testing Loops with Timing Steps for PyTorch 2.0 timing.mp4 | 60.7 MB |
| 16. Preparing Training and Testing Loops with Timing Steps for PyTorch 2.0 timing.srt | 7.1 KB |
| 17. Experiment 1 - Single Run without torch.compile.mp4 | 78.1 MB |
| 17. Experiment 1 - Single Run without torch.compile.srt | 12.8 KB |
| 18. Experiment 2 - Single Run with torch.compile.mp4 | 105.6 MB |
| 18. Experiment 2 - Single Run with torch.compile.srt | 15.1 KB |
| 19. Comparing the Results of Experiment 1 and 2.mp4 | 120.6 MB |
| 19. Comparing the Results of Experiment 1 and 2.srt | 15.5 KB |
| 2. What We Are Going to Cover and PyTorch 2 Reference Materials.mp4 | 15.1 MB |
| 2. What We Are Going to Cover and PyTorch 2 Reference Materials.srt | 2.3 KB |
| 2.1 PyTorch 2.0 tutorial on learnpytorch.io.html | 105 bytes |
| 20. Saving the Results of Experiment 1 and 2.mp4 | 58.0 MB |
| 20. Saving the Results of Experiment 1 and 2.srt | 6.6 KB |
| 21. Preparing Functions for Experiment 3 and 4.mp4 | 116.3 MB |
| 21. Preparing Functions for Experiment 3 and 4.srt | 17.7 KB |
| 22. Experiment 3 - Training a Non-Compiled Model for Multiple Runs.mp4 | 132.8 MB |
| 22. Experiment 3 - Training a Non-Compiled Model for Multiple Runs.srt | 16.6 KB |
| 23. Experiment 4 - Training a Compiled Model for Multiple Runs.mp4 | 105.0 MB |
| 23. Experiment 4 - Training a Compiled Model for Multiple Runs.srt | 14.0 KB |
| 24. Comparing the Results of Experiment 3 and 4.mp4 | 62.8 MB |
| 24. Comparing the Results of Experiment 3 and 4.srt | 8.1 KB |
| 25. Potential Extensions and Resources to Learn More.mp4 | 64.1 MB |
| 25. Potential Extensions and Resources to Learn More.srt | 8.9 KB |
| 3. Getting Started with PyTorch 2 in Google Colab.mp4 | 44.6 MB |
| 3. Getting Started with PyTorch 2 in Google Colab.srt | 6.5 KB |
| 3.1 PyTorch 2.0 tutorial on learnpytorch.io.html | 105 bytes |
| 4. PyTorch 2.0 - 30 Second Intro.mp4 | 22.4 MB |
| 4. PyTorch 2.0 - 30 Second Intro.srt | 4.9 KB |
| 5. Getting Setup for PyTorch 2.mp4 | 27.1 MB |
| 5. Getting Setup for PyTorch 2.srt | 3.3 KB |
| 6. Getting Info from Our GPUs and Seeing if They're Capable of Using PyTorch 2.mp4 | 77.5 MB |
| 6. Getting Info from Our GPUs and Seeing if They're Capable of Using PyTorch 2.srt | 8.9 KB |
| 7. Setting the Default Device in PyTorch 2.mp4 | 103.0 MB |
| 7. Setting the Default Device in PyTorch 2.srt | 13.9 KB |
| 8. Discussing the Experiments We Are Going to Run for PyTorch 2.mp4 | 57.6 MB |
| 8. Discussing the Experiments We Are Going to Run for PyTorch 2.srt | 9.5 KB |
| 9. Introduction to PyTorch 2.mp4 | 82.1 MB |
| 9. Introduction to PyTorch 2.srt | 8.5 KB |
| 1. Special Bonus Lecture.html | 1.2 KB |
| 1. Thank You!.mp4 | 21.0 MB |
| 1. Thank You!.srt | 1.8 KB |
| 2. Become An Alumni.html | 921 bytes |
| 3. Endorsements on LinkedIn.html | 1.4 KB |
| 4. Learning Guideline.html | 353 bytes |
| 1. Why Use Machine Learning or Deep Learning.mp4 | 13.8 MB |
| 1. Why Use Machine Learning or Deep Learning.srt | 6.2 KB |
| 10. How To and How Not To Approach This Course.mp4 | 37.7 MB |
| 10. How To and How Not To Approach This Course.srt | 8.6 KB |
| 11. Important Resources For This Course.mp4 | 58.3 MB |
| 11. Important Resources For This Course.srt | 8.7 KB |
| 12. Getting Setup to Write PyTorch Code.mp4 | 70.0 MB |
| 12. Getting Setup to Write PyTorch Code.srt | 11.8 KB |
| 13. Introduction to PyTorch Tensors.mp4 | 94.0 MB |
| 13. Introduction to PyTorch Tensors.srt | 20.1 KB |
| 14. Creating Random Tensors in PyTorch.mp4 | 86.4 MB |
| 14. Creating Random Tensors in PyTorch.srt | 14.3 KB |
| 15. Creating Tensors With Zeros and Ones in PyTorch.mp4 | 24.6 MB |
| 15. Creating Tensors With Zeros and Ones in PyTorch.srt | 4.5 KB |
| 16. Creating a Tensor Range and Tensors Like Other Tensors.mp4 | 32.6 MB |
| 16. Creating a Tensor Range and Tensors Like Other Tensors.srt | 7.1 KB |
| 17. Dealing With Tensor Data Types.mp4 | 81.4 MB |
| 17. Dealing With Tensor Data Types.srt | 12.7 KB |
| 18. Getting Tensor Attributes.mp4 | 66.4 MB |
| 18. Getting Tensor Attributes.srt | 11.6 KB |
| 19. Manipulating Tensors (Tensor Operations).mp4 | 39.7 MB |
| 19. Manipulating Tensors (Tensor Operations).srt | 8.2 KB |
| 2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.mp4 | 35.3 MB |
| 2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.srt | 9.5 KB |
| 20. Matrix Multiplication (Part 1).mp4 | 77.8 MB |
| 20. Matrix Multiplication (Part 1).srt | 12.7 KB |
| 21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.mp4 | 57.8 MB |
| 21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.srt | 11.4 KB |
| 22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.mp4 | 97.3 MB |
| 22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.srt | 17.6 KB |
| 23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).mp4 | 48.1 MB |
| 23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).srt | 8.4 KB |
| 24. Finding The Positional Min and Max of Tensors.mp4 | 24.5 MB |
| 24. Finding The Positional Min and Max of Tensors.srt | 4.0 KB |
| 25. Reshaping, Viewing and Stacking Tensors.mp4 | 104.0 MB |
| 25. Reshaping, Viewing and Stacking Tensors.srt | 20.3 KB |
| 26. Squeezing, Unsqueezing and Permuting Tensors.mp4 | 88.4 MB |
| 26. Squeezing, Unsqueezing and Permuting Tensors.srt | 16.8 KB |
| 27. Selecting Data From Tensors (Indexing).mp4 | 57.0 MB |
| 27. Selecting Data From Tensors (Indexing).srt | 13.1 KB |
| 28. PyTorch Tensors and NumPy.mp4 | 59.8 MB |
| 28. PyTorch Tensors and NumPy.srt | 11.8 KB |
| 29. PyTorch Reproducibility (Taking the Random Out of Random).mp4 | 95.1 MB |
| 29. PyTorch Reproducibility (Taking the Random Out of Random).srt | 14.9 KB |
| 3. Machine Learning vs. Deep Learning.mp4 | 55.3 MB |
| 3. Machine Learning vs. Deep Learning.srt | 9.7 KB |
| 30. Different Ways of Accessing a GPU in PyTorch.mp4 | 113.0 MB |
| 30. Different Ways of Accessing a GPU in PyTorch.srt | 14.5 KB |
| 31. Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.mp4 | 64.5 MB |
| 31. Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.srt | 10.4 KB |
| 32. PyTorch Fundamentals Exercises and Extra-Curriculum.mp4 | 56.8 MB |
| 32. PyTorch Fundamentals Exercises and Extra-Curriculum.srt | 7.5 KB |
| 4. Anatomy of Neural Networks.mp4 | 70.3 MB |
| 4. Anatomy of Neural Networks.srt | 14.5 KB |
| 5. Different Types of Learning Paradigms.mp4 | 27.0 MB |
| 5. Different Types of Learning Paradigms.srt | 6.8 KB |
| 6. What Can Deep Learning Be Used For.mp4 | 43.2 MB |
| 6. What Can Deep Learning Be Used For.srt | 11.1 KB |
| 7. What Is and Why PyTorch.mp4 | 113.6 MB |
| 7. What Is and Why PyTorch.srt | 15.6 KB |
| 8. What Are Tensors.mp4 | 25.0 MB |
| 8. What Are Tensors.srt | 6.7 KB |
| 9. What We Are Going To Cover With PyTorch.mp4 | 50.4 MB |
| 9. What We Are Going To Cover With PyTorch.srt | 10.6 KB |
| 1. Introduction and Where You Can Get Help.mp4 | 28.6 MB |
| 1. Introduction and Where You Can Get Help.srt | 5.1 KB |
| 10. Making Predictions With Our Random Model Using Inference Mode.mp4 | 107.0 MB |
| 10. Making Predictions With Our Random Model Using Inference Mode.srt | 16.0 KB |
| 11. Training a Model Intuition (The Things We Need).mp4 | 69.5 MB |
| 11. Training a Model Intuition (The Things We Need).srt | 12.5 KB |
| 12. Setting Up an Optimizer and a Loss Function.mp4 | 116.0 MB |
| 12. Setting Up an Optimizer and a Loss Function.srt | 20.3 KB |
| 13. PyTorch Training Loop Steps and Intuition.mp4 | 128.8 MB |
| 13. PyTorch Training Loop Steps and Intuition.srt | 21.7 KB |
| 14. Writing Code for a PyTorch Training Loop.mp4 | 83.0 MB |
| 14. Writing Code for a PyTorch Training Loop.srt | 13.5 KB |
| 15. Reviewing the Steps in a Training Loop Step by Step.mp4 | 177.4 MB |
| 15. Reviewing the Steps in a Training Loop Step by Step.srt | 23.2 KB |
| 16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.mp4 | 101.7 MB |
| 16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.srt | 15.6 KB |
| 17. Writing Testing Loop Code and Discussing What's Happening Step by Step.mp4 | 135.0 MB |
| 17. Writing Testing Loop Code and Discussing What's Happening Step by Step.srt | 19.6 KB |
| 18. Reviewing What Happens in a Testing Loop Step by Step.mp4 | 161.6 MB |
| 18. Reviewing What Happens in a Testing Loop Step by Step.srt | 22.9 KB |
| 19. Writing Code to Save a PyTorch Model.mp4 | 129.8 MB |
| 19. Writing Code to Save a PyTorch Model.srt | 21.6 KB |
| 2. Getting Setup and What We Are Covering.mp4 | 69.7 MB |
| 2. Getting Setup and What We Are Covering.srt | 11.3 KB |
| 20. Writing Code to Load a PyTorch Model.mp4 | 79.6 MB |
| 20. Writing Code to Load a PyTorch Model.srt | 12.6 KB |
| 21. Setting Up to Practice Everything We Have Done Using Device Agnostic code.mp4 | 45.8 MB |
| 21. Setting Up to Practice Everything We Have Done Using Device Agnostic code.srt | 9.4 KB |
| 22. Putting Everything Together (Part 1) Data.mp4 | 49.3 MB |
| 22. Putting Everything Together (Part 1) Data.srt | 9.3 KB |
| 23. Putting Everything Together (Part 2) Building a Model.mp4 | 88.7 MB |
| 23. Putting Everything Together (Part 2) Building a Model.srt | 13.6 KB |
| 24. Putting Everything Together (Part 3) Training a Model.mp4 | 103.0 MB |
| 24. Putting Everything Together (Part 3) Training a Model.srt | 19.9 KB |
| 25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.mp4 | 50.6 MB |
| 25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.srt | 8.1 KB |
| 26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.mp4 | 72.5 MB |
| 26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.srt | 13.9 KB |
| 27. Exercise Imposter Syndrome.mp4 | 39.3 MB |
| 27. Exercise Imposter Syndrome.srt | 4.5 KB |
| 28. PyTorch Workflow Exercises and Extra-Curriculum.mp4 | 49.3 MB |
| 28. PyTorch Workflow Exercises and Extra-Curriculum.srt | 6.4 KB |
| 3. Creating a Simple Dataset Using the Linear Regression Formula.mp4 | 68.7 MB |
| 3. Creating a Simple Dataset Using the Linear Regression Formula.srt | 13.9 KB |
| 4. Splitting Our Data Into Training and Test Sets.mp4 | 65.2 MB |
| 4. Splitting Our Data Into Training and Test Sets.srt | 11.9 KB |
| 5. Building a function to Visualize Our Data.mp4 | 61.9 MB |
| 5. Building a function to Visualize Our Data.srt | 12.2 KB |
| 6. Creating Our First PyTorch Model for Linear Regression.mp4 | 130.1 MB |
| 6. Creating Our First PyTorch Model for Linear Regression.srt | 18.3 KB |
| 7. Breaking Down What's Happening in Our PyTorch Linear regression Model.mp4 | 62.2 MB |
| 7. Breaking Down What's Happening in Our PyTorch Linear regression Model.srt | 8.8 KB |
| 8. Discussing Some of the Most Important PyTorch Model Building Classes.mp4 | 74.4 MB |
| 8. Discussing Some of the Most Important PyTorch Model Building Classes.srt | 8.7 KB |
| 9. Checking Out the Internals of Our PyTorch Model.mp4 | 102.7 MB |
| 9. Checking Out the Internals of Our PyTorch Model.srt | 14.8 KB |
| 1. Introduction to Machine Learning Classification With PyTorch.mp4 | 84.6 MB |
| 1. Introduction to Machine Learning Classification With PyTorch.srt | 16.0 KB |
| 10. Loss Function Optimizer and Evaluation Function for Our Classification Network.mp4 | 161.0 MB |
| 10. Loss Function Optimizer and Evaluation Function for Our Classification Network.srt | 23.1 KB |
| 11. Going from Model Logits to Prediction Probabilities to Prediction Labels.mp4 | 134.5 MB |
| 11. Going from Model Logits to Prediction Probabilities to Prediction Labels.srt | 22.6 KB |
| 12. Coding a Training and Testing Optimization Loop for Our Classification Model.mp4 | 126.8 MB |
| 12. Coding a Training and Testing Optimization Loop for Our Classification Model.srt | 22.8 KB |
| 13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.mp4 | 150.0 MB |
| 13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.srt | 22.8 KB |
| 14. Discussing Options to Improve a Model.mp4 | 80.9 MB |
| 14. Discussing Options to Improve a Model.srt | 13.2 KB |
| 15. Creating a New Model with More Layers and Hidden Units.mp4 | 68.8 MB |
| 15. Creating a New Model with More Layers and Hidden Units.srt | 12.3 KB |
| 16. Writing Training and Testing Code to See if Our Upgraded Model Performs Better.mp4 | 118.6 MB |
| 16. Writing Training and Testing Code to See if Our Upgraded Model Performs Better.srt | 19.1 KB |
| 17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.mp4 | 61.4 MB |
| 17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.srt | 11.9 KB |
| 18. Building and Training a Model to Fit on Straight Line Data.mp4 | 71.7 MB |
| 18. Building and Training a Model to Fit on Straight Line Data.srt | 15.7 KB |
| 19. Evaluating Our Models Predictions on Straight Line Data.mp4 | 50.8 MB |
| 19. Evaluating Our Models Predictions on Straight Line Data.srt | 8.6 KB |
| 2. Classification Problem Example Input and Output Shapes.mp4 | 50.0 MB |
| 2. Classification Problem Example Input and Output Shapes.srt | 14.5 KB |
| 20. Introducing the Missing Piece for Our Classification Model Non-Linearity.mp4 | 96.5 MB |
| 20. Introducing the Missing Piece for Our Classification Model Non-Linearity.srt | 15.7 KB |
| 21. Building Our First Neural Network with Non-Linearity.mp4 | 92.6 MB |
| 21. Building Our First Neural Network with Non-Linearity.srt | 15.5 KB |
| 22. Writing Training and Testing Code for Our First Non-Linear Model.mp4 | 150.6 MB |
| 22. Writing Training and Testing Code for Our First Non-Linear Model.srt | 22.9 KB |
| 23. Making Predictions with and Evaluating Our First Non-Linear Model.mp4 | 53.0 MB |
| 23. Making Predictions with and Evaluating Our First Non-Linear Model.srt | 8.7 KB |
| 24. Replicating Non-Linear Activation Functions with Pure PyTorch.mp4 | 80.7 MB |
| 24. Replicating Non-Linear Activation Functions with Pure PyTorch.srt | 14.7 KB |
| 25. Putting It All Together (Part 1) Building a Multiclass Dataset.mp4 | 97.4 MB |
| 25. Putting It All Together (Part 1) Building a Multiclass Dataset.srt | 17.9 KB |
| 26. Creating a Multi-Class Classification Model with PyTorch.mp4 | 107.4 MB |
| 26. Creating a Multi-Class Classification Model with PyTorch.srt | 18.3 KB |
| 27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.mp4 | 65.1 MB |
| 27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.srt | 10.1 KB |
| 28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.mp4 | 97.0 MB |
| 28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.srt | 16.7 KB |
| 29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.mp4 | 150.1 MB |
| 29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.srt | 25.0 KB |
| 3. Typical Architecture of a Classification Neural Network (Overview).mp4 | 67.0 MB |
| 3. Typical Architecture of a Classification Neural Network (Overview).srt | 10.0 KB |
| 30. Making Predictions with and Evaluating Our Multi-Class Classification Model.mp4 | 77.0 MB |
| 30. Making Predictions with and Evaluating Our Multi-Class Classification Model.srt | 13.2 KB |
| 31. Discussing a Few More Classification Metrics.mp4 | 97.5 MB |
| 31. Discussing a Few More Classification Metrics.srt | 13.7 KB |
| 32. PyTorch Classification Exercises and Extra-Curriculum.mp4 | 41.5 MB |
| 32. PyTorch Classification Exercises and Extra-Curriculum.srt | 4.4 KB |
| 4. Making a Toy Classification Dataset.mp4 | 91.5 MB |
| 4. Making a Toy Classification Dataset.srt | 18.0 KB |
| 5. Turning Our Data into Tensors and Making a Training and Test Split.mp4 | 81.1 MB |
| 5. Turning Our Data into Tensors and Making a Training and Test Split.srt | 17.8 KB |
| 6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.mp4 | 31.9 MB |
| 6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.srt | 6.5 KB |
| 7. Coding a Small Neural Network to Handle Our Classification Data.mp4 | 86.8 MB |
| 7. Coding a Small Neural Network to Handle Our Classification Data.srt | 15.8 KB |
| 8. Making Our Neural Network Visual.mp4 | 91.3 MB |
| 8. Making Our Neural Network Visual.srt | 11.0 KB |
| 9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.mp4 | 123.2 MB |
| 9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.srt | 20.7 KB |
| [CourseClub.Me].url | 122 bytes |
| [FreeCourseSite.com].url | 127 bytes |
| [GigaCourse.Com].url | 49 bytes |
| 1. What Is a Computer Vision Problem and What We Are Going to Cover.mp4 | 113.7 MB |
| 1. What Is a Computer Vision Problem and What We Are Going to Cover.srt | 20.3 KB |
| 10. Creating a Loss Function an Optimizer for Model 0.mp4 | 110.5 MB |
| 10. Creating a Loss Function an Optimizer for Model 0.srt | 15.3 KB |
| 11. Creating a Function to Time Our Modelling Code.mp4 | 45.6 MB |
| 11. Creating a Function to Time Our Modelling Code.srt | 8.1 KB |
| 12. Writing Training and Testing Loops for Our Batched Data.mp4 | 157.6 MB |
| 12. Writing Training and Testing Loops for Our Batched Data.srt | 31.2 KB |
| 13. Writing an Evaluation Function to Get Our Models Results.mp4 | 106.8 MB |
| 13. Writing an Evaluation Function to Get Our Models Results.srt | 20.1 KB |
| 14. Setup Device-Agnostic Code for Running Experiments on the GPU.mp4 | 44.3 MB |
| 14. Setup Device-Agnostic Code for Running Experiments on the GPU.srt | 6.1 KB |
| 15. Model 1 Creating a Model with Non-Linear Functions.mp4 | 86.4 MB |
| 15. Model 1 Creating a Model with Non-Linear Functions.srt | 13.5 KB |
| 16. Mode 1 Creating a Loss Function and Optimizer.mp4 | 31.3 MB |
| 16. Mode 1 Creating a Loss Function and Optimizer.srt | 4.6 KB |
| 17. Turing Our Training Loop into a Function.mp4 | 70.9 MB |
| 17. Turing Our Training Loop into a Function.srt | 12.1 KB |
| 18. Turing Our Testing Loop into a Function.mp4 | 50.9 MB |
| 18. Turing Our Testing Loop into a Function.srt | 9.6 KB |
| 19. Training and Testing Model 1 with Our Training and Testing Functions.mp4 | 108.4 MB |
| 19. Training and Testing Model 1 with Our Training and Testing Functions.srt | 17.9 KB |
| 2. Computer Vision Input and Output Shapes.mp4 | 85.0 MB |
| 2. Computer Vision Input and Output Shapes.srt | 16.5 KB |
| 20. Getting a Results Dictionary for Model 1.mp4 | 41.3 MB |
| 20. Getting a Results Dictionary for Model 1.srt | 6.1 KB |
| 21. Model 2 Convolutional Neural Networks High Level Overview.mp4 | 94.6 MB |
| 21. Model 2 Convolutional Neural Networks High Level Overview.srt | 13.3 KB |
| 22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.mp4 | 208.3 MB |
| 22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.srt | 30.9 KB |
| 23. Model 2 Breaking Down Conv2D Step by Step.mp4 | 162.7 MB |
| 23. Model 2 Breaking Down Conv2D Step by Step.srt | 23.6 KB |
| 24. Model 2 Breaking Down MaxPool2D Step by Step.mp4 | 158.1 MB |
| 24. Model 2 Breaking Down MaxPool2D Step by Step.srt | 22.7 KB |
| 25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.mp4 | 174.8 MB |
| 25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.srt | 20.1 KB |
| 26. Model 2 Setting Up a Loss Function and Optimizer.mp4 | 27.9 MB |
| 26. Model 2 Setting Up a Loss Function and Optimizer.srt | 3.6 KB |
| 27. Model 2 Training Our First CNN and Evaluating Its Results.mp4 | 76.8 MB |
| 27. Model 2 Training Our First CNN and Evaluating Its Results.srt | 11.8 KB |
| 28. Comparing the Results of Our Modelling Experiments.mp4 | 61.8 MB |
| 28. Comparing the Results of Our Modelling Experiments.srt | 11.0 KB |
| 29. Making Predictions on Random Test Samples with the Best Trained Model.mp4 | 83.7 MB |
| 29. Making Predictions on Random Test Samples with the Best Trained Model.srt | 16.2 KB |
| 3. What Is a Convolutional Neural Network (CNN).mp4 | 55.4 MB |
| 3. What Is a Convolutional Neural Network (CNN).srt | 8.1 KB |
| 30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.mp4 | 63.5 MB |
| 30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.srt | 12.2 KB |
| 31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.mp4 | 160.8 MB |
| 31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.srt | 21.6 KB |
| 32. Evaluating Our Best Models Predictions with a Confusion Matrix.mp4 | 67.0 MB |
| 32. Evaluating Our Best Models Predictions with a Confusion Matrix.srt | 10.0 KB |
| 33. Saving and Loading Our Best Performing Model.mp4 | 98.1 MB |
| 33. Saving and Loading Our Best Performing Model.srt | 17.1 KB |
| 34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.mp4 | 81.9 MB |
| 34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.srt | 9.4 KB |
| 4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.mp4 | 89.2 MB |
| 4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.srt | 14.7 KB |
| 5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.mp4 | 154.0 MB |
| 5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.srt | 23.8 KB |
| 6. Visualizing Random Samples of Data.mp4 | 68.1 MB |
| 6. Visualizing Random Samples of Data.srt | 15.5 KB |
| 7. DataLoader Overview Understanding Mini-Batches.mp4 | 60.2 MB |
| 7. DataLoader Overview Understanding Mini-Batches.srt | 10.4 KB |
| 8. Turning Our Datasets Into DataLoaders.mp4 | 100.2 MB |
| 8. Turning Our Datasets Into DataLoaders.srt | 19.4 KB |
| 9. Model 0 Creating a Baseline Model with Two Linear Layers.mp4 | 136.9 MB |
| 9. Model 0 Creating a Baseline Model with Two Linear Layers.srt | 21.7 KB |
| 1. What Is a Custom Dataset and What We Are Going to Cover.mp4 | 92.6 MB |
| 1. What Is a Custom Dataset and What We Are Going to Cover.srt | 15.0 KB |
| 10. Visualizing a Loaded Image From the Train Dataset.mp4 | 76.7 MB |
| 10. Visualizing a Loaded Image From the Train Dataset.srt | 10.3 KB |
| 11. Turning Our Image Datasets into PyTorch Dataloaders.mp4 | 84.3 MB |
| 11. Turning Our Image Datasets into PyTorch Dataloaders.srt | 12.3 KB |
| 12. Creating a Custom Dataset Class in PyTorch High Level Overview.mp4 | 74.7 MB |
| 12. Creating a Custom Dataset Class in PyTorch High Level Overview.srt | 10.4 KB |
| 13. Creating a Helper Function to Get Class Names From a Directory.mp4 | 79.1 MB |
| 13. Creating a Helper Function to Get Class Names From a Directory.srt | 11.9 KB |
| 14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.mp4 | 176.3 MB |
| 14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.srt | 22.9 KB |
| 15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.mp4 | 69.5 MB |
| 15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.srt | 9.8 KB |
| 16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.mp4 | 131.2 MB |
| 16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.srt | 19.3 KB |
| 17. Turning Our Custom Datasets Into DataLoaders.mp4 | 80.6 MB |
| 17. Turning Our Custom Datasets Into DataLoaders.srt | 9.7 KB |
| 18. Exploring State of the Art Data Augmentation With Torchvision Transforms.mp4 | 166.4 MB |
| 18. Exploring State of the Art Data Augmentation With Torchvision Transforms.srt | 20.7 KB |
| 19. Building a Baseline Model (Part 1) Loading and Transforming Data.mp4 | 77.9 MB |
| 19. Building a Baseline Model (Part 1) Loading and Transforming Data.srt | 11.6 KB |
| 2. Importing PyTorch and Setting Up Device Agnostic Code.mp4 | 49.0 MB |
| 2. Importing PyTorch and Setting Up Device Agnostic Code.srt | 7.8 KB |
| 20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.mp4 | 117.2 MB |
| 20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.srt | 15.6 KB |
| 21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.mp4 | 96.5 MB |
| 21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.srt | 12.0 KB |
| 22. Using the Torchinfo Package to Get a Summary of Our Model.mp4 | 65.0 MB |
| 22. Using the Torchinfo Package to Get a Summary of Our Model.srt | 9.5 KB |
| 23. Creating Training and Testing loop Functions.mp4 | 106.2 MB |
| 23. Creating Training and Testing loop Functions.srt | 17.5 KB |
| 24. Creating a Train Function to Train and Evaluate Our Models.mp4 | 103.5 MB |
| 24. Creating a Train Function to Train and Evaluate Our Models.srt | 15.6 KB |
| 25. Training and Evaluating Model 0 With Our Training Functions.mp4 | 89.3 MB |
| 25. Training and Evaluating Model 0 With Our Training Functions.srt | 14.7 KB |
| 26. Plotting the Loss Curves of Model 0.mp4 | 89.4 MB |
| 26. Plotting the Loss Curves of Model 0.srt | 12.5 KB |
| 27. The Balance Between Overfitting and Underfitting and How to Deal With Each.mp4 | 131.8 MB |
| 27. The Balance Between Overfitting and Underfitting and How to Deal With Each.srt | 21.6 KB |
| 28. Creating Augmented Training Datasets and DataLoaders for Model 1.mp4 | 98.8 MB |
| 28. Creating Augmented Training Datasets and DataLoaders for Model 1.srt | 15.1 KB |
| 29. Constructing and Training Model 1.mp4 | 60.6 MB |
| 29. Constructing and Training Model 1.srt | 9.5 KB |
| 3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.mp4 | 151.0 MB |
| 3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.srt | 19.1 KB |
| 30. Plotting the Loss Curves of Model 1.mp4 | 31.7 MB |
| 30. Plotting the Loss Curves of Model 1.srt | 5.1 KB |
| 31. Plotting the Loss Curves of All of Our Models Against Each Other.mp4 | 89.3 MB |
| 31. Plotting the Loss Curves of All of Our Models Against Each Other.srt | 15.8 KB |
| 32. Predicting on Custom Data (Part 1) Downloading an Image.mp4 | 51.7 MB |
| 32. Predicting on Custom Data (Part 1) Downloading an Image.srt | 7.7 KB |
| 33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.mp4 | 68.0 MB |
| 33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.srt | 10.7 KB |
| 34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.mp4 | 127.0 MB |
| 34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.srt | 19.7 KB |
| 35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.mp4 | 36.1 MB |
| 35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.srt | 5.9 KB |
| 36. Predicting on Custom Data (Part 5) Putting It All Together.mp4 | 113.0 MB |
| 36. Predicting on Custom Data (Part 5) Putting It All Together.srt | 18.4 KB |
| 37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.mp4 | 73.3 MB |
| 37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.srt | 9.3 KB |
| 4. Becoming One With the Data (Part 1) Exploring the Data Format.mp4 | 87.6 MB |
| 4. Becoming One With the Data (Part 1) Exploring the Data Format.srt | 12.1 KB |
| 5. Becoming One With the Data (Part 2) Visualizing a Random Image.mp4 | 115.3 MB |
| 5. Becoming One With the Data (Part 2) Visualizing a Random Image.srt | 17.2 KB |
| 6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.mp4 | 51.9 MB |
| 6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.srt | 7.1 KB |
| 7. Transforming Data (Part 1) Turning Images Into Tensors.mp4 | 81.7 MB |
| 7. Transforming Data (Part 1) Turning Images Into Tensors.srt | 11.7 KB |
| 8. Transforming Data (Part 2) Visualizing Transformed Images.mp4 | 127.6 MB |
| 8. Transforming Data (Part 2) Visualizing Transformed Images.srt | 16.7 KB |
| 9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.mp4 | 98.2 MB |
| 9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.srt | 13.3 KB |
| [CourseClub.Me].url | 122 bytes |
| [FreeCourseSite.com].url | 127 bytes |
| [GigaCourse.Com].url | 49 bytes |
| 1. What Is Going Modular and What We Are Going to Cover.mp4 | 100.1 MB |
| 1. What Is Going Modular and What We Are Going to Cover.srt | 18.0 KB |
| 10. Going Modular Summary, Exercises and Extra-Curriculum.mp4 | 80.7 MB |
| 10. Going Modular Summary, Exercises and Extra-Curriculum.srt | 8.9 KB |
| 2. Going Modular Notebook (Part 1) Running It End to End.mp4 | 104.9 MB |
| 2. Going Modular Notebook (Part 1) Running It End to End.srt | 11.5 KB |
| 3. Downloading a Dataset.mp4 | 67.6 MB |
| 3. Downloading a Dataset.srt | 7.2 KB |
| 4. Writing the Outline for Our First Python Script to Setup the Data.mp4 | 156.8 MB |
| 4. Writing the Outline for Our First Python Script to Setup the Data.srt | 18.9 KB |
| 5. Creating a Python Script to Create Our PyTorch DataLoaders.mp4 | 135.1 MB |
| 5. Creating a Python Script to Create Our PyTorch DataLoaders.srt | 15.9 KB |
| 6. Turning Our Model Building Code into a Python Script.mp4 | 115.1 MB |
| 6. Turning Our Model Building Code into a Python Script.srt | 13.4 KB |
| 7. Turning Our Model Training Code into a Python Script.mp4 | 80.0 MB |
| 7. Turning Our Model Training Code into a Python Script.srt | 8.6 KB |
| 8. Turning Our Utility Function to Save a Model into a Python Script.mp4 | 75.8 MB |
| 8. Turning Our Utility Function to Save a Model into a Python Script.srt | 9.0 KB |
| 9. Creating a Training Script to Train Our Model in One Line of Code.mp4 | 165.5 MB |
| 9. Creating a Training Script to Train Our Model in One Line of Code.srt | 21.9 KB |
| 1. Introduction What is Transfer Learning and Why Use It.mp4 | 97.3 MB |
| 1. Introduction What is Transfer Learning and Why Use It.srt | 15.7 KB |
| 10. Different Kinds of Transfer Learning.mp4 | 57.0 MB |
| 10. Different Kinds of Transfer Learning.srt | 10.7 KB |
| 11. Getting a Summary of the Different Layers of Our Model.mp4 | 76.0 MB |
| 11. Getting a Summary of the Different Layers of Our Model.srt | 10.0 KB |
| 12. Freezing the Base Layers of Our Model and Updating the Classifier Head.mp4 | 160.7 MB |
| 12. Freezing the Base Layers of Our Model and Updating the Classifier Head.srt | 19.9 KB |
| 13. Training Our First Transfer Learning Feature Extractor Model.mp4 | 74.8 MB |
| 13. Training Our First Transfer Learning Feature Extractor Model.srt | 11.6 KB |
| 14. Plotting the Loss curves of Our Transfer Learning Model.mp4 | 58.9 MB |
| 14. Plotting the Loss curves of Our Transfer Learning Model.srt | 9.4 KB |
| 15. Outlining the Steps to Make Predictions on the Test Images.mp4 | 66.7 MB |
| 15. Outlining the Steps to Make Predictions on the Test Images.srt | 10.5 KB |
| 16. Creating a Function Predict On and Plot Images.mp4 | 101.7 MB |
| 16. Creating a Function Predict On and Plot Images.srt | 14.2 KB |
| 17. Making and Plotting Predictions on Test Images.mp4 | 78.1 MB |
| 17. Making and Plotting Predictions on Test Images.srt | 10.7 KB |
| 18. Making a Prediction on a Custom Image.mp4 | 67.8 MB |
| 18. Making a Prediction on a Custom Image.srt | 9.4 KB |
| 19. Main Takeaways, Exercises and Extra- Curriculum.mp4 | 44.4 MB |
| 19. Main Takeaways, Exercises and Extra- Curriculum.srt | 5.2 KB |
| 2. Where Can You Find Pretrained Models and What We Are Going to Cover.mp4 | 55.9 MB |
| 2. Where Can You Find Pretrained Models and What We Are Going to Cover.srt | 8.3 KB |
| 3. Installing the Latest Versions of Torch and Torchvision.mp4 | 82.4 MB |
| 3. Installing the Latest Versions of Torch and Torchvision.srt | 11.1 KB |
| 4. Downloading Our Previously Written Code from Going Modular.mp4 | 83.7 MB |
| 4. Downloading Our Previously Written Code from Going Modular.srt | 10.3 KB |
| 5. Downloading Pizza, Steak, Sushi Image Data from Github.mp4 | 72.2 MB |
| 5. Downloading Pizza, Steak, Sushi Image Data from Github.srt | 11.2 KB |
| 6. Turning Our Data into DataLoaders with Manually Created Transforms.mp4 | 141.5 MB |
| 6. Turning Our Data into DataLoaders with Manually Created Transforms.srt | 19.4 KB |
| 7. Turning Our Data into DataLoaders with Automatic Created Transforms.mp4 | 139.7 MB |
| 7. Turning Our Data into DataLoaders with Automatic Created Transforms.srt | 18.4 KB |
| 8. Which Pretrained Model Should You Use.mp4 | 128.8 MB |
| 8. Which Pretrained Model Should You Use.srt | 17.7 KB |
| 9. Setting Up a Pretrained Model with Torchvision.mp4 | 113.1 MB |
| 9. Setting Up a Pretrained Model with Torchvision.srt | 16.6 KB |
| [CourseClub.Me].url | 122 bytes |
| [FreeCourseSite.com].url | 127 bytes |
| [GigaCourse.Com].url | 49 bytes |
| 1. What Is Experiment Tracking and Why Track Experiments.mp4 | 61.9 MB |
| 1. What Is Experiment Tracking and Why Track Experiments.srt | 11.3 KB |
| 10. Creating a Function to Create SummaryWriter Instances.mp4 | 80.1 MB |
| 10. Creating a Function to Create SummaryWriter Instances.srt | 14.2 KB |
| 11. Adapting Our Train Function to Be Able to Track Multiple Experiments.mp4 | 66.5 MB |
| 11. Adapting Our Train Function to Be Able to Track Multiple Experiments.srt | 6.6 KB |
| 12. What Experiments Should You Try.mp4 | 46.9 MB |
| 12. What Experiments Should You Try.srt | 8.5 KB |
| 13. Discussing the Experiments We Are Going to Try.mp4 | 48.3 MB |
| 13. Discussing the Experiments We Are Going to Try.srt | 8.1 KB |
| 14. Downloading Datasets for Our Modelling Experiments.mp4 | 66.4 MB |
| 14. Downloading Datasets for Our Modelling Experiments.srt | 8.9 KB |
| 15. Turning Our Datasets into DataLoaders Ready for Experimentation.mp4 | 78.1 MB |
| 15. Turning Our Datasets into DataLoaders Ready for Experimentation.srt | 11.3 KB |
| 16. Creating Functions to Prepare Our Feature Extractor Models.mp4 | 159.2 MB |
| 16. Creating Functions to Prepare Our Feature Extractor Models.srt | 22.7 KB |
| 17. Coding Out the Steps to Run a Series of Modelling Experiments.mp4 | 127.6 MB |
| 17. Coding Out the Steps to Run a Series of Modelling Experiments.srt | 19.6 KB |
| 18. Running Eight Different Modelling Experiments in 5 Minutes.mp4 | 45.7 MB |
| 18. Running Eight Different Modelling Experiments in 5 Minutes.srt | 6.3 KB |
| 19. Viewing Our Modelling Experiments in TensorBoard.mp4 | 140.3 MB |
| 19. Viewing Our Modelling Experiments in TensorBoard.srt | 19.7 KB |
| 2. Getting Setup by Importing Torch Libraries and Going Modular Code.mp4 | 93.4 MB |
| 2. Getting Setup by Importing Torch Libraries and Going Modular Code.srt | 12.4 KB |
| 20. Loading the Best Model and Making Predictions on Random Images from the Test Set.mp4 | 99.2 MB |
| 20. Loading the Best Model and Making Predictions on Random Images from the Test Set.srt | 14.8 KB |
| 21. Making a Prediction on Our Own Custom Image with the Best Model.mp4 | 39.7 MB |
| 21. Making a Prediction on Our Own Custom Image with the Best Model.srt | 5.8 KB |
| 22. Main Takeaways, Exercises and Extra- Curriculum.mp4 | 43.6 MB |
| 22. Main Takeaways, Exercises and Extra- Curriculum.srt | 6.6 KB |
| 3. Creating a Function to Download Data.mp4 | 95.2 MB |
| 3. Creating a Function to Download Data.srt | 14.6 KB |
| 4. Turning Our Data into DataLoaders Using Manual Transforms.mp4 | 92.7 MB |
| 4. Turning Our Data into DataLoaders Using Manual Transforms.srt | 12.3 KB |
| 5. Turning Our Data into DataLoaders Using Automatic Transforms.mp4 | 82.0 MB |
| 5. Turning Our Data into DataLoaders Using Automatic Transforms.srt | 11.1 KB |
| 6. Preparing a Pretrained Model for Our Own Problem.mp4 | 113.2 MB |
| 6. Preparing a Pretrained Model for Our Own Problem.srt | 15.7 KB |
| 7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.mp4 | 150.3 MB |
| 7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.srt | 20.0 KB |
| 8. Training a Single Model and Saving the Results to TensorBoard.mp4 | 41.8 MB |
| 8. Training a Single Model and Saving the Results to TensorBoard.srt | 6.7 KB |
| 9. Exploring Our Single Models Results with TensorBoard.mp4 | 116.3 MB |
| 9. Exploring Our Single Models Results with TensorBoard.srt | 16.7 KB |
Name
DL
Uploader
Size
S/L
Added
-
2.3 GB
[12
/
10]
2023-06-01
| Uploaded by freecoursewb | Size 2.3 GB | Health [ 12 /10 ] | Added 2023-06-01 |
NOTE
SOURCE: Udemy PyTorch for Deep Learning in 2023 Zero to Mastery
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