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Udemy PyTorch for Deep Learning in 2023 Zero to Mastery

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860

Type:

Tutorials

Language:

English

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None

Category:

Other

Size:

29.7 GB

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Aug. 22, 2023, 8:21 a.m.

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Files:

[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
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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
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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
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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

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