ZeroToMastery PyTorch for Deep Learning Bootcamp Zero to Mastery 2025

/1. Introduction/

1. PyTorch for Deep Learning Bootcamp Zero to Mastery.mp4

1. PyTorch for Deep Learning Bootcamp Zero to Mastery.srt

2. Course Welcome and What Is Deep Learning.mp4

2. Course Welcome and What Is Deep Learning.srt

3. Exercise Meet Your Classmates and Instructor.html

4. Course Companion Book + Code + More.html

5. Machine Learning + Python Monthly.html

6. ZTM Plugin + Understanding Your Video Player.html

7. Set Your Learning Streak Goal.html

/10. Section 08 PyTorch Paper Replicating/

1. What Is a Machine Learning Research Paper.mp4

1. What Is a Machine Learning Research Paper.srt

10. Breaking Down Figure 1 of the ViT Paper.mp4

10. Breaking Down Figure 1 of the ViT Paper.srt

11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.mp4

11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.srt

12. Breaking Down Equation 1.mp4

12. Breaking Down Equation 1.srt

13. Breaking Down Equations 2 and 3.mp4

13. Breaking Down Equations 2 and 3.srt

14. Breaking Down Equation 4.mp4

14. Breaking Down Equation 4.srt

15. Breaking Down Table 1.mp4

15. Breaking Down Table 1.srt

16. Calculating the Input and Output Shape of the Embedding Layer by Hand.mp4

16. Calculating the Input and Output Shape of the Embedding Layer by Hand.srt

17. Turning a Single Image into Patches (Part 1 Patching the Top Row).mp4

17. Turning a Single Image into Patches (Part 1 Patching the Top Row).srt

18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).mp4

18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).srt

19. Creating Patch Embeddings with a Convolutional Layer.mp4

19. Creating Patch Embeddings with a Convolutional Layer.srt

2. Why Replicate a Machine Learning Research Paper.mp4

2. Why Replicate a Machine Learning Research Paper.srt

20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.mp4

20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.srt

21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.mp4

21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.srt

22. Visualizing a Single Sequence Vector of Patch Embeddings.mp4

22. Visualizing a Single Sequence Vector of Patch Embeddings.srt

23. Creating the Patch Embedding Layer with PyTorch.mp4

23. Creating the Patch Embedding Layer with PyTorch.srt

24. Creating the Class Token Embedding.mp4

24. Creating the Class Token Embedding.srt

25. Creating the Class Token Embedding - Less Birds.mp4

25. Creating the Class Token Embedding - Less Birds.srt

26. Creating the Position Embedding.mp4

26. Creating the Position Embedding.srt

27. Equation 1 Putting it All Together.mp4

27. Equation 1 Putting it All Together.srt

28. Equation 2 Multihead Attention Overview.mp4

28. Equation 2 Multihead Attention Overview.srt

29. Equation 2 Layernorm Overview.mp4

29. Equation 2 Layernorm Overview.srt

3. Where Can You Find Machine Learning Research Papers and Code.mp4

3. Where Can You Find Machine Learning Research Papers and Code.srt

30. Turning Equation 2 into Code.mp4

30. Turning Equation 2 into Code.srt

31. Checking the Inputs and Outputs of Equation.mp4

31. Checking the Inputs and Outputs of Equation.srt

32. Equation 3 Replication Overview.mp4

32. Equation 3 Replication Overview.srt

33. Turning Equation 3 into Code.mp4

33. Turning Equation 3 into Code.srt

34. Transformer Encoder Overview.mp4

34. Transformer Encoder Overview.srt

35. Combining Equation 2 and 3 to Create the Transformer Encoder.mp4

35. Combining Equation 2 and 3 to Create the Transformer Encoder.srt

36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.mp4

36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.srt

37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces of the Puzzle.mp4

37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces of the Puzzle.srt

38. Bringing Our Own Vision Transformer to Life - Part 2 Putting Together the Forward Method.mp4

38. Bringing Our Own Vision Transformer to Life - Part 2 Putting Together the Forward Method.srt

39. Getting a Visual Summary of Our Custom Vision Transformer.mp4

39. Getting a Visual Summary of Our Custom Vision Transformer.srt

4. What We Are Going to Cover.mp4

4. What We Are Going to Cover.srt

40. Creating a Loss Function and Optimizer from the ViT Paper.mp4

40. Creating a Loss Function and Optimizer from the ViT Paper.srt

41. Training our Custom ViT on Food Vision Mini.mp4

41. Training our Custom ViT on Food Vision Mini.srt

42. Discussing what Our Training Setup Is Missing.mp4

42. Discussing what Our Training Setup Is Missing.srt

43. Plotting a Loss Curve for Our ViT Model.mp4

43. Plotting a Loss Curve for Our ViT Model.srt

44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.mp4

44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.srt

45. Preparing Data to Be Used with a Pretrained ViT.mp4

45. Preparing Data to Be Used with a Pretrained ViT.srt

46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.mp4

46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.srt

47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.mp4

47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.srt

48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.mp4

48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.srt

49. Making Predictions on a Custom Image with Our Pretrained ViT.mp4

49. Making Predictions on a Custom Image with Our Pretrained ViT.srt

5. Getting Setup for Coding in Google Colab.mp4

5. Getting Setup for Coding in Google Colab.srt

50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.mp4

50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.srt

6. Downloading Data for Food Vision Mini.mp4

6. Downloading Data for Food Vision Mini.srt

7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.mp4

7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.srt

8. Visualizing a Single Image.mp4

8. Visualizing a Single Image.srt

9. Replicating a Vision Transformer - High Level Overview.mp4

9. Replicating a Vision Transformer - High Level Overview.srt

/11. Section 09 PyTorch Model Deployment/

1. What is Machine Learning Model Deployment and Why Deploy a Machine Learning Model.mp4

1. What is Machine Learning Model Deployment and Why Deploy a Machine Learning Model.srt

10. Creating an EffNetB2 Feature Extractor Model.mp4

10. Creating an EffNetB2 Feature Extractor Model.srt

11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.mp4

11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.srt

12. Creating DataLoaders for EffNetB2.mp4

13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.mp4

13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.srt

14. Saving Our EffNetB2 Model to File.mp4

14. Saving Our EffNetB2 Model to File.srt

15. Getting the Size of Our EffNetB2 Model in Megabytes.mp4

15. Getting the Size of Our EffNetB2 Model in Megabytes.srt

16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.mp4

16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.srt

17. Creating a Vision Transformer Feature Extractor Model.mp4

17. Creating a Vision Transformer Feature Extractor Model.srt

18. Creating DataLoaders for Our ViT Feature Extractor Model.mp4

18. Creating DataLoaders for Our ViT Feature Extractor Model.srt

19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.mp4

19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.srt

2. Three Questions to Ask for Machine Learning Model Deployment.mp4

2. Three Questions to Ask for Machine Learning Model Deployment.srt

20. Saving Our ViT Feature Extractor and Inspecting Its Size.mp4

20. Saving Our ViT Feature Extractor and Inspecting Its Size.srt

21. Collecting Stats About Our ViT Feature Extractor.mp4

21. Collecting Stats About Our ViT Feature Extractor.srt

22. Outlining the Steps for Making and Timing Predictions for Our Models.mp4

22. Outlining the Steps for Making and Timing Predictions for Our Models.srt

23. Creating a Function to Make and Time Predictions with Our Models.mp4

23. Creating a Function to Make and Time Predictions with Our Models.srt

24. Making and Timing Predictions with EffNetB2.mp4

24. Making and Timing Predictions with EffNetB2.srt

25. Making and Timing Predictions with ViT.mp4

25. Making and Timing Predictions with ViT.srt

26. Comparing EffNetB2 and ViT Model Statistics.mp4

26. Comparing EffNetB2 and ViT Model Statistics.srt

27. Visualizing the Performance vs Speed Trade-off.mp4

27. Visualizing the Performance vs Speed Trade-off.srt

28. Gradio Overview and Installation.mp4

28. Gradio Overview and Installation.srt

29. Gradio Function Outline.mp4

3. Where Is My Model Going to Go.mp4

3. Where Is My Model Going to Go.srt

30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.mp4

30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.srt

31. Creating a List of Examples to Pass to Our Gradio Demo.mp4

31. Creating a List of Examples to Pass to Our Gradio Demo.srt

32. Bringing Food Vision Mini to Life in a Live Web Application.mp4

32. Bringing Food Vision Mini to Life in a Live Web Application.srt

33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.mp4

33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.srt

34. Outlining the File Structure of Our Deployed App.mp4

34. Outlining the File Structure of Our Deployed App.srt

35. Creating a Food Vision Mini Demo Directory to House Our App Files.mp4

35. Creating a Food Vision Mini Demo Directory to House Our App Files.srt

36. Creating an Examples Directory with Example Food Vision Mini Images.mp4

36. Creating an Examples Directory with Example Food Vision Mini Images.srt

37. Writing Code to Move Our Saved EffNetB2 Model File.mp4

37. Writing Code to Move Our Saved EffNetB2 Model File.srt

38. Turning Our EffNetB2 Model Creation Function Into a Python Script.mp4

38. Turning Our EffNetB2 Model Creation Function Into a Python Script.srt

39. Turning Our Food Vision Mini Demo App Into a Python Script.mp4

39. Turning Our Food Vision Mini Demo App Into a Python Script.srt

4. How Is My Model Going to Function.mp4

4. How Is My Model Going to Function.srt

40. Creating a Requirements File for Our Food Vision Mini App.mp4

40. Creating a Requirements File for Our Food Vision Mini App.srt

41. Downloading Our Food Vision Mini App Files from Google Colab.mp4

41. Downloading Our Food Vision Mini App Files from Google Colab.srt

42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.mp4

42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.srt

43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.mp4

43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.srt

44. Food Vision Big Project Outline.mp4

44. Food Vision Big Project Outline.srt

45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.mp4

45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.srt

46. Downloading the Food 101 Dataset.mp4

46. Downloading the Food 101 Dataset.srt

47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.mp4

47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.srt

48. Turning Our Food 101 Datasets into DataLoaders.mp4

48. Turning Our Food 101 Datasets into DataLoaders.srt

49. Training Food Vision Big Our Biggest Model Yet!.mp4

49. Training Food Vision Big Our Biggest Model Yet!.srt

5. Some Tools and Places to Deploy Machine Learning Models.mp4

5. Some Tools and Places to Deploy Machine Learning Models.srt

50. Outlining the File Structure for Our Food Vision Big.mp4

50. Outlining the File Structure for Our Food Vision Big.srt

51. Downloading an Example Image and Moving Our Food Vision Big Model File.mp4

51. Downloading an Example Image and Moving Our Food Vision Big Model File.srt

52. Saving Food 101 Class Names to a Text File and Reading them Back In.mp4

52. Saving Food 101 Class Names to a Text File and Reading them Back In.srt

53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.mp4

53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.srt

54. Creating an App Script for Our Food Vision Big Model Gradio Demo.mp4

54. Creating an App Script for Our Food Vision Big Model Gradio Demo.srt

55. Zipping and Downloading Our Food Vision Big App Files.mp4

55. Zipping and Downloading Our Food Vision Big App Files.srt

56. Deploying Food Vision Big to Hugging Face Spaces.mp4

56. Deploying Food Vision Big to Hugging Face Spaces.srt

57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.mp4

57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.srt

6. What We Are Going to Cover.mp4

6. What We Are Going to Cover.srt

7. Getting Setup to Code.mp4

7. Getting Setup to Code.srt

8. Downloading a Dataset for Food Vision Mini.mp4

8. Downloading a Dataset for Food Vision Mini.srt

9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.mp4

9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.srt

/12. Introduction to PyTorch 2.0 and torch.compile/

1. Introduction to PyTorch 2.0.mp4

1. Introduction to PyTorch 2.0.srt

10. Discussing How to Get Better Relative Speedups for Training Models.mp4

10. Discussing How to Get Better Relative Speedups for Training Models.srt

11. Setting the Batch Size and Data Size Programmatically.mp4

11. Setting the Batch Size and Data Size Programmatically.srt

12. Getting More Speedups with TensorFloat-32.mp4

12. Getting More Speedups with TensorFloat-32.srt

13. Downloading the CIFAR10 Dataset.mp4

13. Downloading the CIFAR10 Dataset.srt

14. Creating Training and Test DataLoaders.mp4

14. Creating Training and Test DataLoaders.srt

15. Preparing Training and Testing Loops with Timing Steps.mp4

15. Preparing Training and Testing Loops with Timing Steps.srt

16. Experiment 1 - Single Run without Torch Compile.mp4

16. Experiment 1 - Single Run without Torch Compile.srt

17. Experiment 2 - Single Run with Torch Compile.mp4

17. Experiment 2 - Single Run with Torch Compile.srt

18. Comparing the Results of Experiments 1 and 2.mp4

18. Comparing the Results of Experiments 1 and 2.srt

19. Saving the Results of Experiments 1 and 2.mp4

19. Saving the Results of Experiments 1 and 2.srt

2. What We Are Going to Cover and PyTorch 2 Reference Materials.mp4

2. What We Are Going to Cover and PyTorch 2 Reference Materials.srt

20. Preparing Functions for Experiments 3 and 4.mp4

20. Preparing Functions for Experiments 3 and 4.srt

21. Experiment 3 - Training a Non-Compiled Model for Multiple Runs.mp4

21. Experiment 3 - Training a Non-Compiled Model for Multiple Runs.srt

22. Experiment 4 - Training a Compiled Model for Multiple Runs.mp4

22. Experiment 4 - Training a Compiled Model for Multiple Runs.srt

23. Comparing the Results of Experiments 3 and 4.mp4

23. Comparing the Results of Experiments 3 and 4.srt

24. Potential Extensions and Resources to Learn More.mp4

24. Potential Extensions and Resources to Learn More.srt

3. Getting Started with PyTorch 2.0 in Google Colab.mp4

3. Getting Started with PyTorch 2.0 in Google Colab.srt

4. PyTorch 2.0 - 30 Second Intro.mp4

4. PyTorch 2.0 - 30 Second Intro.srt

5. Getting Setup for PyTorch 2.0.mp4

5. Getting Setup for PyTorch 2.0.srt

6. Getting Info from Our GPUs and Seeing if They're Capable of Using PyTorch 2.0.mp4

6. Getting Info from Our GPUs and Seeing if They're Capable of Using PyTorch 2.0.srt

7. Setting the Default Device in PyTorch 2.0.mp4

7. Setting the Default Device in PyTorch 2.0.srt

8. Discussing the Experiments We Are Going to Run for PyTorch 2.0.mp4

8. Discussing the Experiments We Are Going to Run for PyTorch 2.0.srt

9. Creating a Function to Setup Our Model and Transforms.mp4

9. Creating a Function to Setup Our Model and Transforms.srt

/13. Where To Go From Here/

1. Thank You!.mp4

1. Thank You!.srt

2. Review This Course!.html

3. Become An Alumni.html

4. Learning Guideline.html

5. ZTM Events Every Month.html

6. LinkedIn Endorsements.html

/2. Section 00 PyTorch Fundamentals/

1. Why Use Machine Learning or Deep Learning.mp4

1. Why Use Machine Learning or Deep Learning.srt

10. How To and How Not To Approach This Course.mp4

10. How To and How Not To Approach This Course.srt

11. Important Resources For This Course.mp4

11. Important Resources For This Course.srt

12. Getting Setup to Write PyTorch Code.mp4

12. Getting Setup to Write PyTorch Code.srt

13. Introduction to PyTorch Tensors.mp4

13. Introduction to PyTorch Tensors.srt

14. Creating Random Tensors in PyTorch.mp4

14. Creating Random Tensors in PyTorch.srt

15. Creating Tensors With Zeros and Ones in PyTorch.mp4

15. Creating Tensors With Zeros and Ones in PyTorch.srt

16. Creating a Tensor Range and Tensors Like Other Tensors.mp4

16. Creating a Tensor Range and Tensors Like Other Tensors.srt

17. Dealing With Tensor Data Types.mp4

17. Dealing With Tensor Data Types.srt

18. Getting Tensor Attributes.mp4

18. Getting Tensor Attributes.srt

19. Manipulating Tensors (Tensor Operations).mp4

19. Manipulating Tensors (Tensor Operations).srt

2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.mp4

2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.srt

20. Matrix Multiplication (Part 1).mp4

20. Matrix Multiplication (Part 1).srt

21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.mp4

21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.srt

22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.mp4

22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.srt

23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).mp4

23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).srt

24. Finding The Positional Min and Max of Tensors.mp4

24. Finding The Positional Min and Max of Tensors.srt

25. Reshaping, Viewing and Stacking Tensors.mp4

25. Reshaping, Viewing and Stacking Tensors.srt

26. Squeezing, Unsqueezing and Permuting Tensors.mp4

26. Squeezing, Unsqueezing and Permuting Tensors.srt

27. Selecting Data From Tensors (Indexing).mp4

27. Selecting Data From Tensors (Indexing).srt

28. PyTorch Tensors and NumPy.en.copy.srt

28. PyTorch Tensors and NumPy.mp4

28. PyTorch Tensors and NumPy.srt

29. PyTorch Reproducibility (Taking the Random Out of Random).mp4

29. PyTorch Reproducibility (Taking the Random Out of Random).srt

3. Machine Learning vs. Deep Learning.mp4

3. Machine Learning vs. Deep Learning.srt

30. Different Ways of Accessing a GPU in PyTorch.mp4

30. Different Ways of Accessing a GPU in PyTorch.srt

31. Setting up Device Agnostic Code and Putting Tensors On and Off the GPU.mp4

31. Setting up Device Agnostic Code and Putting Tensors On and Off the GPU.srt

32. PyTorch Fundamentals Exercises and Extra-Curriculum.mp4

32. PyTorch Fundamentals Exercises and Extra-Curriculum.srt

33. Let's Have Some Fun (+ Free Resources).html

4. Anatomy of Neural Networks.mp4

4. Anatomy of Neural Networks.srt

5. Different Types of Learning Paradigms.mp4

5. Different Types of Learning Paradigms.srt

6. What Can Deep Learning Be Used For.mp4

6. What Can Deep Learning Be Used For.srt

7. What Is and Why PyTorch.mp4

7. What Is and Why PyTorch.srt

8. What Are Tensors.mp4

8. What Are Tensors.srt

9. What We Are Going To Cover With PyTorch.mp4

9. What We Are Going To Cover With PyTorch.srt

/3. Section 01 PyTorch Workflow/

1. Introduction and Where You Can Get Help.mp4

1. Introduction and Where You Can Get Help.srt

10. Making Predictions With Our Random Model Using Inference Mode.mp4

10. Making Predictions With Our Random Model Using Inference Mode.srt

11. Training a Model Intuition (The Things We Need).mp4

11. Training a Model Intuition (The Things We Need).srt

12. Setting Up an Optimizer and a Loss Function.mp4

12. Setting Up an Optimizer and a Loss Function.srt

13. PyTorch Training Loop Steps and Intuition.mp4

13. PyTorch Training Loop Steps and Intuition.srt

14. Writing Code for a PyTorch Training Loop.mp4

14. Writing Code for a PyTorch Training Loop.srt

15. Reviewing the Steps in a Training Loop Step by Step.mp4

15. Reviewing the Steps in a Training Loop Step by Step.srt

16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.mp4

16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.srt

17. Writing Testing Loop Code and Discussing What's Happening Step by Step.mp4

17. Writing Testing Loop Code and Discussing What's Happening Step by Step.srt

18. Reviewing What Happens in a Testing Loop Step by Step.mp4

18. Reviewing What Happens in a Testing Loop Step by Step.srt

19. Writing Code to Save a PyTorch Model.mp4

19. Writing Code to Save a PyTorch Model.srt

2. Getting Setup and What We Are Covering.mp4

2. Getting Setup and What We Are Covering.srt

20. Writing Code to Load a PyTorch Model.mp4

20. Writing Code to Load a PyTorch Model.srt

21. Setting Up to Practice Everything We Have Done Using Device-Agnostic Code.mp4

21. Setting Up to Practice Everything We Have Done Using Device-Agnostic Code.srt

22. Putting Everything Together (Part 1) Data.mp4

22. Putting Everything Together (Part 1) Data.srt

23. Putting Everything Together (Part 2) Building a Model.mp4

23. Putting Everything Together (Part 2) Building a Model.srt

24. Putting Everything Together (Part 3) Training a Model.mp4

24. Putting Everything Together (Part 3) Training a Model.srt

25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.mp4

25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.srt

26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.mp4

26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.srt

27. PyTorch Workflow Exercises and Extra-Curriculum.mp4

27. PyTorch Workflow Exercises and Extra-Curriculum.srt

28. Unlimited Updates.html

3. Creating a Simple Dataset Using the Linear Regression Formula.mp4

3. Creating a Simple Dataset Using the Linear Regression Formula.srt

4. Splitting Our Data Into Training and Test Sets.mp4

4. Splitting Our Data Into Training and Test Sets.srt

5. Building a function to Visualize Our Data.mp4

5. Building a function to Visualize Our Data.srt

6. Creating Our First PyTorch Model for Linear Regression.mp4

6. Creating Our First PyTorch Model for Linear Regression.srt

7. Breaking Down What's Happening in Our PyTorch Linear regression Model.mp4

7. Breaking Down What's Happening in Our PyTorch Linear regression Model.srt

8. Discussing Some of the Most Important PyTorch Model Building Classes.mp4

8. Discussing Some of the Most Important PyTorch Model Building Classes.srt

9. Checking Out the Internals of Our PyTorch Model.mp4

9. Checking Out the Internals of Our PyTorch Model.srt

/4. Section 02 PyTorch Neural Network Classification/

1. Introduction to Machine Learning Classification With PyTorch.mp4

1. Introduction to Machine Learning Classification With PyTorch.srt

10. Setting Up a Loss Function Optimizer and Evaluation Function for Our Classification Network.mp4

10. Setting Up a Loss Function Optimizer and Evaluation Function for Our Classification Network.srt

11. Going from Model Logits to Prediction Probabilities to Prediction Labels.mp4

11. Going from Model Logits to Prediction Probabilities to Prediction Labels.srt

12. Coding a Training and Testing Optimization Loop for Our Classification Model.mp4

12. Coding a Training and Testing Optimization Loop for Our Classification Model.srt

13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.mp4

13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.srt

14. Discussing Options to Improve a Model.mp4

14. Discussing Options to Improve a Model.srt

15. Creating a New Model with More Layers and Hidden Units.mp4

15. Creating a New Model with More Layers and Hidden Units.srt

16. Writing Training and Testing Code to See if Our New and Upgraded Model Performs Better.mp4

16. Writing Training and Testing Code to See if Our New and Upgraded Model Performs Better.srt

17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.mp4

17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.srt

18. Building and Training a Model to Fit on Straight Line Data.mp4

18. Building and Training a Model to Fit on Straight Line Data.srt

19. Evaluating Our Models Predictions on Straight Line Data.mp4

19. Evaluating Our Models Predictions on Straight Line Data.srt

2. Classification Problem Example Input and Output Shapes.mp4

2. Classification Problem Example Input and Output Shapes.srt

20. Introducing the Missing Piece for Our Classification Model Non-Linearity.mp4

20. Introducing the Missing Piece for Our Classification Model Non-Linearity.srt

21. Building Our First Neural Network with Non-Linearity.mp4

21. Building Our First Neural Network with Non-Linearity.srt

22. Writing Training and Testing Code for Our First Non-Linear Model.mp4

22. Writing Training and Testing Code for Our First Non-Linear Model.srt

23. Making Predictions with and Evaluating Our First Non-Linear Model.mp4

23. Making Predictions with and Evaluating Our First Non-Linear Model.srt

24. Replicating Non-Linear Activation Functions with Pure PyTorch.mp4

24. Replicating Non-Linear Activation Functions with Pure PyTorch.srt

25. Putting It All Together (Part 1) Building a Multiclass Dataset.mp4

25. Putting It All Together (Part 1) Building a Multiclass Dataset.srt

26. Creating a Multi-Class Classification Model with PyTorch.mp4

26. Creating a Multi-Class Classification Model with PyTorch.srt

27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.mp4

27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.srt

28. Going from Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.mp4

28. Going from Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.srt

29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.mp4

29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.srt

3. Typical Architecture of a Classification Neural Network (Overview).mp4

3. Typical Architecture of a Classification Neural Network (Overview).srt

30. Making Predictions with and Evaluating Our Multi-Class Classification Model.mp4

30. Making Predictions with and Evaluating Our Multi-Class Classification Model.srt

31. Discussing a Few More Classification Metrics.mp4

31. Discussing a Few More Classification Metrics.srt

32. PyTorch Classification Exercises and Extra-Curriculum.mp4

32. PyTorch Classification Exercises and Extra-Curriculum.srt

33. Course Check-In.html

4. Making a Toy Classification Dataset.mp4

4. Making a Toy Classification Dataset.srt

5. Turning Our Data into Tensors and Making a Training and Test Split.mp4

5. Turning Our Data into Tensors and Making a Training and Test Split.srt

6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.mp4

6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.srt

7. Coding a Small Neural Network to Handle Our Classification Data.mp4

7. Coding a Small Neural Network to Handle Our Classification Data.srt

8. Making Our Neural Network Visual.mp4

8. Making Our Neural Network Visual.srt

9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.mp4

9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.srt

/5. Section 03 PyTorch Computer Vision/

1. What Is a Computer Vision Problem and What We Are Going to Cover.mp4

1. What Is a Computer Vision Problem and What We Are Going to Cover.srt

10. Creating a Loss Function an Optimizer for Model 0.mp4

10. Creating a Loss Function an Optimizer for Model 0.srt

11. Creating a Function to Time Our Modelling Code.mp4

11. Creating a Function to Time Our Modelling Code.srt

12. Writing Training and Testing Loops for Our Batched Data.mp4

12. Writing Training and Testing Loops for Our Batched Data.srt

13. Writing an Evaluation Function to Get Our Models Results.mp4

13. Writing an Evaluation Function to Get Our Models Results.srt

14. Setup Device-Agnostic Code for Running Experiments on the GPU.mp4

14. Setup Device-Agnostic Code for Running Experiments on the GPU.srt

15. Model 1 Creating a Model with Non-Linear Functions.mp4

15. Model 1 Creating a Model with Non-Linear Functions.srt

16. Model 1 Creating a Loss Function and Optimizer.mp4

16. Model 1 Creating a Loss Function and Optimizer.srt

17. Turing Our Training Loop into a Function.mp4

17. Turing Our Training Loop into a Function.srt

18. Turing Our Testing Loop into a Function.mp4

18. Turing Our Testing Loop into a Function.srt

19. Training and Testing Model 1 with Our Training and Testing Functions.mp4

19. Training and Testing Model 1 with Our Training and Testing Functions.srt

2. Computer Vision Input and Output Shapes.mp4

2. Computer Vision Input and Output Shapes.srt

20. Getting a Results Dictionary for Model 1.mp4

20. Getting a Results Dictionary for Model 1.srt

21. Model 2 Convolutional Neural Networks High Level Overview.mp4

21. Model 2 Convolutional Neural Networks High Level Overview.srt

22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.mp4

22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.srt

23. Model 2 Breaking Down Conv2D Step by Step.mp4

23. Model 2 Breaking Down Conv2D Step by Step.srt

24. Model 2 Breaking Down MaxPool2D Step by Step.mp4

24. Model 2 Breaking Down MaxPool2D Step by Step.srt

25. Model 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.mp4

25. Model 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.srt

26. Model 2 Setting Up a Loss Function and Optimizer.mp4

26. Model 2 Setting Up a Loss Function and Optimizer.srt

27. Model 2 Training Our First CNN and Evaluating Its Results.mp4

27. Model 2 Training Our First CNN and Evaluating Its Results.srt

28. Comparing the Results of Our Modelling Experiments.mp4

28. Comparing the Results of Our Modelling Experiments.srt

29. Making Predictions on Random Test Samples with the Best Trained Model.mp4

29. Making Predictions on Random Test Samples with the Best Trained Model.srt

3. What Is a Convolutional Neural Network (CNN).mp4

3. What Is a Convolutional Neural Network (CNN).srt

30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.mp4

30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.srt

31. Making Predictions Across the Whole Test Dataset and Importing Libraries to Plot a Confusion Matrix.mp4

31. Making Predictions Across the Whole Test Dataset and Importing Libraries to Plot a Confusion Matrix.srt

32. Evaluating Our Best Models Predictions with a Confusion Matrix.mp4

32. Evaluating Our Best Models Predictions with a Confusion Matrix.srt

33. Saving and Loading Our Best Performing Model.mp4

33. Saving and Loading Our Best Performing Model.srt

34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.mp4

34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.srt

35. Implement a New Life System.html

4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.mp4

4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.srt

5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.mp4

5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.srt

6. Visualizing Random Samples of Data.mp4

6. Visualizing Random Samples of Data.srt

7. DataLoader Overview Understanding Mini-Batch.mp4

7. DataLoader Overview Understanding Mini-Batch.srt

8. Turning Our Datasets Into DataLoaders.mp4

8. Turning Our Datasets Into DataLoaders.srt

9. Model 0 Creating a Baseline Model with Two Linear Layers.mp4

9. Model 0 Creating a Baseline Model with Two Linear Layers.srt

/6. Section 04 PyTorch Custom Datasets/

1. What Is a Custom Dataset and What We Are Going to Cover.mp4

1. What Is a Custom Dataset and What We Are Going to Cover.srt

10. Visualizing a Loaded Image From the Train Dataset.mp4

10. Visualizing a Loaded Image From the Train Dataset.srt

11. Turning Our Image Datasets into PyTorch DataLoaders.mp4

11. Turning Our Image Datasets into PyTorch DataLoaders.srt

12. Creating a Custom Dataset Class in PyTorch High Level Overview.mp4

12. Creating a Custom Dataset Class in PyTorch High Level Overview.srt

13. Creating a Helper Function to Get Class Names From a Directory.mp4

13. Creating a Helper Function to Get Class Names From a Directory.srt

14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.mp4

14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.srt

15. Compare Our Custom Dataset Class to the Original ImageFolder Class.mp4

15. Compare Our Custom Dataset Class to the Original ImageFolder Class.srt

16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.mp4

16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.srt

17. Turning Our Custom Datasets Into DataLoaders.mp4

17. Turning Our Custom Datasets Into DataLoaders.srt

18. Exploring State of the Art Data Augmentation With Torchvision Transforms.mp4

18. Exploring State of the Art Data Augmentation With Torchvision Transforms.srt

19. Building a Baseline Model (Part 1) Loading and Transforming Data.mp4

19. Building a Baseline Model (Part 1) Loading and Transforming Data.srt

2. Importing PyTorch and Setting Up Device-Agnostic Code.mp4

2. Importing PyTorch and Setting Up Device-Agnostic Code.srt

20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.mp4

20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.srt

21. Building a Baseline Model (Part 3) Doing a Forward Pass to Test Our Model Shapes.mp4

21. Building a Baseline Model (Part 3) Doing a Forward Pass to Test Our Model Shapes.srt

22. Using the Torchinfo Package to Get a Summary of Our Model.mp4

22. Using the Torchinfo Package to Get a Summary of Our Model.srt

23. Creating Training and Testing loop Functions.mp4

23. Creating Training and Testing loop Functions.srt

24. Creating a Train Function to Train and Evaluate Our Models.mp4

24. Creating a Train Function to Train and Evaluate Our Models.srt

25. Training and Evaluating Model 0 With Our Training Functions.mp4

25. Training and Evaluating Model 0 With Our Training Functions.srt

26. Plotting the Loss Curves of Model 0.mp4

27. Discussing the Balance Between Overfitting and Underfitting and How to Deal With Each.mp4

27. Discussing the Balance Between Overfitting and Underfitting and How to Deal With Each.srt

28. Creating Augmented Training Datasets and DataLoaders for Model 1.mp4

28. Creating Augmented Training Datasets and DataLoaders for Model 1.srt

29. Constructing and Training Model 1.mp4

29. Constructing and Training Model 1.srt

3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.mp4

3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.srt

30. Plotting the Loss Curves of Model 1.mp4

30. Plotting the Loss Curves of Model 1.srt

31. Plotting the Loss Curves of All of Our Models Against Each Other.mp4

31. Plotting the Loss Curves of All of Our Models Against Each Other.srt

32. Predicting on Custom Data (Part 1) Downloading an Image.mp4

32. Predicting on Custom Data (Part 1) Downloading an Image.srt

33. Predicting on Custom Data (Part2) Loading In a Custom Image With PyTorch.mp4

33. Predicting on Custom Data (Part2) Loading In a Custom Image With PyTorch.srt

34. Predicting on Custom Data (Part 3) Getting Our Custom Image Into the Right Format.mp4

34. Predicting on Custom Data (Part 3) Getting Our Custom Image Into the Right Format.srt

35. Predicting on Custom Data (Part 4) Turning Our Models Raw Outputs Into Prediction Labels.mp4

35. Predicting on Custom Data (Part 4) Turning Our Models Raw Outputs Into Prediction Labels.srt

36. Predicting on Custom Data (Part 5) Putting It All Together.mp4

36. Predicting on Custom Data (Part 5) Putting It All Together.srt

37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.mp4

37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.srt

38. Exercise Imposter Syndrome.mp4

38. Exercise Imposter Syndrome.srt

4. Becoming One With the Data (Part 1) Exploring the Data Format.mp4

4. Becoming One With the Data (Part 1) Exploring the Data Format.srt

5. Becoming One With the Data (Part 2) Visualizing a Random Image.mp4

5. Becoming One With the Data (Part 2) Visualizing a Random Image.srt

6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.mp4

6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.srt

7. Transforming Data (Part 1) Turning Images Into Tensors.mp4

7. Transforming Data (Part 1) Turning Images Into Tensors.srt

8. Transforming Data (Part 2) Visualizing Transformed Images.mp4

8. Transforming Data (Part 2) Visualizing Transformed Images.srt

9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.mp4

9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.srt

/7. Section 05 PyTorch Going Modular/

1. What Is Going Modular and What We Are Going to Cover.mp4

1. What Is Going Modular and What We Are Going to Cover.srt

10. Going Modular Summary, Exercises and Extra-Curriculum.mp4

10. Going Modular Summary, Exercises and Extra-Curriculum.srt

2. Going Modular Notebook (Part 1) Running It End to End.mp4

2. Going Modular Notebook (Part 1) Running It End to End.srt

3. Downloading a Dataset.mp4

3. Downloading a Dataset.srt

4. Writing the Outline for Our First Python Script to Setup the Data.mp4

4. Writing the Outline for Our First Python Script to Setup the Data.srt

5. Creating a Python Script to Create Our PyTorch DataLoaders.mp4

5. Creating a Python Script to Create Our PyTorch DataLoaders.srt

6. Turning Our Model Building Code into a Python Script.mp4

6. Turning Our Model Building Code into a Python Script.srt

7. Turning Our Model Training Code into a Python Script.mp4

7. Turning Our Model Training Code into a Python Script.srt

8. Turning Our Utility Function to Save a Model into a Python Script.mp4

8. Turning Our Utility Function to Save a Model into a Python Script.srt

9. Creating a Training Script to Train Our Model in One Line of Code.mp4

9. Creating a Training Script to Train Our Model in One Line of Code.srt

/8. Section 06 PyTorch Transfer Learning/

1. Introduction What is Transfer Learning and Why Use It.mp4

1. Introduction What is Transfer Learning and Why Use It.srt

10. Different Kinds of Transfer Learning.mp4

10. Different Kinds of Transfer Learning.srt

11. Getting a Summary of the Different Layers of Our Model.mp4

11. Getting a Summary of the Different Layers of Our Model.srt

12. Freezing the Base Layers of Our Model and Updating the Classifier Head.mp4

12. Freezing the Base Layers of Our Model and Updating the Classifier Head.srt

13. Training Our First Transfer Learning Feature Extractor Model.mp4

13. Training Our First Transfer Learning Feature Extractor Model.srt

14. Plotting the Loss Curves of Our Transfer Learning Model.mp4

14. Plotting the Loss Curves of Our Transfer Learning Model.srt

15. Outlining the Steps to Make Predictions on the Test Images.mp4

15. Outlining the Steps to Make Predictions on the Test Images.srt

16. Creating a Function Predict On and Plot Images.mp4

16. Creating a Function Predict On and Plot Images.srt

17. Making and Plotting Predictions on Test Images.mp4

17. Making and Plotting Predictions on Test Images.srt

18. Making a Prediction on a Custom Image.mp4

18. Making a Prediction on a Custom Image.srt

19. Main Takeaways, Exercises and Extra Curriculum.mp4

19. Main Takeaways, Exercises and Extra Curriculum.srt

2. Where Can You Find Pretrained Models and What We Are Going to Cover.mp4

2. Where Can You Find Pretrained Models and What We Are Going to Cover.srt

3. Installing the Latest Versions of Torch and Torchvision.mp4

3. Installing the Latest Versions of Torch and Torchvision.srt

4. Downloading Our Previously Written Code from Going Modular.mp4

4. Downloading Our Previously Written Code from Going Modular.srt

5. Downloading Pizza, Steak, Sushi Image Data from Github.mp4

5. Downloading Pizza, Steak, Sushi Image Data from Github.srt

6. Turning Our Data into DataLoaders with Manually Created Transforms.mp4

6. Turning Our Data into DataLoaders with Manually Created Transforms.srt

7. Turning Our Data into DataLoaders with Automatic Created Transforms.mp4

7. Turning Our Data into DataLoaders with Automatic Created Transforms.srt

8. Which Pretrained Model Should You Use.mp4

8. Which Pretrained Model Should You Use.srt

9. Setting Up a Pretrained Model with Torchvision.mp4

9. Setting Up a Pretrained Model with Torchvision.srt

/9. Section 07 PyTorch Experiment Tracking/

1. What Is Experiment Tracking and Why Track Experiments.mp4

1. What Is Experiment Tracking and Why Track Experiments.srt

10. Creating a Function to Create SummaryWriter Instances.mp4

10. Creating a Function to Create SummaryWriter Instances.srt

11. Adapting Our Train Function to Be Able to Track Multiple Experiments.mp4

11. Adapting Our Train Function to Be Able to Track Multiple Experiments.srt

12. What Experiments Should You Try.mp4

12. What Experiments Should You Try.srt

13. Discussing the Experiments We Are Going to Try.mp4

13. Discussing the Experiments We Are Going to Try.srt

14. Downloading Datasets for Our Modelling Experiments.mp4

14. Downloading Datasets for Our Modelling Experiments.srt

15. Turning Our Datasets into DataLoaders Ready for Experimentation.mp4

15. Turning Our Datasets into DataLoaders Ready for Experimentation.srt

16. Creating Functions to Prepare Our Feature Extractor Models.mp4

16. Creating Functions to Prepare Our Feature Extractor Models.srt

17. Coding Out the Steps to Run a Series of Modelling Experiments.mp4

17. Coding Out the Steps to Run a Series of Modelling Experiments.srt

18. Running Eight Different Modelling Experiments in 5 Minutes.mp4

18. Running Eight Different Modelling Experiments in 5 Minutes.srt

19. Viewing Our Modelling Experiments in TensorBoard.mp4

19. Viewing Our Modelling Experiments in TensorBoard.srt

2. Getting Setup by Importing Torch Libraries and Going Modular Code.mp4

2. Getting Setup by Importing Torch Libraries and Going Modular Code.srt

20. Loading In the Best Model and Making Predictions on Random Images from the Test Set.mp4

20. Loading In the Best Model and Making Predictions on Random Images from the Test Set.srt

21. Making a Prediction on Our Own Custom Image with the Best Model.mp4

21. Making a Prediction on Our Own Custom Image with the Best Model.srt

22. Main Takeaways, Exercises and Extra Curriculum.mp4

22. Main Takeaways, Exercises and Extra Curriculum.srt

3. Creating a Function to Download Data.mp4

3. Creating a Function to Download Data.srt

4. Turning Our Data into DataLoaders Using Manual Transforms.mp4

4. Turning Our Data into DataLoaders Using Manual Transforms.srt

5. Turning Our Data into DataLoaders Using Automatic Transforms.mp4