ZeroToMastery - PyTorch for Deep Learning Bootcamp Zero to Mastery (4.2025)

正在检测能否在线播放…

大小: 18.5 GB
文件数: 708
分类: 视频
热度: 296
最近活跃: 25 分钟前

文件列表

1. Introduction/1. PyTorch for Deep Learning Bootcamp Zero to Mastery.mp4104.6 MB
1. Introduction/1. PyTorch for Deep Learning Bootcamp Zero to Mastery.srt5.6 KB
1. Introduction/2. Course Welcome and What Is Deep Learning.mp417.3 MB
1. Introduction/2. Course Welcome and What Is Deep Learning.srt9.5 KB
1. Introduction/3. Exercise Meet Your Classmates and Instructor.html4.5 KB
1. Introduction/4. Course Companion Book + Code + More.html6.7 KB
1. Introduction/5. Machine Learning + Python Monthly.html6.6 KB
1. Introduction/6. ZTM Plugin + Understanding Your Video Player.html6.9 KB
1. Introduction/7. Set Your Learning Streak Goal.html7.8 KB
10. Section 08 PyTorch Paper Replicating/1. What Is a Machine Learning Research Paper.mp477.9 MB
10. Section 08 PyTorch Paper Replicating/1. What Is a Machine Learning Research Paper.srt12.7 KB
10. Section 08 PyTorch Paper Replicating/10. Breaking Down Figure 1 of the ViT Paper.mp448.3 MB
10. Section 08 PyTorch Paper Replicating/10. Breaking Down Figure 1 of the ViT Paper.srt17.3 KB
10. Section 08 PyTorch Paper Replicating/11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.mp4108.1 MB
10. Section 08 PyTorch Paper Replicating/11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.srt17.8 KB
10. Section 08 PyTorch Paper Replicating/12. Breaking Down Equation 1.mp474.6 MB
10. Section 08 PyTorch Paper Replicating/12. Breaking Down Equation 1.srt13.4 KB
10. Section 08 PyTorch Paper Replicating/13. Breaking Down Equations 2 and 3.mp487.5 MB
10. Section 08 PyTorch Paper Replicating/13. Breaking Down Equations 2 and 3.srt15.9 KB
10. Section 08 PyTorch Paper Replicating/14. Breaking Down Equation 4.mp466.8 MB
10. Section 08 PyTorch Paper Replicating/14. Breaking Down Equation 4.srt10.7 KB
10. Section 08 PyTorch Paper Replicating/15. Breaking Down Table 1.mp484.8 MB
10. Section 08 PyTorch Paper Replicating/15. Breaking Down Table 1.srt14.3 KB
10. Section 08 PyTorch Paper Replicating/16. Calculating the Input and Output Shape of the Embedding Layer by Hand.mp4103.3 MB
10. Section 08 PyTorch Paper Replicating/16. Calculating the Input and Output Shape of the Embedding Layer by Hand.srt22.5 KB
10. Section 08 PyTorch Paper Replicating/17. Turning a Single Image into Patches (Part 1 Patching the Top Row).mp486.8 MB
10. Section 08 PyTorch Paper Replicating/17. Turning a Single Image into Patches (Part 1 Patching the Top Row).srt20.9 KB
10. Section 08 PyTorch Paper Replicating/18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).mp481.4 MB
10. Section 08 PyTorch Paper Replicating/18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).srt19.1 KB
10. Section 08 PyTorch Paper Replicating/19. Creating Patch Embeddings with a Convolutional Layer.mp478.3 MB
10. Section 08 PyTorch Paper Replicating/19. Creating Patch Embeddings with a Convolutional Layer.srt21.1 KB
10. Section 08 PyTorch Paper Replicating/2. Why Replicate a Machine Learning Research Paper.mp412.7 MB
10. Section 08 PyTorch Paper Replicating/2. Why Replicate a Machine Learning Research Paper.srt5.3 KB
10. Section 08 PyTorch Paper Replicating/20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.mp483.7 MB
10. Section 08 PyTorch Paper Replicating/20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.srt20.5 KB
10. Section 08 PyTorch Paper Replicating/21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.mp457.0 MB
10. Section 08 PyTorch Paper Replicating/21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.srt14.3 KB
10. Section 08 PyTorch Paper Replicating/22. Visualizing a Single Sequence Vector of Patch Embeddings.mp433.4 MB
10. Section 08 PyTorch Paper Replicating/22. Visualizing a Single Sequence Vector of Patch Embeddings.srt7.2 KB
10. Section 08 PyTorch Paper Replicating/23. Creating the Patch Embedding Layer with PyTorch.mp4104.5 MB
10. Section 08 PyTorch Paper Replicating/23. Creating the Patch Embedding Layer with PyTorch.srt24.5 KB
10. Section 08 PyTorch Paper Replicating/24. Creating the Class Token Embedding.mp481.6 MB
10. Section 08 PyTorch Paper Replicating/24. Creating the Class Token Embedding.srt19.3 KB
10. Section 08 PyTorch Paper Replicating/25. Creating the Class Token Embedding - Less Birds.mp477.4 MB
10. Section 08 PyTorch Paper Replicating/25. Creating the Class Token Embedding - Less Birds.srt18.9 KB
10. Section 08 PyTorch Paper Replicating/26. Creating the Position Embedding.mp466.2 MB
10. Section 08 PyTorch Paper Replicating/26. Creating the Position Embedding.srt18.3 KB
10. Section 08 PyTorch Paper Replicating/27. Equation 1 Putting it All Together.mp486.8 MB
10. Section 08 PyTorch Paper Replicating/27. Equation 1 Putting it All Together.srt20.2 KB
10. Section 08 PyTorch Paper Replicating/28. Equation 2 Multihead Attention Overview.mp490.1 MB
10. Section 08 PyTorch Paper Replicating/28. Equation 2 Multihead Attention Overview.srt23.4 KB
10. Section 08 PyTorch Paper Replicating/29. Equation 2 Layernorm Overview.mp477.3 MB
10. Section 08 PyTorch Paper Replicating/29. Equation 2 Layernorm Overview.srt13.6 KB
10. Section 08 PyTorch Paper Replicating/3. Where Can You Find Machine Learning Research Papers and Code.mp478.6 MB
10. Section 08 PyTorch Paper Replicating/3. Where Can You Find Machine Learning Research Papers and Code.srt11.8 KB
10. Section 08 PyTorch Paper Replicating/30. Turning Equation 2 into Code.mp4113.8 MB
10. Section 08 PyTorch Paper Replicating/30. Turning Equation 2 into Code.srt23.4 KB
10. Section 08 PyTorch Paper Replicating/31. Checking the Inputs and Outputs of Equation.mp433.7 MB
10. Section 08 PyTorch Paper Replicating/31. Checking the Inputs and Outputs of Equation.srt8.6 KB
10. Section 08 PyTorch Paper Replicating/32. Equation 3 Replication Overview.mp454.5 MB
10. Section 08 PyTorch Paper Replicating/32. Equation 3 Replication Overview.srt14.3 KB
10. Section 08 PyTorch Paper Replicating/33. Turning Equation 3 into Code.mp470.1 MB
10. Section 08 PyTorch Paper Replicating/33. Turning Equation 3 into Code.srt16.3 KB
10. Section 08 PyTorch Paper Replicating/34. Transformer Encoder Overview.mp450.3 MB
10. Section 08 PyTorch Paper Replicating/34. Transformer Encoder Overview.srt12.5 KB
10. Section 08 PyTorch Paper Replicating/35. Combining Equation 2 and 3 to Create the Transformer Encoder.mp452.2 MB
10. Section 08 PyTorch Paper Replicating/35. Combining Equation 2 and 3 to Create the Transformer Encoder.srt10.8 KB
10. Section 08 PyTorch Paper Replicating/36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.mp4131.1 MB
10. Section 08 PyTorch Paper Replicating/36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.srt23.8 KB
10. Section 08 PyTorch Paper Replicating/37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces of the Puzzle.mp4128.4 MB
10. Section 08 PyTorch Paper Replicating/37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces of the Puzzle.srt23.9 KB
10. Section 08 PyTorch Paper Replicating/38. Bringing Our Own Vision Transformer to Life - Part 2 Putting Together the Forward Method.mp478.3 MB
10. Section 08 PyTorch Paper Replicating/38. Bringing Our Own Vision Transformer to Life - Part 2 Putting Together the Forward Method.srt13.4 KB
10. Section 08 PyTorch Paper Replicating/39. Getting a Visual Summary of Our Custom Vision Transformer.mp464.9 MB
10. Section 08 PyTorch Paper Replicating/39. Getting a Visual Summary of Our Custom Vision Transformer.srt11.2 KB
10. Section 08 PyTorch Paper Replicating/4. What We Are Going to Cover.mp456.3 MB
10. Section 08 PyTorch Paper Replicating/4. What We Are Going to Cover.srt14.7 KB
10. Section 08 PyTorch Paper Replicating/40. Creating a Loss Function and Optimizer from the ViT Paper.mp479.5 MB
10. Section 08 PyTorch Paper Replicating/40. Creating a Loss Function and Optimizer from the ViT Paper.srt17.3 KB
10. Section 08 PyTorch Paper Replicating/41. Training our Custom ViT on Food Vision Mini.mp437.8 MB
10. Section 08 PyTorch Paper Replicating/41. Training our Custom ViT on Food Vision Mini.srt7.3 KB
10. Section 08 PyTorch Paper Replicating/42. Discussing what Our Training Setup Is Missing.mp465.7 MB
10. Section 08 PyTorch Paper Replicating/42. Discussing what Our Training Setup Is Missing.srt13.6 KB
10. Section 08 PyTorch Paper Replicating/43. Plotting a Loss Curve for Our ViT Model.mp440.9 MB
10. Section 08 PyTorch Paper Replicating/43. Plotting a Loss Curve for Our ViT Model.srt9.0 KB
10. Section 08 PyTorch Paper Replicating/44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.mp4112.3 MB
10. Section 08 PyTorch Paper Replicating/44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.srt21.2 KB
10. Section 08 PyTorch Paper Replicating/45. Preparing Data to Be Used with a Pretrained ViT.mp436.3 MB
10. Section 08 PyTorch Paper Replicating/45. Preparing Data to Be Used with a Pretrained ViT.srt8.6 KB
10. Section 08 PyTorch Paper Replicating/46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.mp451.9 MB
10. Section 08 PyTorch Paper Replicating/46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.srt10.1 KB
10. Section 08 PyTorch Paper Replicating/47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.mp424.8 MB
10. Section 08 PyTorch Paper Replicating/47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.srt5.3 KB
10. Section 08 PyTorch Paper Replicating/48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.mp426.4 MB
10. Section 08 PyTorch Paper Replicating/48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.srt5.2 KB
10. Section 08 PyTorch Paper Replicating/49. Making Predictions on a Custom Image with Our Pretrained ViT.mp422.9 MB
10. Section 08 PyTorch Paper Replicating/49. Making Predictions on a Custom Image with Our Pretrained ViT.srt5.3 KB
10. Section 08 PyTorch Paper Replicating/5. Getting Setup for Coding in Google Colab.mp466.4 MB
10. Section 08 PyTorch Paper Replicating/5. Getting Setup for Coding in Google Colab.srt13.1 KB
10. Section 08 PyTorch Paper Replicating/50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.mp464.3 MB
10. Section 08 PyTorch Paper Replicating/50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.srt13.7 KB
10. Section 08 PyTorch Paper Replicating/6. Downloading Data for Food Vision Mini.mp430.2 MB
10. Section 08 PyTorch Paper Replicating/6. Downloading Data for Food Vision Mini.srt6.0 KB
10. Section 08 PyTorch Paper Replicating/7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.mp457.7 MB
10. Section 08 PyTorch Paper Replicating/7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.srt14.1 KB
10. Section 08 PyTorch Paper Replicating/8. Visualizing a Single Image.mp422.8 MB
10. Section 08 PyTorch Paper Replicating/8. Visualizing a Single Image.srt5.8 KB
10. Section 08 PyTorch Paper Replicating/9. Replicating a Vision Transformer - High Level Overview.mp448.2 MB
10. Section 08 PyTorch Paper Replicating/9. Replicating a Vision Transformer - High Level Overview.srt15.5 KB
11. Section 09 PyTorch Model Deployment/1. What is Machine Learning Model Deployment and Why Deploy a Machine Learning Model.mp444.0 MB
11. Section 09 PyTorch Model Deployment/1. What is Machine Learning Model Deployment and Why Deploy a Machine Learning Model.srt15.5 KB
11. Section 09 PyTorch Model Deployment/10. Creating an EffNetB2 Feature Extractor Model.mp460.3 MB
11. Section 09 PyTorch Model Deployment/10. Creating an EffNetB2 Feature Extractor Model.srt14.9 KB
11. Section 09 PyTorch Model Deployment/11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.mp437.9 MB
11. Section 09 PyTorch Model Deployment/11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.srt8.7 KB
11. Section 09 PyTorch Model Deployment/12. Creating DataLoaders for EffNetB2.mp417.5 MB
11. Section 09 PyTorch Model Deployment/13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.mp465.2 MB
11. Section 09 PyTorch Model Deployment/13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.srt14.8 KB
11. Section 09 PyTorch Model Deployment/14. Saving Our EffNetB2 Model to File.mp414.7 MB
11. Section 09 PyTorch Model Deployment/14. Saving Our EffNetB2 Model to File.srt4.7 KB
11. Section 09 PyTorch Model Deployment/15. Getting the Size of Our EffNetB2 Model in Megabytes.mp433.7 MB
11. Section 09 PyTorch Model Deployment/15. Getting the Size of Our EffNetB2 Model in Megabytes.srt7.2 KB
11. Section 09 PyTorch Model Deployment/16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.mp441.4 MB
11. Section 09 PyTorch Model Deployment/16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.srt9.5 KB
11. Section 09 PyTorch Model Deployment/17. Creating a Vision Transformer Feature Extractor Model.mp451.0 MB
11. Section 09 PyTorch Model Deployment/17. Creating a Vision Transformer Feature Extractor Model.srt11.2 KB
11. Section 09 PyTorch Model Deployment/18. Creating DataLoaders for Our ViT Feature Extractor Model.mp410.5 MB
11. Section 09 PyTorch Model Deployment/18. Creating DataLoaders for Our ViT Feature Extractor Model.srt4.1 KB
11. Section 09 PyTorch Model Deployment/19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.mp439.3 MB
11. Section 09 PyTorch Model Deployment/19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.srt10.8 KB
11. Section 09 PyTorch Model Deployment/2. Three Questions to Ask for Machine Learning Model Deployment.mp426.0 MB
11. Section 09 PyTorch Model Deployment/2. Three Questions to Ask for Machine Learning Model Deployment.srt13.2 KB
11. Section 09 PyTorch Model Deployment/20. Saving Our ViT Feature Extractor and Inspecting Its Size.mp425.1 MB
11. Section 09 PyTorch Model Deployment/20. Saving Our ViT Feature Extractor and Inspecting Its Size.srt7.7 KB
11. Section 09 PyTorch Model Deployment/21. Collecting Stats About Our ViT Feature Extractor.mp425.6 MB
11. Section 09 PyTorch Model Deployment/21. Collecting Stats About Our ViT Feature Extractor.srt9.4 KB
11. Section 09 PyTorch Model Deployment/22. Outlining the Steps for Making and Timing Predictions for Our Models.mp453.4 MB
11. Section 09 PyTorch Model Deployment/22. Outlining the Steps for Making and Timing Predictions for Our Models.srt15.0 KB
11. Section 09 PyTorch Model Deployment/23. Creating a Function to Make and Time Predictions with Our Models.mp4122.6 MB
11. Section 09 PyTorch Model Deployment/23. Creating a Function to Make and Time Predictions with Our Models.srt20.6 KB
11. Section 09 PyTorch Model Deployment/24. Making and Timing Predictions with EffNetB2.mp463.5 MB
11. Section 09 PyTorch Model Deployment/24. Making and Timing Predictions with EffNetB2.srt14.4 KB
11. Section 09 PyTorch Model Deployment/25. Making and Timing Predictions with ViT.mp447.5 MB
11. Section 09 PyTorch Model Deployment/25. Making and Timing Predictions with ViT.srt8.7 KB
11. Section 09 PyTorch Model Deployment/26. Comparing EffNetB2 and ViT Model Statistics.mp450.7 MB
11. Section 09 PyTorch Model Deployment/26. Comparing EffNetB2 and ViT Model Statistics.srt16.2 KB
11. Section 09 PyTorch Model Deployment/27. Visualizing the Performance vs Speed Trade-off.mp475.2 MB
11. Section 09 PyTorch Model Deployment/27. Visualizing the Performance vs Speed Trade-off.srt23.6 KB
11. Section 09 PyTorch Model Deployment/28. Gradio Overview and Installation.mp455.0 MB
11. Section 09 PyTorch Model Deployment/28. Gradio Overview and Installation.srt14.4 KB
11. Section 09 PyTorch Model Deployment/29. Gradio Function Outline.mp446.4 MB
11. Section 09 PyTorch Model Deployment/3. Where Is My Model Going to Go.mp483.8 MB
11. Section 09 PyTorch Model Deployment/3. Where Is My Model Going to Go.srt20.9 KB
11. Section 09 PyTorch Model Deployment/30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.mp453.3 MB
11. Section 09 PyTorch Model Deployment/30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.srt14.3 KB
11. Section 09 PyTorch Model Deployment/31. Creating a List of Examples to Pass to Our Gradio Demo.mp431.8 MB
11. Section 09 PyTorch Model Deployment/31. Creating a List of Examples to Pass to Our Gradio Demo.srt7.1 KB
11. Section 09 PyTorch Model Deployment/32. Bringing Food Vision Mini to Life in a Live Web Application.mp478.1 MB
11. Section 09 PyTorch Model Deployment/32. Bringing Food Vision Mini to Life in a Live Web Application.srt20.7 KB
11. Section 09 PyTorch Model Deployment/33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.mp434.7 MB
11. Section 09 PyTorch Model Deployment/33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.srt9.8 KB
11. Section 09 PyTorch Model Deployment/34. Outlining the File Structure of Our Deployed App.mp452.8 MB
11. Section 09 PyTorch Model Deployment/34. Outlining the File Structure of Our Deployed App.srt11.7 KB
11. Section 09 PyTorch Model Deployment/35. Creating a Food Vision Mini Demo Directory to House Our App Files.mp424.2 MB
11. Section 09 PyTorch Model Deployment/35. Creating a Food Vision Mini Demo Directory to House Our App Files.srt6.2 KB
11. Section 09 PyTorch Model Deployment/36. Creating an Examples Directory with Example Food Vision Mini Images.mp459.2 MB
11. Section 09 PyTorch Model Deployment/36. Creating an Examples Directory with Example Food Vision Mini Images.srt13.8 KB
11. Section 09 PyTorch Model Deployment/37. Writing Code to Move Our Saved EffNetB2 Model File.mp443.8 MB
11. Section 09 PyTorch Model Deployment/37. Writing Code to Move Our Saved EffNetB2 Model File.srt10.7 KB
11. Section 09 PyTorch Model Deployment/38. Turning Our EffNetB2 Model Creation Function Into a Python Script.mp431.0 MB
11. Section 09 PyTorch Model Deployment/38. Turning Our EffNetB2 Model Creation Function Into a Python Script.srt4.9 KB
11. Section 09 PyTorch Model Deployment/39. Turning Our Food Vision Mini Demo App Into a Python Script.mp493.0 MB
11. Section 09 PyTorch Model Deployment/39. Turning Our Food Vision Mini Demo App Into a Python Script.srt20.9 KB
11. Section 09 PyTorch Model Deployment/4. How Is My Model Going to Function.mp437.6 MB
11. Section 09 PyTorch Model Deployment/4. How Is My Model Going to Function.srt12.4 KB
11. Section 09 PyTorch Model Deployment/40. Creating a Requirements File for Our Food Vision Mini App.mp423.9 MB
11. Section 09 PyTorch Model Deployment/40. Creating a Requirements File for Our Food Vision Mini App.srt6.4 KB
11. Section 09 PyTorch Model Deployment/41. Downloading Our Food Vision Mini App Files from Google Colab.mp467.5 MB
11. Section 09 PyTorch Model Deployment/41. Downloading Our Food Vision Mini App Files from Google Colab.srt18.1 KB
11. Section 09 PyTorch Model Deployment/42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.mp489.4 MB
11. Section 09 PyTorch Model Deployment/42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.srt23.4 KB
11. Section 09 PyTorch Model Deployment/43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.mp453.6 MB
11. Section 09 PyTorch Model Deployment/43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.srt13.5 KB
11. Section 09 PyTorch Model Deployment/44. Food Vision Big Project Outline.mp421.7 MB
11. Section 09 PyTorch Model Deployment/44. Food Vision Big Project Outline.srt6.2 KB
11. Section 09 PyTorch Model Deployment/45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.mp462.1 MB
11. Section 09 PyTorch Model Deployment/45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.srt15.2 KB
11. Section 09 PyTorch Model Deployment/46. Downloading the Food 101 Dataset.mp442.2 MB
11. Section 09 PyTorch Model Deployment/46. Downloading the Food 101 Dataset.srt11.1 KB
11. Section 09 PyTorch Model Deployment/47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.mp475.8 MB
11. Section 09 PyTorch Model Deployment/47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.srt20.0 KB
11. Section 09 PyTorch Model Deployment/48. Turning Our Food 101 Datasets into DataLoaders.mp437.3 MB
11. Section 09 PyTorch Model Deployment/48. Turning Our Food 101 Datasets into DataLoaders.srt8.3 KB
11. Section 09 PyTorch Model Deployment/49. Training Food Vision Big Our Biggest Model Yet!.mp4115.7 MB
11. Section 09 PyTorch Model Deployment/49. Training Food Vision Big Our Biggest Model Yet!.srt31.0 KB
11. Section 09 PyTorch Model Deployment/5. Some Tools and Places to Deploy Machine Learning Models.mp438.6 MB
11. Section 09 PyTorch Model Deployment/5. Some Tools and Places to Deploy Machine Learning Models.srt9.3 KB
11. Section 09 PyTorch Model Deployment/50. Outlining the File Structure for Our Food Vision Big.mp430.9 MB
11. Section 09 PyTorch Model Deployment/50. Outlining the File Structure for Our Food Vision Big.srt8.4 KB
11. Section 09 PyTorch Model Deployment/51. Downloading an Example Image and Moving Our Food Vision Big Model File.mp421.8 MB
11. Section 09 PyTorch Model Deployment/51. Downloading an Example Image and Moving Our Food Vision Big Model File.srt5.8 KB
11. Section 09 PyTorch Model Deployment/52. Saving Food 101 Class Names to a Text File and Reading them Back In.mp442.9 MB
11. Section 09 PyTorch Model Deployment/52. Saving Food 101 Class Names to a Text File and Reading them Back In.srt10.4 KB
11. Section 09 PyTorch Model Deployment/53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.mp415.7 MB
11. Section 09 PyTorch Model Deployment/53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.srt3.5 KB
11. Section 09 PyTorch Model Deployment/54. Creating an App Script for Our Food Vision Big Model Gradio Demo.mp467.0 MB
11. Section 09 PyTorch Model Deployment/54. Creating an App Script for Our Food Vision Big Model Gradio Demo.srt15.7 KB
11. Section 09 PyTorch Model Deployment/55. Zipping and Downloading Our Food Vision Big App Files.mp425.9 MB
11. Section 09 PyTorch Model Deployment/55. Zipping and Downloading Our Food Vision Big App Files.srt5.6 KB
11. Section 09 PyTorch Model Deployment/56. Deploying Food Vision Big to Hugging Face Spaces.mp495.7 MB
11. Section 09 PyTorch Model Deployment/56. Deploying Food Vision Big to Hugging Face Spaces.srt21.0 KB
11. Section 09 PyTorch Model Deployment/57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.mp463.4 MB
11. Section 09 PyTorch Model Deployment/57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.srt10.5 KB
11. Section 09 PyTorch Model Deployment/6. What We Are Going to Cover.mp418.6 MB
11. Section 09 PyTorch Model Deployment/6. What We Are Going to Cover.srt8.2 KB
11. Section 09 PyTorch Model Deployment/7. Getting Setup to Code.mp441.1 MB
11. Section 09 PyTorch Model Deployment/7. Getting Setup to Code.srt9.3 KB
11. Section 09 PyTorch Model Deployment/8. Downloading a Dataset for Food Vision Mini.mp424.4 MB
11. Section 09 PyTorch Model Deployment/8. Downloading a Dataset for Food Vision Mini.srt5.4 KB
11. Section 09 PyTorch Model Deployment/9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.mp432.4 MB
11. Section 09 PyTorch Model Deployment/9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.srt12.0 KB
12. Introduction to PyTorch 2.0 and torch.compile/1. Introduction to PyTorch 2.0.mp460.7 MB
12. Introduction to PyTorch 2.0 and torch.compile/1. Introduction to PyTorch 2.0.srt9.5 KB
12. Introduction to PyTorch 2.0 and torch.compile/10. Discussing How to Get Better Relative Speedups for Training Models.mp439.8 MB
12. Introduction to PyTorch 2.0 and torch.compile/10. Discussing How to Get Better Relative Speedups for Training Models.srt11.3 KB
12. Introduction to PyTorch 2.0 and torch.compile/11. Setting the Batch Size and Data Size Programmatically.mp442.8 MB
12. Introduction to PyTorch 2.0 and torch.compile/11. Setting the Batch Size and Data Size Programmatically.srt10.6 KB
12. Introduction to PyTorch 2.0 and torch.compile/12. Getting More Speedups with TensorFloat-32.mp455.8 MB
12. Introduction to PyTorch 2.0 and torch.compile/12. Getting More Speedups with TensorFloat-32.srt14.6 KB
12. Introduction to PyTorch 2.0 and torch.compile/13. Downloading the CIFAR10 Dataset.mp439.3 MB
12. Introduction to PyTorch 2.0 and torch.compile/13. Downloading the CIFAR10 Dataset.srt11.0 KB
12. Introduction to PyTorch 2.0 and torch.compile/14. Creating Training and Test DataLoaders.mp442.8 MB
12. Introduction to PyTorch 2.0 and torch.compile/14. Creating Training and Test DataLoaders.srt11.8 KB
12. Introduction to PyTorch 2.0 and torch.compile/15. Preparing Training and Testing Loops with Timing Steps.mp440.1 MB
12. Introduction to PyTorch 2.0 and torch.compile/15. Preparing Training and Testing Loops with Timing Steps.srt7.4 KB
12. Introduction to PyTorch 2.0 and torch.compile/16. Experiment 1 - Single Run without Torch Compile.mp450.7 MB
12. Introduction to PyTorch 2.0 and torch.compile/16. Experiment 1 - Single Run without Torch Compile.srt14.7 KB
12. Introduction to PyTorch 2.0 and torch.compile/17. Experiment 2 - Single Run with Torch Compile.mp470.2 MB
12. Introduction to PyTorch 2.0 and torch.compile/17. Experiment 2 - Single Run with Torch Compile.srt17.0 KB
12. Introduction to PyTorch 2.0 and torch.compile/18. Comparing the Results of Experiments 1 and 2.mp483.1 MB
12. Introduction to PyTorch 2.0 and torch.compile/18. Comparing the Results of Experiments 1 and 2.srt15.6 KB
12. Introduction to PyTorch 2.0 and torch.compile/19. Saving the Results of Experiments 1 and 2.mp439.1 MB
12. Introduction to PyTorch 2.0 and torch.compile/19. Saving the Results of Experiments 1 and 2.srt7.0 KB
12. Introduction to PyTorch 2.0 and torch.compile/2. What We Are Going to Cover and PyTorch 2 Reference Materials.mp410.0 MB
12. Introduction to PyTorch 2.0 and torch.compile/2. What We Are Going to Cover and PyTorch 2 Reference Materials.srt2.4 KB
12. Introduction to PyTorch 2.0 and torch.compile/20. Preparing Functions for Experiments 3 and 4.mp478.9 MB
12. Introduction to PyTorch 2.0 and torch.compile/20. Preparing Functions for Experiments 3 and 4.srt20.9 KB
12. Introduction to PyTorch 2.0 and torch.compile/21. Experiment 3 - Training a Non-Compiled Model for Multiple Runs.mp490.4 MB
12. Introduction to PyTorch 2.0 and torch.compile/21. Experiment 3 - Training a Non-Compiled Model for Multiple Runs.srt15.2 KB
12. Introduction to PyTorch 2.0 and torch.compile/22. Experiment 4 - Training a Compiled Model for Multiple Runs.mp474.9 MB
12. Introduction to PyTorch 2.0 and torch.compile/22. Experiment 4 - Training a Compiled Model for Multiple Runs.srt15.2 KB
12. Introduction to PyTorch 2.0 and torch.compile/23. Comparing the Results of Experiments 3 and 4.mp439.8 MB
12. Introduction to PyTorch 2.0 and torch.compile/23. Comparing the Results of Experiments 3 and 4.srt7.3 KB
12. Introduction to PyTorch 2.0 and torch.compile/24. Potential Extensions and Resources to Learn More.mp440.4 MB
12. Introduction to PyTorch 2.0 and torch.compile/24. Potential Extensions and Resources to Learn More.srt9.5 KB
12. Introduction to PyTorch 2.0 and torch.compile/3. Getting Started with PyTorch 2.0 in Google Colab.mp426.5 MB
12. Introduction to PyTorch 2.0 and torch.compile/3. Getting Started with PyTorch 2.0 in Google Colab.srt7.0 KB
12. Introduction to PyTorch 2.0 and torch.compile/4. PyTorch 2.0 - 30 Second Intro.mp412.1 MB
12. Introduction to PyTorch 2.0 and torch.compile/4. PyTorch 2.0 - 30 Second Intro.srt5.2 KB
12. Introduction to PyTorch 2.0 and torch.compile/5. Getting Setup for PyTorch 2.0.mp417.1 MB
12. Introduction to PyTorch 2.0 and torch.compile/5. Getting Setup for PyTorch 2.0.srt3.5 KB
12. Introduction to PyTorch 2.0 and torch.compile/6. Getting Info from Our GPUs and Seeing if They're Capable of Using PyTorch 2.0.mp448.2 MB
12. Introduction to PyTorch 2.0 and torch.compile/6. Getting Info from Our GPUs and Seeing if They're Capable of Using PyTorch 2.0.srt10.0 KB
12. Introduction to PyTorch 2.0 and torch.compile/7. Setting the Default Device in PyTorch 2.0.mp471.1 MB
12. Introduction to PyTorch 2.0 and torch.compile/7. Setting the Default Device in PyTorch 2.0.srt15.2 KB
12. Introduction to PyTorch 2.0 and torch.compile/8. Discussing the Experiments We Are Going to Run for PyTorch 2.0.mp434.4 MB
12. Introduction to PyTorch 2.0 and torch.compile/8. Discussing the Experiments We Are Going to Run for PyTorch 2.0.srt9.4 KB
12. Introduction to PyTorch 2.0 and torch.compile/9. Creating a Function to Setup Our Model and Transforms.mp467.0 MB
12. Introduction to PyTorch 2.0 and torch.compile/9. Creating a Function to Setup Our Model and Transforms.srt15.4 KB
13. Where To Go From Here/1. Thank You!.mp49.6 MB
13. Where To Go From Here/1. Thank You!.srt2.0 KB
13. Where To Go From Here/2. Review This Course!.html341.7 KB
13. Where To Go From Here/3. Become An Alumni.html342.6 KB
13. Where To Go From Here/4. Learning Guideline.html343.5 KB
13. Where To Go From Here/5. ZTM Events Every Month.html344.4 KB
13. Where To Go From Here/6. LinkedIn Endorsements.html347.1 KB
2. Section 00 PyTorch Fundamentals/1. Why Use Machine Learning or Deep Learning.mp48.1 MB
2. Section 00 PyTorch Fundamentals/1. Why Use Machine Learning or Deep Learning.srt6.8 KB
2. Section 00 PyTorch Fundamentals/10. How To and How Not To Approach This Course.mp424.1 MB
2. Section 00 PyTorch Fundamentals/10. How To and How Not To Approach This Course.srt9.5 KB
2. Section 00 PyTorch Fundamentals/11. Important Resources For This Course.mp436.6 MB
2. Section 00 PyTorch Fundamentals/11. Important Resources For This Course.srt9.7 KB
2. Section 00 PyTorch Fundamentals/12. Getting Setup to Write PyTorch Code.mp443.3 MB
2. Section 00 PyTorch Fundamentals/12. Getting Setup to Write PyTorch Code.srt13.2 KB
2. Section 00 PyTorch Fundamentals/13. Introduction to PyTorch Tensors.mp457.6 MB
2. Section 00 PyTorch Fundamentals/13. Introduction to PyTorch Tensors.srt22.1 KB
2. Section 00 PyTorch Fundamentals/14. Creating Random Tensors in PyTorch.mp451.1 MB
2. Section 00 PyTorch Fundamentals/14. Creating Random Tensors in PyTorch.srt16.0 KB
2. Section 00 PyTorch Fundamentals/15. Creating Tensors With Zeros and Ones in PyTorch.mp413.9 MB
2. Section 00 PyTorch Fundamentals/15. Creating Tensors With Zeros and Ones in PyTorch.srt4.2 KB
2. Section 00 PyTorch Fundamentals/16. Creating a Tensor Range and Tensors Like Other Tensors.mp417.4 MB
2. Section 00 PyTorch Fundamentals/16. Creating a Tensor Range and Tensors Like Other Tensors.srt6.5 KB
2. Section 00 PyTorch Fundamentals/17. Dealing With Tensor Data Types.mp448.3 MB
2. Section 00 PyTorch Fundamentals/17. Dealing With Tensor Data Types.srt13.9 KB
2. Section 00 PyTorch Fundamentals/18. Getting Tensor Attributes.mp440.3 MB
2. Section 00 PyTorch Fundamentals/18. Getting Tensor Attributes.srt13.7 KB
2. Section 00 PyTorch Fundamentals/19. Manipulating Tensors (Tensor Operations).mp421.8 MB
2. Section 00 PyTorch Fundamentals/19. Manipulating Tensors (Tensor Operations).srt8.4 KB
2. Section 00 PyTorch Fundamentals/2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.mp418.1 MB
2. Section 00 PyTorch Fundamentals/2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.srt10.2 KB
2. Section 00 PyTorch Fundamentals/20. Matrix Multiplication (Part 1).mp443.5 MB
2. Section 00 PyTorch Fundamentals/20. Matrix Multiplication (Part 1).srt13.0 KB
2. Section 00 PyTorch Fundamentals/21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.mp431.3 MB
2. Section 00 PyTorch Fundamentals/21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.srt12.0 KB
2. Section 00 PyTorch Fundamentals/22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.mp457.1 MB
2. Section 00 PyTorch Fundamentals/22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.srt19.4 KB
2. Section 00 PyTorch Fundamentals/23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).mp429.3 MB
2. Section 00 PyTorch Fundamentals/23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).srt9.9 KB
2. Section 00 PyTorch Fundamentals/24. Finding The Positional Min and Max of Tensors.mp413.1 MB
2. Section 00 PyTorch Fundamentals/24. Finding The Positional Min and Max of Tensors.srt4.3 KB
2. Section 00 PyTorch Fundamentals/25. Reshaping, Viewing and Stacking Tensors.mp460.5 MB
2. Section 00 PyTorch Fundamentals/25. Reshaping, Viewing and Stacking Tensors.srt21.3 KB
2. Section 00 PyTorch Fundamentals/26. Squeezing, Unsqueezing and Permuting Tensors.mp450.1 MB
2. Section 00 PyTorch Fundamentals/26. Squeezing, Unsqueezing and Permuting Tensors.srt15.3 KB
2. Section 00 PyTorch Fundamentals/27. Selecting Data From Tensors (Indexing).mp430.8 MB
2. Section 00 PyTorch Fundamentals/27. Selecting Data From Tensors (Indexing).srt13.7 KB
2. Section 00 PyTorch Fundamentals/28. PyTorch Tensors and NumPy.en.copy.srt12.6 KB
2. Section 00 PyTorch Fundamentals/28. PyTorch Tensors and NumPy.mp434.2 MB
2. Section 00 PyTorch Fundamentals/28. PyTorch Tensors and NumPy.srt12.6 KB
2. Section 00 PyTorch Fundamentals/29. PyTorch Reproducibility (Taking the Random Out of Random).mp459.2 MB
2. Section 00 PyTorch Fundamentals/29. PyTorch Reproducibility (Taking the Random Out of Random).srt16.0 KB
2. Section 00 PyTorch Fundamentals/3. Machine Learning vs. Deep Learning.mp430.5 MB
2. Section 00 PyTorch Fundamentals/3. Machine Learning vs. Deep Learning.srt10.6 KB
2. Section 00 PyTorch Fundamentals/30. Different Ways of Accessing a GPU in PyTorch.mp471.6 MB
2. Section 00 PyTorch Fundamentals/30. Different Ways of Accessing a GPU in PyTorch.srt16.1 KB
2. Section 00 PyTorch Fundamentals/31. Setting up Device Agnostic Code and Putting Tensors On and Off the GPU.mp441.0 MB
2. Section 00 PyTorch Fundamentals/31. Setting up Device Agnostic Code and Putting Tensors On and Off the GPU.srt11.6 KB
2. Section 00 PyTorch Fundamentals/32. PyTorch Fundamentals Exercises and Extra-Curriculum.mp435.7 MB
2. Section 00 PyTorch Fundamentals/32. PyTorch Fundamentals Exercises and Extra-Curriculum.srt7.8 KB
2. Section 00 PyTorch Fundamentals/33. Let's Have Some Fun (+ Free Resources).html38.8 KB
2. Section 00 PyTorch Fundamentals/4. Anatomy of Neural Networks.mp444.2 MB
2. Section 00 PyTorch Fundamentals/4. Anatomy of Neural Networks.srt16.3 KB
2. Section 00 PyTorch Fundamentals/5. Different Types of Learning Paradigms.mp417.9 MB
2. Section 00 PyTorch Fundamentals/5. Different Types of Learning Paradigms.srt7.3 KB
2. Section 00 PyTorch Fundamentals/6. What Can Deep Learning Be Used For.mp422.6 MB
2. Section 00 PyTorch Fundamentals/6. What Can Deep Learning Be Used For.srt10.6 KB
2. Section 00 PyTorch Fundamentals/7. What Is and Why PyTorch.mp474.2 MB
2. Section 00 PyTorch Fundamentals/7. What Is and Why PyTorch.srt17.4 KB
2. Section 00 PyTorch Fundamentals/8. What Are Tensors.mp413.8 MB
2. Section 00 PyTorch Fundamentals/8. What Are Tensors.srt8.0 KB
2. Section 00 PyTorch Fundamentals/9. What We Are Going To Cover With PyTorch.mp429.6 MB
2. Section 00 PyTorch Fundamentals/9. What We Are Going To Cover With PyTorch.srt12.0 KB
3. Section 01 PyTorch Workflow/1. Introduction and Where You Can Get Help.mp418.7 MB
3. Section 01 PyTorch Workflow/1. Introduction and Where You Can Get Help.srt5.5 KB
3. Section 01 PyTorch Workflow/10. Making Predictions With Our Random Model Using Inference Mode.mp467.7 MB
3. Section 01 PyTorch Workflow/10. Making Predictions With Our Random Model Using Inference Mode.srt16.1 KB
3. Section 01 PyTorch Workflow/11. Training a Model Intuition (The Things We Need).mp442.4 MB
3. Section 01 PyTorch Workflow/11. Training a Model Intuition (The Things We Need).srt13.4 KB
3. Section 01 PyTorch Workflow/12. Setting Up an Optimizer and a Loss Function.mp473.6 MB
3. Section 01 PyTorch Workflow/12. Setting Up an Optimizer and a Loss Function.srt22.1 KB
3. Section 01 PyTorch Workflow/13. PyTorch Training Loop Steps and Intuition.mp477.3 MB
3. Section 01 PyTorch Workflow/13. PyTorch Training Loop Steps and Intuition.srt18.0 KB
3. Section 01 PyTorch Workflow/14. Writing Code for a PyTorch Training Loop.mp451.9 MB
3. Section 01 PyTorch Workflow/14. Writing Code for a PyTorch Training Loop.srt14.0 KB
3. Section 01 PyTorch Workflow/15. Reviewing the Steps in a Training Loop Step by Step.mp4109.5 MB
3. Section 01 PyTorch Workflow/15. Reviewing the Steps in a Training Loop Step by Step.srt25.3 KB
3. Section 01 PyTorch Workflow/16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.mp463.4 MB
3. Section 01 PyTorch Workflow/16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.srt16.7 KB
3. Section 01 PyTorch Workflow/17. Writing Testing Loop Code and Discussing What's Happening Step by Step.mp489.7 MB
3. Section 01 PyTorch Workflow/17. Writing Testing Loop Code and Discussing What's Happening Step by Step.srt20.7 KB
3. Section 01 PyTorch Workflow/18. Reviewing What Happens in a Testing Loop Step by Step.mp4106.9 MB
3. Section 01 PyTorch Workflow/18. Reviewing What Happens in a Testing Loop Step by Step.srt20.9 KB
3. Section 01 PyTorch Workflow/19. Writing Code to Save a PyTorch Model.mp482.8 MB
3. Section 01 PyTorch Workflow/19. Writing Code to Save a PyTorch Model.srt22.7 KB
3. Section 01 PyTorch Workflow/2. Getting Setup and What We Are Covering.mp443.8 MB
3. Section 01 PyTorch Workflow/2. Getting Setup and What We Are Covering.srt12.4 KB
3. Section 01 PyTorch Workflow/20. Writing Code to Load a PyTorch Model.mp448.5 MB
3. Section 01 PyTorch Workflow/20. Writing Code to Load a PyTorch Model.srt13.1 KB
3. Section 01 PyTorch Workflow/21. Setting Up to Practice Everything We Have Done Using Device-Agnostic Code.mp426.0 MB
3. Section 01 PyTorch Workflow/21. Setting Up to Practice Everything We Have Done Using Device-Agnostic Code.srt10.0 KB
3. Section 01 PyTorch Workflow/22. Putting Everything Together (Part 1) Data.mp428.7 MB
3. Section 01 PyTorch Workflow/22. Putting Everything Together (Part 1) Data.srt9.7 KB
3. Section 01 PyTorch Workflow/23. Putting Everything Together (Part 2) Building a Model.mp453.4 MB
3. Section 01 PyTorch Workflow/23. Putting Everything Together (Part 2) Building a Model.srt16.0 KB
3. Section 01 PyTorch Workflow/24. Putting Everything Together (Part 3) Training a Model.mp461.6 MB
3. Section 01 PyTorch Workflow/24. Putting Everything Together (Part 3) Training a Model.srt20.7 KB
3. Section 01 PyTorch Workflow/25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.mp432.2 MB
3. Section 01 PyTorch Workflow/25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.srt9.0 KB
3. Section 01 PyTorch Workflow/26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.mp445.4 MB
3. Section 01 PyTorch Workflow/26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.srt13.1 KB
3. Section 01 PyTorch Workflow/27. PyTorch Workflow Exercises and Extra-Curriculum.mp432.3 MB
3. Section 01 PyTorch Workflow/27. PyTorch Workflow Exercises and Extra-Curriculum.srt7.2 KB
3. Section 01 PyTorch Workflow/28. Unlimited Updates.html65.5 KB
3. Section 01 PyTorch Workflow/3. Creating a Simple Dataset Using the Linear Regression Formula.mp438.1 MB
3. Section 01 PyTorch Workflow/3. Creating a Simple Dataset Using the Linear Regression Formula.srt15.3 KB
3. Section 01 PyTorch Workflow/4. Splitting Our Data Into Training and Test Sets.mp436.9 MB
3. Section 01 PyTorch Workflow/4. Splitting Our Data Into Training and Test Sets.srt13.7 KB
3. Section 01 PyTorch Workflow/5. Building a function to Visualize Our Data.mp435.8 MB
3. Section 01 PyTorch Workflow/5. Building a function to Visualize Our Data.srt12.8 KB
3. Section 01 PyTorch Workflow/6. Creating Our First PyTorch Model for Linear Regression.mp476.2 MB
3. Section 01 PyTorch Workflow/6. Creating Our First PyTorch Model for Linear Regression.srt19.5 KB
3. Section 01 PyTorch Workflow/7. Breaking Down What's Happening in Our PyTorch Linear regression Model.mp438.5 MB
3. Section 01 PyTorch Workflow/7. Breaking Down What's Happening in Our PyTorch Linear regression Model.srt9.5 KB
3. Section 01 PyTorch Workflow/8. Discussing Some of the Most Important PyTorch Model Building Classes.mp449.8 MB
3. Section 01 PyTorch Workflow/8. Discussing Some of the Most Important PyTorch Model Building Classes.srt8.8 KB
3. Section 01 PyTorch Workflow/9. Checking Out the Internals of Our PyTorch Model.mp468.4 MB
3. Section 01 PyTorch Workflow/9. Checking Out the Internals of Our PyTorch Model.srt15.9 KB
4. Section 02 PyTorch Neural Network Classification/1. Introduction to Machine Learning Classification With PyTorch.mp454.0 MB
4. Section 02 PyTorch Neural Network Classification/1. Introduction to Machine Learning Classification With PyTorch.srt17.4 KB
4. Section 02 PyTorch Neural Network Classification/10. Setting Up a Loss Function Optimizer and Evaluation Function for Our Classification Network.mp4107.0 MB
4. Section 02 PyTorch Neural Network Classification/10. Setting Up a Loss Function Optimizer and Evaluation Function for Our Classification Network.srt24.9 KB
4. Section 02 PyTorch Neural Network Classification/11. Going from Model Logits to Prediction Probabilities to Prediction Labels.mp481.5 MB
4. Section 02 PyTorch Neural Network Classification/11. Going from Model Logits to Prediction Probabilities to Prediction Labels.srt24.2 KB
4. Section 02 PyTorch Neural Network Classification/12. Coding a Training and Testing Optimization Loop for Our Classification Model.mp476.4 MB
4. Section 02 PyTorch Neural Network Classification/12. Coding a Training and Testing Optimization Loop for Our Classification Model.srt24.6 KB
4. Section 02 PyTorch Neural Network Classification/13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.mp496.8 MB
4. Section 02 PyTorch Neural Network Classification/13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.srt24.0 KB
4. Section 02 PyTorch Neural Network Classification/14. Discussing Options to Improve a Model.mp447.3 MB
4. Section 02 PyTorch Neural Network Classification/14. Discussing Options to Improve a Model.srt14.3 KB
4. Section 02 PyTorch Neural Network Classification/15. Creating a New Model with More Layers and Hidden Units.mp439.3 MB
4. Section 02 PyTorch Neural Network Classification/15. Creating a New Model with More Layers and Hidden Units.srt13.2 KB
4. Section 02 PyTorch Neural Network Classification/16. Writing Training and Testing Code to See if Our New and Upgraded Model Performs Better.mp478.4 MB
4. Section 02 PyTorch Neural Network Classification/16. Writing Training and Testing Code to See if Our New and Upgraded Model Performs Better.srt20.9 KB
4. Section 02 PyTorch Neural Network Classification/17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.mp437.0 MB
4. Section 02 PyTorch Neural Network Classification/17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.srt11.8 KB
4. Section 02 PyTorch Neural Network Classification/18. Building and Training a Model to Fit on Straight Line Data.mp440.7 MB
4. Section 02 PyTorch Neural Network Classification/18. Building and Training a Model to Fit on Straight Line Data.srt16.5 KB
4. Section 02 PyTorch Neural Network Classification/19. Evaluating Our Models Predictions on Straight Line Data.mp429.0 MB
4. Section 02 PyTorch Neural Network Classification/19. Evaluating Our Models Predictions on Straight Line Data.srt9.4 KB
4. Section 02 PyTorch Neural Network Classification/2. Classification Problem Example Input and Output Shapes.mp428.1 MB
4. Section 02 PyTorch Neural Network Classification/2. Classification Problem Example Input and Output Shapes.srt16.1 KB
4. Section 02 PyTorch Neural Network Classification/20. Introducing the Missing Piece for Our Classification Model Non-Linearity.mp460.9 MB
4. Section 02 PyTorch Neural Network Classification/20. Introducing the Missing Piece for Our Classification Model Non-Linearity.srt17.2 KB
4. Section 02 PyTorch Neural Network Classification/21. Building Our First Neural Network with Non-Linearity.mp451.6 MB
4. Section 02 PyTorch Neural Network Classification/21. Building Our First Neural Network with Non-Linearity.srt17.9 KB
4. Section 02 PyTorch Neural Network Classification/22. Writing Training and Testing Code for Our First Non-Linear Model.mp489.5 MB
4. Section 02 PyTorch Neural Network Classification/22. Writing Training and Testing Code for Our First Non-Linear Model.srt18.8 KB
4. Section 02 PyTorch Neural Network Classification/23. Making Predictions with and Evaluating Our First Non-Linear Model.mp429.7 MB
4. Section 02 PyTorch Neural Network Classification/23. Making Predictions with and Evaluating Our First Non-Linear Model.srt9.3 KB
4. Section 02 PyTorch Neural Network Classification/24. Replicating Non-Linear Activation Functions with Pure PyTorch.mp446.7 MB
4. Section 02 PyTorch Neural Network Classification/24. Replicating Non-Linear Activation Functions with Pure PyTorch.srt15.0 KB
4. Section 02 PyTorch Neural Network Classification/25. Putting It All Together (Part 1) Building a Multiclass Dataset.mp457.5 MB
4. Section 02 PyTorch Neural Network Classification/25. Putting It All Together (Part 1) Building a Multiclass Dataset.srt19.3 KB
4. Section 02 PyTorch Neural Network Classification/26. Creating a Multi-Class Classification Model with PyTorch.mp461.5 MB
4. Section 02 PyTorch Neural Network Classification/26. Creating a Multi-Class Classification Model with PyTorch.srt20.8 KB
4. Section 02 PyTorch Neural Network Classification/27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.mp440.3 MB
4. Section 02 PyTorch Neural Network Classification/27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.srt10.0 KB
4. Section 02 PyTorch Neural Network Classification/28. Going from Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.mp455.0 MB
4. Section 02 PyTorch Neural Network Classification/28. Going from Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.srt17.0 KB
4. Section 02 PyTorch Neural Network Classification/29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.mp496.0 MB
4. Section 02 PyTorch Neural Network Classification/29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.srt23.6 KB
4. Section 02 PyTorch Neural Network Classification/3. Typical Architecture of a Classification Neural Network (Overview).mp442.4 MB
4. Section 02 PyTorch Neural Network Classification/3. Typical Architecture of a Classification Neural Network (Overview).srt11.5 KB
4. Section 02 PyTorch Neural Network Classification/30. Making Predictions with and Evaluating Our Multi-Class Classification Model.mp447.2 MB
4. Section 02 PyTorch Neural Network Classification/30. Making Predictions with and Evaluating Our Multi-Class Classification Model.srt12.1 KB
4. Section 02 PyTorch Neural Network Classification/31. Discussing a Few More Classification Metrics.mp460.2 MB
4. Section 02 PyTorch Neural Network Classification/31. Discussing a Few More Classification Metrics.srt15.5 KB
4. Section 02 PyTorch Neural Network Classification/32. PyTorch Classification Exercises and Extra-Curriculum.mp431.4 MB
4. Section 02 PyTorch Neural Network Classification/32. PyTorch Classification Exercises and Extra-Curriculum.srt4.8 KB
4. Section 02 PyTorch Neural Network Classification/33. Course Check-In.html97.1 KB
4. Section 02 PyTorch Neural Network Classification/4. Making a Toy Classification Dataset.mp452.0 MB
4. Section 02 PyTorch Neural Network Classification/4. Making a Toy Classification Dataset.srt19.1 KB
4. Section 02 PyTorch Neural Network Classification/5. Turning Our Data into Tensors and Making a Training and Test Split.mp447.4 MB
4. Section 02 PyTorch Neural Network Classification/5. Turning Our Data into Tensors and Making a Training and Test Split.srt19.2 KB
4. Section 02 PyTorch Neural Network Classification/6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.mp417.9 MB
4. Section 02 PyTorch Neural Network Classification/6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.srt6.7 KB
4. Section 02 PyTorch Neural Network Classification/7. Coding a Small Neural Network to Handle Our Classification Data.mp449.3 MB
4. Section 02 PyTorch Neural Network Classification/7. Coding a Small Neural Network to Handle Our Classification Data.srt16.3 KB
4. Section 02 PyTorch Neural Network Classification/8. Making Our Neural Network Visual.mp445.9 MB
4. Section 02 PyTorch Neural Network Classification/8. Making Our Neural Network Visual.srt12.4 KB
4. Section 02 PyTorch Neural Network Classification/9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.mp467.8 MB
4. Section 02 PyTorch Neural Network Classification/9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.srt17.5 KB
5. Section 03 PyTorch Computer Vision/1. What Is a Computer Vision Problem and What We Are Going to Cover.mp465.3 MB
5. Section 03 PyTorch Computer Vision/1. What Is a Computer Vision Problem and What We Are Going to Cover.srt18.8 KB
5. Section 03 PyTorch Computer Vision/10. Creating a Loss Function an Optimizer for Model 0.mp471.8 MB
5. Section 03 PyTorch Computer Vision/10. Creating a Loss Function an Optimizer for Model 0.srt16.2 KB
5. Section 03 PyTorch Computer Vision/11. Creating a Function to Time Our Modelling Code.mp429.3 MB
5. Section 03 PyTorch Computer Vision/11. Creating a Function to Time Our Modelling Code.srt8.9 KB
5. Section 03 PyTorch Computer Vision/12. Writing Training and Testing Loops for Our Batched Data.mp490.2 MB
5. Section 03 PyTorch Computer Vision/12. Writing Training and Testing Loops for Our Batched Data.srt28.1 KB
5. Section 03 PyTorch Computer Vision/13. Writing an Evaluation Function to Get Our Models Results.mp462.5 MB
5. Section 03 PyTorch Computer Vision/13. Writing an Evaluation Function to Get Our Models Results.srt22.4 KB
5. Section 03 PyTorch Computer Vision/14. Setup Device-Agnostic Code for Running Experiments on the GPU.mp429.6 MB
5. Section 03 PyTorch Computer Vision/14. Setup Device-Agnostic Code for Running Experiments on the GPU.srt6.8 KB
5. Section 03 PyTorch Computer Vision/15. Model 1 Creating a Model with Non-Linear Functions.mp455.1 MB
5. Section 03 PyTorch Computer Vision/15. Model 1 Creating a Model with Non-Linear Functions.srt15.2 KB
5. Section 03 PyTorch Computer Vision/16. Model 1 Creating a Loss Function and Optimizer.mp421.2 MB
5. Section 03 PyTorch Computer Vision/16. Model 1 Creating a Loss Function and Optimizer.srt4.9 KB
5. Section 03 PyTorch Computer Vision/17. Turing Our Training Loop into a Function.mp439.7 MB
5. Section 03 PyTorch Computer Vision/17. Turing Our Training Loop into a Function.srt13.0 KB
5. Section 03 PyTorch Computer Vision/18. Turing Our Testing Loop into a Function.mp430.8 MB
5. Section 03 PyTorch Computer Vision/18. Turing Our Testing Loop into a Function.srt9.9 KB
5. Section 03 PyTorch Computer Vision/19. Training and Testing Model 1 with Our Training and Testing Functions.mp466.2 MB
5. Section 03 PyTorch Computer Vision/19. Training and Testing Model 1 with Our Training and Testing Functions.srt18.3 KB
5. Section 03 PyTorch Computer Vision/2. Computer Vision Input and Output Shapes.mp446.9 MB
5. Section 03 PyTorch Computer Vision/2. Computer Vision Input and Output Shapes.srt18.3 KB
5. Section 03 PyTorch Computer Vision/20. Getting a Results Dictionary for Model 1.mp425.1 MB
5. Section 03 PyTorch Computer Vision/20. Getting a Results Dictionary for Model 1.srt7.1 KB
5. Section 03 PyTorch Computer Vision/21. Model 2 Convolutional Neural Networks High Level Overview.mp452.3 MB
5. Section 03 PyTorch Computer Vision/21. Model 2 Convolutional Neural Networks High Level Overview.srt14.4 KB
5. Section 03 PyTorch Computer Vision/22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.mp4124.7 MB
5. Section 03 PyTorch Computer Vision/22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.srt34.4 KB
5. Section 03 PyTorch Computer Vision/23. Model 2 Breaking Down Conv2D Step by Step.mp499.8 MB
5. Section 03 PyTorch Computer Vision/23. Model 2 Breaking Down Conv2D Step by Step.srt25.5 KB
5. Section 03 PyTorch Computer Vision/24. Model 2 Breaking Down MaxPool2D Step by Step.mp485.2 MB
5. Section 03 PyTorch Computer Vision/24. Model 2 Breaking Down MaxPool2D Step by Step.srt24.7 KB
5. Section 03 PyTorch Computer Vision/25. Model 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.mp4111.6 MB
5. Section 03 PyTorch Computer Vision/25. Model 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.srt21.8 KB
5. Section 03 PyTorch Computer Vision/26. Model 2 Setting Up a Loss Function and Optimizer.mp418.2 MB
5. Section 03 PyTorch Computer Vision/26. Model 2 Setting Up a Loss Function and Optimizer.srt4.0 KB

另有 208 个文件未显示。

举报这个资源

ZeroToMastery - PyTorch for Deep Learning Bootcamp Zero to Mastery (4.2025)

播放列表

文件列表

相关资源