PyTorch for Deep Learning in 2023: Zero to Mastery
- Type:
- Other > Other
- Files:
- 337
- Size:
- 27.81 GiB (29865756772 Bytes)
- Uploaded:
- 2023-02-10 08:09:23 GMT
- By:
-
cybil18
- Seeders:
- 4
- Leechers:
- 0
- Comments
- 0
- Info Hash: B0C637B060695FFDAC314EDC544B35FB6747C18E
(Problems with magnets links are fixed by upgrading your torrent client!)
PyTorch for Deep Learning in 2023: Zero to Mastery Learn PyTorch. Become a Deep Learning Engineer. Get Hired. Udemy Link - https://www.udemy.com/course/pytorch-for-deep-learning/ Please seed as much as you can!
| 1. Introduction/1. PyTorch for Deep Learning.mp4 | 75.35 MiB |
| 1. Introduction/2. Course Welcome and What Is Deep Learning.mp4 | 38.99 MiB |
| 1. Introduction/3. Join Our Online Classroom!.mp4 | 75.35 MiB |
| 1. Introduction/4. Exercise Meet Your Classmates + Instructor.html | 3.79 KiB |
| 1. Introduction/5. Course Companion Book + Code + More.html | 1.1 KiB |
| 1. Introduction/6. Machine Learning + Python Monthly Newsletters.html | 87 B |
| 10. PyTorch Paper Replicating/1. What Is a Machine Learning Research Paper.mp4 | 93.94 MiB |
| 10. PyTorch Paper Replicating/10. Breaking Down Figure 1 of the ViT Paper.mp4 | 87.12 MiB |
| 10. PyTorch Paper Replicating/11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.mp4 | 140.93 MiB |
| 10. PyTorch Paper Replicating/12. Breaking Down Equation 1.mp4 | 103.22 MiB |
| 10. PyTorch Paper Replicating/13. Breaking Down Equation 2 and 3.mp4 | 125.04 MiB |
| 10. PyTorch Paper Replicating/14. Breaking Down Equation 4.mp4 | 92.44 MiB |
| 10. PyTorch Paper Replicating/15. Breaking Down Table 1.mp4 | 122.08 MiB |
| 10. PyTorch Paper Replicating/16. Calculating the Input and Output Shape of the Embedding Layer by Hand.mp4 | 160.6 MiB |
| 10. PyTorch Paper Replicating/17. Turning a Single Image into Patches (Part 1 Patching the Top Row).mp4 | 150.16 MiB |
| 10. PyTorch Paper Replicating/18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).mp4 | 130.64 MiB |
| 10. PyTorch Paper Replicating/19. Creating Patch Embeddings with a Convolutional Layer.mp4 | 142.63 MiB |
| 10. PyTorch Paper Replicating/2. Why Replicate a Machine Learning Research Paper.mp4 | 23.26 MiB |
| 10. PyTorch Paper Replicating/20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.mp4 | 129.06 MiB |
| 10. PyTorch Paper Replicating/21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.mp4 | 89.61 MiB |
| 10. PyTorch Paper Replicating/22. Visualizing a Single Sequence Vector of Patch Embeddings.mp4 | 50.37 MiB |
| 10. PyTorch Paper Replicating/23. Creating the Patch Embedding Layer with PyTorch.mp4 | 170.03 MiB |
| 10. PyTorch Paper Replicating/24. Creating the Class Token Embedding.mp4 | 131.99 MiB |
| 10. PyTorch Paper Replicating/25. Creating the Class Token Embedding - Less Birds.mp4 | 131.91 MiB |
| 10. PyTorch Paper Replicating/26. Creating the Position Embedding.mp4 | 109.18 MiB |
| 10. PyTorch Paper Replicating/27. Equation 1 Putting it All Together.mp4 | 134.82 MiB |
| 10. PyTorch Paper Replicating/28. Equation 2 Multihead Attention Overview.mp4 | 144.11 MiB |
| 10. PyTorch Paper Replicating/29. Equation 2 Layernorm Overview.mp4 | 111.76 MiB |
| 10. PyTorch Paper Replicating/3. Where Can You Find Machine Learning Research Papers and Code.mp4 | 110.75 MiB |
| 10. PyTorch Paper Replicating/30. Turning Equation 2 into Code.mp4 | 163.87 MiB |
| 10. PyTorch Paper Replicating/31. Checking the Inputs and Outputs of Equation.mp4 | 53.69 MiB |
| 10. PyTorch Paper Replicating/32. Equation 3 Replication Overview.mp4 | 88.7 MiB |
| 10. PyTorch Paper Replicating/33. Turning Equation 3 into Code.mp4 | 107.07 MiB |
| 10. PyTorch Paper Replicating/34. Transformer Encoder Overview.mp4 | 82.85 MiB |
| 10. PyTorch Paper Replicating/35. Combining equation 2 and 3 to Create the Transformer Encoder.mp4 | 84.87 MiB |
| 10. PyTorch Paper Replicating/36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.mp4 | 188.75 MiB |
| 10. PyTorch Paper Replicating/37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces.mp4 | 190.82 MiB |
| 10. PyTorch Paper Replicating/38. Bringing Our Own Vision Transformer to Life - Part 2 The Forward Method.mp4 | 111.37 MiB |
| 10. PyTorch Paper Replicating/39. Getting a Visual Summary of Our Custom Vision Transformer.mp4 | 84.89 MiB |
| 10. PyTorch Paper Replicating/4. What We Are Going to Cover.mp4 | 87.76 MiB |
| 10. PyTorch Paper Replicating/40. Creating a Loss Function and Optimizer from the ViT Paper.mp4 | 118.33 MiB |
| 10. PyTorch Paper Replicating/41. Training our Custom ViT on Food Vision Mini.mp4 | 53.48 MiB |
| 10. PyTorch Paper Replicating/42. Discussing what Our Training Setup Is Missing.mp4 | 101.2 MiB |
| 10. PyTorch Paper Replicating/43. Plotting a Loss Curve for Our ViT Model.mp4 | 63.4 MiB |
| 10. PyTorch Paper Replicating/44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.mp4 | 164.75 MiB |
| 10. PyTorch Paper Replicating/45. Preparing Data to Be Used with a Pretrained ViT.mp4 | 57.22 MiB |
| 10. PyTorch Paper Replicating/46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.mp4 | 76.29 MiB |
| 10. PyTorch Paper Replicating/47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.mp4 | 40.36 MiB |
| 10. PyTorch Paper Replicating/48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.mp4 | 41.81 MiB |
| 10. PyTorch Paper Replicating/49. Making Predictions on a Custom Image with Our Pretrained ViT.mp4 | 37.11 MiB |
| 10. PyTorch Paper Replicating/5. Getting Setup for Coding in Google Colab.mp4 | 99.14 MiB |
| 10. PyTorch Paper Replicating/50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.mp4 | 85.49 MiB |
| 10. PyTorch Paper Replicating/6. Downloading Data for Food Vision Mini.mp4 | 43.83 MiB |
| 10. PyTorch Paper Replicating/7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.mp4 | 89.7 MiB |
| 10. PyTorch Paper Replicating/8. Visualizing a Single Image.mp4 | 36.44 MiB |
| 10. PyTorch Paper Replicating/9. Replicating a Vision Transformer - High Level Overview.mp4 | 77.84 MiB |
| 11. PyTorch Model Deployment/1. What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.mp4 | 73.84 MiB |
| 11. PyTorch Model Deployment/10. Creating an EffNetB2 Feature Extractor Model.mp4 | 92.12 MiB |
| 11. PyTorch Model Deployment/11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.mp4 | 57.6 MiB |
| 11. PyTorch Model Deployment/12. Creating DataLoaders for EffNetB2.mp4 | 31.38 MiB |
| 11. PyTorch Model Deployment/13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.mp4 | 97.04 MiB |
| 11. PyTorch Model Deployment/14. Saving Our EffNetB2 Model to File.mp4 | 26.71 MiB |
| 11. PyTorch Model Deployment/15. Getting the Size of Our EffNetB2 Model in Megabytes.mp4 | 55.48 MiB |
| 11. PyTorch Model Deployment/16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.mp4 | 63.27 MiB |
| 11. PyTorch Model Deployment/17. Creating a Vision Transformer Feature Extractor Model.mp4 | 78.51 MiB |
| 11. PyTorch Model Deployment/18. Creating DataLoaders for Our ViT Feature Extractor Model.mp4 | 19.7 MiB |
| 11. PyTorch Model Deployment/19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.mp4 | 62 MiB |
| 11. PyTorch Model Deployment/2. Three Questions to Ask for Machine Learning Model Deployment.mp4 | 46.93 MiB |
| 11. PyTorch Model Deployment/20. Saving Our ViT Feature Extractor and Inspecting Its Size.mp4 | 43.77 MiB |
| 11. PyTorch Model Deployment/21. Collecting Stats About Our-ViT Feature Extractor.mp4 | 45.86 MiB |
| 11. PyTorch Model Deployment/22. Outlining the Steps for Making and Timing Predictions for Our Models.mp4 | 93.42 MiB |
| 11. PyTorch Model Deployment/23. Creating a Function to Make and Time Predictions with Our Models.mp4 | 185.78 MiB |
| 11. PyTorch Model Deployment/24. Making and Timing Predictions with EffNetB2.mp4 | 97.63 MiB |
| 11. PyTorch Model Deployment/25. Making and Timing Predictions with ViT.mp4 | 72.47 MiB |
| 11. PyTorch Model Deployment/26. Comparing EffNetB2 and ViT Model Statistics.mp4 | 89.62 MiB |
| 11. PyTorch Model Deployment/27. Visualizing the Performance vs Speed Trade-off.mp4 | 134.67 MiB |
| 11. PyTorch Model Deployment/28. Gradio Overview and Installation.mp4 | 95.13 MiB |
| 11. PyTorch Model Deployment/29. Gradio Function Outline.mp4 | 79.9 MiB |
| 11. PyTorch Model Deployment/3. Where Is My Model Going to Go.mp4 | 139.84 MiB |
| 11. PyTorch Model Deployment/30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.mp4 | 95.22 MiB |
| 11. PyTorch Model Deployment/31. Creating a List of Examples to Pass to Our Gradio Demo.mp4 | 53.31 MiB |
| 11. PyTorch Model Deployment/32. Bringing Food Vision Mini to Life in a Live Web Application.mp4 | 135.39 MiB |
| 11. PyTorch Model Deployment/33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.mp4 | 64.81 MiB |
| 11. PyTorch Model Deployment/34. Outlining the File Structure of Our Deployed App.mp4 | 89.54 MiB |
| 11. PyTorch Model Deployment/35. Creating a Food Vision Mini Demo Directory to House Our App Files.mp4 | 39.14 MiB |
| 11. PyTorch Model Deployment/36. Creating an Examples Directory with Example Food Vision Mini Images.mp4 | 92.41 MiB |
| 11. PyTorch Model Deployment/37. Writing Code to Move Our Saved EffNetB2 Model File.mp4 | 71.91 MiB |
| 11. PyTorch Model Deployment/38. Turning Our EffNetB2 Model Creation Function Into a Python Script.mp4 | 44.78 MiB |
| 11. PyTorch Model Deployment/39. Turning Our Food Vision Mini Demo App Into a Python Script.mp4 | 137.63 MiB |
| 11. PyTorch Model Deployment/4. How Is My Model Going to Function.mp4 | 67.36 MiB |
| 11. PyTorch Model Deployment/40. Creating a Requirements File for Our Food Vision Mini App.mp4 | 37.5 MiB |
| 11. PyTorch Model Deployment/41. Downloading Our Food Vision Mini App Files from Google Colab.mp4 | 112.22 MiB |
| 11. PyTorch Model Deployment/42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.mp4 | 143.59 MiB |
| 11. PyTorch Model Deployment/43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.mp4 | 91.61 MiB |
| 11. PyTorch Model Deployment/44. Food Vision Big Project Outline.mp4 | 39.15 MiB |
| 11. PyTorch Model Deployment/45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.mp4 | 96.53 MiB |
| 11. PyTorch Model Deployment/46. Downloading the Food 101 Dataset.mp4 | 71.67 MiB |
| 11. PyTorch Model Deployment/47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.mp4 | 119.74 MiB |
| 11. PyTorch Model Deployment/48. Turning Our Food 101 Datasets into DataLoaders.mp4 | 61.5 MiB |
| 11. PyTorch Model Deployment/49. Training Food Vision Big Our Biggest Model Yet!.mp4 | 184.22 MiB |
| 11. PyTorch Model Deployment/5. Some Tools and Places to Deploy Machine Learning Models.mp4 | 65.36 MiB |
| 11. PyTorch Model Deployment/50. Outlining the File Structure for Our Food Vision Big.mp4 | 52.78 MiB |
| 11. PyTorch Model Deployment/51. Downloading an Example Image and Moving Our Food Vision Big Model File.mp4 | 36.59 MiB |
| 11. PyTorch Model Deployment/52. Saving Food 101 Class Names to a Text File and Reading them Back In.mp4 | 66.81 MiB |
| 11. PyTorch Model Deployment/53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.mp4 | 23.9 MiB |
| 11. PyTorch Model Deployment/54. Creating an App Script for Our Food Vision Big Model Gradio Demo.mp4 | 104.81 MiB |
| 11. PyTorch Model Deployment/55. Zipping and Downloading Our Food Vision Big App Files.mp4 | 39.76 MiB |
| 11. PyTorch Model Deployment/56. Deploying Food Vision Big to Hugging Face Spaces.mp4 | 162.53 MiB |
| 11. PyTorch Model Deployment/57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.mp4 | 81.75 MiB |
| 11. PyTorch Model Deployment/6. What We Are Going to Cover.mp4 | 40.83 MiB |
| 11. PyTorch Model Deployment/7. Getting Setup to Code.mp4 | 62.88 MiB |
| 11. PyTorch Model Deployment/8. Downloading a Dataset for Food Vision Mini.mp4 | 39.25 MiB |
| 11. PyTorch Model Deployment/9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.mp4 | 58.56 MiB |
| 11. PyTorch Model Deployment/Download Paid Udemy Courses For Free.url | 116 B |
| 11. PyTorch Model Deployment/GetFreeCourses.Co.url | 116 B |
| 12. Where To Go From Here/1. Thank You!.mp4 | 20.99 MiB |
| 2. PyTorch Fundamentals/1. Why Use Machine Learning or Deep Learning.mp4 | 13.8 MiB |
| 2. PyTorch Fundamentals/10. How To and How Not To Approach This Course.mp4 | 37.74 MiB |
| 2. PyTorch Fundamentals/11. Important Resources For This Course.mp4 | 58.31 MiB |
| 2. PyTorch Fundamentals/12. Getting Setup to Write PyTorch Code.mp4 | 7 MiB |
| 2. PyTorch Fundamentals/13. Introduction to PyTorch Tensors.mp4 | 94 MiB |
| 2. PyTorch Fundamentals/14. Creating Random Tensors in PyTorch.mp4 | 86.42 MiB |
| 2. PyTorch Fundamentals/15. Creating Tensors With Zeros and Ones in PyTorch.mp4 | 24.56 MiB |
| 2. PyTorch Fundamentals/16. Creating a Tensor Range and Tensors Like Other Tensors.mp4 | 32.59 MiB |
| 2. PyTorch Fundamentals/17. Dealing With Tensor Data Types.mp4 | 81.4 MiB |
| 2. PyTorch Fundamentals/18. Getting Tensor Attributes.mp4 | 66.44 MiB |
| 2. PyTorch Fundamentals/19. Manipulating Tensors (Tensor Operations).mp4 | 39.7 MiB |
| 2. PyTorch Fundamentals/2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.mp4 | 35.34 MiB |
| 2. PyTorch Fundamentals/20. Matrix Multiplication (Part 1).mp4 | 77.8 MiB |
| 2. PyTorch Fundamentals/21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.mp4 | 57.78 MiB |
| 2. PyTorch Fundamentals/22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.mp4 | 97.35 MiB |
| 2. PyTorch Fundamentals/23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).mp4 | 48.14 MiB |
| 2. PyTorch Fundamentals/24. Finding The Positional Min and Max of Tensors.mp4 | 24.5 MiB |
| 2. PyTorch Fundamentals/25. Reshaping, Viewing and Stacking Tensors.mp4 | 103.95 MiB |
| 2. PyTorch Fundamentals/26. Squeezing, Unsqueezing and Permuting Tensors.mp4 | 88.41 MiB |
| 2. PyTorch Fundamentals/27. Selecting Data From Tensors (Indexing).mp4 | 56.96 MiB |
| 2. PyTorch Fundamentals/28. PyTorch Tensors and NumPy.mp4 | 59.78 MiB |
| 2. PyTorch Fundamentals/29. PyTorch Reproducibility (Taking the Random Out of Random).mp4 | 95.11 MiB |
| 2. PyTorch Fundamentals/3. Machine Learning vs. Deep Learning.mp4 | 55.3 MiB |
| 2. PyTorch Fundamentals/30. Different Ways of Accessing a GPU in PyTorch.mp4 | 113.01 MiB |
| 2. PyTorch Fundamentals/31. Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.mp4 | 64.51 MiB |
| 2. PyTorch Fundamentals/32. PyTorch Fundamentals Exercises and Extra-Curriculum.mp4 | 56.76 MiB |
| 2. PyTorch Fundamentals/33. Unlimited Updates.html | 1.68 KiB |
| 2. PyTorch Fundamentals/4. Anatomy of Neural Networks.mp4 | 70.32 MiB |
| 2. PyTorch Fundamentals/5. Different Types of Learning Paradigms.mp4 | 27.05 MiB |
| 2. PyTorch Fundamentals/6. What Can Deep Learning Be Used For.mp4 | 43.2 MiB |
| 2. PyTorch Fundamentals/7. What Is and Why PyTorch.mp4 | 113.56 MiB |
| 2. PyTorch Fundamentals/8. What Are Tensors.mp4 | 24.99 MiB |
| 2. PyTorch Fundamentals/9. What We Are Going To Cover With PyTorch.mp4 | 50.45 MiB |
| 2. PyTorch Fundamentals/Download Paid Udemy Courses For Free.url | 116 B |
| 2. PyTorch Fundamentals/GetFreeCourses.Co.url | 116 B |
| 3. PyTorch Workflow/1. Introduction and Where You Can Get Help.mp4 | 28.6 MiB |
| 3. PyTorch Workflow/10. Making Predictions With Our Random Model Using Inference Mode.mp4 | 107.03 MiB |
| 3. PyTorch Workflow/11. Training a Model Intuition (The Things We Need).mp4 | 69.5 MiB |
| 3. PyTorch Workflow/12. Setting Up an Optimizer and a Loss Function.mp4 | 116 MiB |
| 3. PyTorch Workflow/13. PyTorch Training Loop Steps and Intuition.mp4 | 128.78 MiB |
| 3. PyTorch Workflow/14. Writing Code for a PyTorch Training Loop.mp4 | 83 MiB |
| 3. PyTorch Workflow/15. Reviewing the Steps in a Training Loop Step by Step.mp4 | 177.46 MiB |
| 3. PyTorch Workflow/16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.mp4 | 101.7 MiB |
| 3. PyTorch Workflow/17. Writing Testing Loop Code and Discussing What's Happening Step by Step.mp4 | 135.03 MiB |
| 3. PyTorch Workflow/18. Reviewing What Happens in a Testing Loop Step by Step.mp4 | 161.56 MiB |
| 3. PyTorch Workflow/19. Writing Code to Save a PyTorch Model.mp4 | 129.82 MiB |
| 3. PyTorch Workflow/2. Getting Setup and What We Are Covering.mp4 | 69.67 MiB |
| 3. PyTorch Workflow/20. Writing Code to Load a PyTorch Model.mp4 | 79.58 MiB |
| 3. PyTorch Workflow/21. Setting Up to Practice Everything We Have Done Using Device Agnostic code.mp4 | 45.8 MiB |
| 3. PyTorch Workflow/22. Putting Everything Together (Part 1) Data.mp4 | 49.35 MiB |
| 3. PyTorch Workflow/23. Putting Everything Together (Part 2) Building a Model.mp4 | 88.7 MiB |
| 3. PyTorch Workflow/24. Putting Everything Together (Part 3) Training a Model.mp4 | 103 MiB |
| 3. PyTorch Workflow/25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.mp4 | 50.63 MiB |
| 3. PyTorch Workflow/26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.mp4 | 72.52 MiB |
| 3. PyTorch Workflow/27. Exercise Imposter Syndrome.mp4 | 39.25 MiB |
| 3. PyTorch Workflow/28. PyTorch Workflow Exercises and Extra-Curriculum.mp4 | 49.32 MiB |
| 3. PyTorch Workflow/3. Creating a Simple Dataset Using the Linear Regression Formula.mp4 | 68.65 MiB |
| 3. PyTorch Workflow/4. Splitting Our Data Into Training and Test Sets.mp4 | 65.22 MiB |
| 3. PyTorch Workflow/5. Building a function to Visualize Our Data.mp4 | 61.89 MiB |
| 3. PyTorch Workflow/6. Creating Our First PyTorch Model for Linear Regression.mp4 | 130.08 MiB |
| 3. PyTorch Workflow/7. Breaking Down What's Happening in Our PyTorch Linear regression Model.mp4 | 62.18 MiB |
| 3. PyTorch Workflow/8. Discussing Some of the Most Important PyTorch Model Building Classes.mp4 | 74.44 MiB |
| 3. PyTorch Workflow/9. Checking Out the Internals of Our PyTorch Model.mp4 | 102.71 MiB |
| 4. PyTorch Neural Network Classification/1. Introduction to Machine Learning Classification With PyTorch.mp4 | 84.58 MiB |
| 4. PyTorch Neural Network Classification/10. Loss Function Optimizer and Evaluation Function for Our Classification Network.mp4 | 161.06 MiB |
| 4. PyTorch Neural Network Classification/11. Going from Model Logits to Prediction Probabilities to Prediction Labels.mp4 | 134.54 MiB |
| 4. PyTorch Neural Network Classification/12. Coding a Training and Testing Optimization Loop for Our Classification Model.mp4 | 126.75 MiB |
| 4. PyTorch Neural Network Classification/13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.mp4 | 149.99 MiB |
| 4. PyTorch Neural Network Classification/14. Discussing Options to Improve a Model.mp4 | 80.87 MiB |
| 4. PyTorch Neural Network Classification/15. Creating a New Model with More Layers and Hidden Units.mp4 | 68.81 MiB |
| 4. PyTorch Neural Network Classification/16. Writing Training and Testing Code to See if Our Upgraded Model Performs Better.mp4 | 118.64 MiB |
| 4. PyTorch Neural Network Classification/17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.mp4 | 61.36 MiB |
| 4. PyTorch Neural Network Classification/18. Building and Training a Model to Fit on Straight Line Data.mp4 | 71.67 MiB |
| 4. PyTorch Neural Network Classification/19. Evaluating Our Models Predictions on Straight Line Data.mp4 | 50.8 MiB |
| 4. PyTorch Neural Network Classification/2. Classification Problem Example Input and Output Shapes.mp4 | 49.97 MiB |
| 4. PyTorch Neural Network Classification/20. Introducing the Missing Piece for Our Classification Model Non-Linearity.mp4 | 96.51 MiB |
| 4. PyTorch Neural Network Classification/21. Building Our First Neural Network with Non-Linearity.mp4 | 92.59 MiB |
| 4. PyTorch Neural Network Classification/22. Writing Training and Testing Code for Our First Non-Linear Model.mp4 | 150.57 MiB |
| 4. PyTorch Neural Network Classification/23. Making Predictions with and Evaluating Our First Non-Linear Model.mp4 | 53.05 MiB |
| 4. PyTorch Neural Network Classification/24. Replicating Non-Linear Activation Functions with Pure PyTorch.mp4 | 80.74 MiB |
| 4. PyTorch Neural Network Classification/25. Putting It All Together (Part 1) Building a Multiclass Dataset.mp4 | 97.46 MiB |
| 4. PyTorch Neural Network Classification/26. Creating a Multi-Class Classification Model with PyTorch.mp4 | 107.44 MiB |
| 4. PyTorch Neural Network Classification/27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.mp4 | 65.06 MiB |
| 4. PyTorch Neural Network Classification/28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.mp4 | 97.05 MiB |
| 4. PyTorch Neural Network Classification/29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.mp4 | 150.09 MiB |
| 4. PyTorch Neural Network Classification/3. Typical Architecture of a Classification Neural Network (Overview).mp4 | 67.05 MiB |
| 4. PyTorch Neural Network Classification/30. Making Predictions with and Evaluating Our Multi-Class Classification Model.mp4 | 77.05 MiB |
| 4. PyTorch Neural Network Classification/31. Discussing a Few More Classification Metrics.mp4 | 97.54 MiB |
| 4. PyTorch Neural Network Classification/32. PyTorch Classification Exercises and Extra-Curriculum.mp4 | 41.47 MiB |
| 4. PyTorch Neural Network Classification/4. Making a Toy Classification Dataset.mp4 | 91.48 MiB |
| 4. PyTorch Neural Network Classification/5. Turning Our Data into Tensors and Making a Training and Test Split.mp4 | 81.06 MiB |
| 4. PyTorch Neural Network Classification/6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.mp4 | 31.92 MiB |
| 4. PyTorch Neural Network Classification/7. Coding a Small Neural Network to Handle Our Classification Data.mp4 | 86.85 MiB |
| 4. PyTorch Neural Network Classification/8. Making Our Neural Network Visual.mp4 | 91.27 MiB |
| 4. PyTorch Neural Network Classification/9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.mp4 | 123.24 MiB |
| 5. PyTorch Computer Vision/1. What Is a Computer Vision Problem and What We Are Going to Cover.mp4 | 113.67 MiB |
| 5. PyTorch Computer Vision/10. Creating a Loss Function an Optimizer for Model 0.mp4 | 110.54 MiB |
| 5. PyTorch Computer Vision/11. Creating a Function to Time Our Modelling Code.mp4 | 45.61 MiB |
| 5. PyTorch Computer Vision/12. Writing Training and Testing Loops for Our Batched Data.mp4 | 157.56 MiB |
| 5. PyTorch Computer Vision/13. Writing an Evaluation Function to Get Our Models Results.mp4 | 106.79 MiB |
| 5. PyTorch Computer Vision/14. Setup Device-Agnostic Code for Running Experiments on the GPU.mp4 | 44.32 MiB |
| 5. PyTorch Computer Vision/15. Model 1 Creating a Model with Non-Linear Functions.mp4 | 86.39 MiB |
| 5. PyTorch Computer Vision/16. Mode 1 Creating a Loss Function and Optimizer.mp4 | 31.34 MiB |
| 5. PyTorch Computer Vision/17. Turing Our Training Loop into a Function.mp4 | 70.89 MiB |
| 5. PyTorch Computer Vision/18. Turing Our Testing Loop into a Function.mp4 | 50.89 MiB |
| 5. PyTorch Computer Vision/19. Training and Testing Model 1 with Our Training and Testing Functions.mp4 | 108.44 MiB |
| 5. PyTorch Computer Vision/2. Computer Vision Input and Output Shapes.mp4 | 85.02 MiB |
| 5. PyTorch Computer Vision/20. Getting a Results Dictionary for Model 1.mp4 | 41.35 MiB |
| 5. PyTorch Computer Vision/21. Model 2 Convolutional Neural Networks High Level Overview.mp4 | 94.63 MiB |
| 5. PyTorch Computer Vision/22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.mp4 | 208.33 MiB |
| 5. PyTorch Computer Vision/23. Model 2 Breaking Down Conv2D Step by Step.mp4 | 162.72 MiB |
| 5. PyTorch Computer Vision/24. Model 2 Breaking Down MaxPool2D Step by Step.mp4 | 158.11 MiB |
| 5. PyTorch Computer Vision/25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.mp4 | 174.82 MiB |
| 5. PyTorch Computer Vision/26. Model 2 Setting Up a Loss Function and Optimizer.mp4 | 27.88 MiB |
| 5. PyTorch Computer Vision/27. Model 2 Training Our First CNN and Evaluating Its Results.mp4 | 76.79 MiB |
| 5. PyTorch Computer Vision/28. Comparing the Results of Our Modelling Experiments.mp4 | 61.76 MiB |
| 5. PyTorch Computer Vision/29. Making Predictions on Random Test Samples with the Best Trained Model.mp4 | 83.66 MiB |
| 5. PyTorch Computer Vision/3. What Is a Convolutional Neural Network (CNN).mp4 | 55.4 MiB |
| 5. PyTorch Computer Vision/30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.mp4 | 63.49 MiB |
| 5. PyTorch Computer Vision/31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.mp4 | 160.84 MiB |
| 5. PyTorch Computer Vision/32. Evaluating Our Best Models Predictions with a Confusion Matrix.mp4 | 67.01 MiB |
| 5. PyTorch Computer Vision/33. Saving and Loading Our Best Performing Model.mp4 | 98.16 MiB |
| 5. PyTorch Computer Vision/34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.mp4 | 81.9 MiB |
| 5. PyTorch Computer Vision/4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.mp4 | 89.2 MiB |
| 5. PyTorch Computer Vision/5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.mp4 | 154 MiB |
| 5. PyTorch Computer Vision/6. Visualizing Random Samples of Data.mp4 | 68.11 MiB |
| 5. PyTorch Computer Vision/7. DataLoader Overview Understanding Mini-Batches.mp4 | 60.21 MiB |
| 5. PyTorch Computer Vision/8. Turning Our Datasets Into DataLoaders.mp4 | 100.24 MiB |
| 5. PyTorch Computer Vision/9. Model 0 Creating a Baseline Model with Two Linear Layers.mp4 | 136.88 MiB |
| 6. PyTorch Custom Datasets/1. What Is a Custom Dataset and What We Are Going to Cover.mp4 | 92.59 MiB |
| 6. PyTorch Custom Datasets/10. Visualizing a Loaded Image From the Train Dataset.mp4 | 76.73 MiB |
| 6. PyTorch Custom Datasets/11. Turning Our Image Datasets into PyTorch Dataloaders.mp4 | 84.33 MiB |
| 6. PyTorch Custom Datasets/12. Creating a Custom Dataset Class in PyTorch High Level Overview.mp4 | 74.7 MiB |
| 6. PyTorch Custom Datasets/13. Creating a Helper Function to Get Class Names From a Directory.mp4 | 79.09 MiB |
| 6. PyTorch Custom Datasets/14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.mp4 | 176.28 MiB |
| 6. PyTorch Custom Datasets/15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.mp4 | 69.5 MiB |
| 6. PyTorch Custom Datasets/16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.mp4 | 131.22 MiB |
| 6. PyTorch Custom Datasets/17. Turning Our Custom Datasets Into DataLoaders.mp4 | 80.62 MiB |
| 6. PyTorch Custom Datasets/18. Exploring State of the Art Data Augmentation With Torchvision Transforms.mp4 | 166.35 MiB |
| 6. PyTorch Custom Datasets/19. Building a Baseline Model (Part 1) Loading and Transforming Data.mp4 | 77.93 MiB |
| 6. PyTorch Custom Datasets/2. Importing PyTorch and Setting Up Device Agnostic Code.mp4 | 48.97 MiB |
| 6. PyTorch Custom Datasets/20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.mp4 | 117.23 MiB |
| 6. PyTorch Custom Datasets/21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.mp4 | 96.5 MiB |
| 6. PyTorch Custom Datasets/22. Using the Torchinfo Package to Get a Summary of Our Model.mp4 | 64.97 MiB |
| 6. PyTorch Custom Datasets/23. Creating Training and Testing loop Functions.mp4 | 106.17 MiB |
| 6. PyTorch Custom Datasets/24. Creating a Train Function to Train and Evaluate Our Models.mp4 | 103.47 MiB |
| 6. PyTorch Custom Datasets/25. Training and Evaluating Model 0 With Our Training Functions.mp4 | 89.28 MiB |
| 6. PyTorch Custom Datasets/26. Plotting the Loss Curves of Model 0.mp4 | 89.45 MiB |
| 6. PyTorch Custom Datasets/27. The Balance Between Overfitting and Underfitting and How to Deal With Each.mp4 | 131.82 MiB |
| 6. PyTorch Custom Datasets/28. Creating Augmented Training Datasets and DataLoaders for Model 1.mp4 | 98.83 MiB |
| 6. PyTorch Custom Datasets/29. Constructing and Training Model 1.mp4 | 60.65 MiB |
| 6. PyTorch Custom Datasets/3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.mp4 | 150.96 MiB |
| 6. PyTorch Custom Datasets/30. Plotting the Loss Curves of Model 1.mp4 | 31.69 MiB |
| 6. PyTorch Custom Datasets/31. Plotting the Loss Curves of All of Our Models Against Each Other.mp4 | 89.27 MiB |
| 6. PyTorch Custom Datasets/32. Predicting on Custom Data (Part 1) Downloading an Image.mp4 | 51.66 MiB |
| 6. PyTorch Custom Datasets/33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.mp4 | 67.99 MiB |
| 6. PyTorch Custom Datasets/34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.mp4 | 127.06 MiB |
| 6. PyTorch Custom Datasets/35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.mp4 | 36.07 MiB |
| 6. PyTorch Custom Datasets/36. Predicting on Custom Data (Part 5) Putting It All Together.mp4 | 113.03 MiB |
| 6. PyTorch Custom Datasets/37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.mp4 | 73.32 MiB |
| 6. PyTorch Custom Datasets/4. Becoming One With the Data (Part 1) Exploring the Data Format.mp4 | 87.61 MiB |
| 6. PyTorch Custom Datasets/5. Becoming One With the Data (Part 2) Visualizing a Random Image.mp4 | 115.34 MiB |
| 6. PyTorch Custom Datasets/6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.mp4 | 51.91 MiB |
| 6. PyTorch Custom Datasets/7. Transforming Data (Part 1) Turning Images Into Tensors.mp4 | 81.72 MiB |
| 6. PyTorch Custom Datasets/8. Transforming Data (Part 2) Visualizing Transformed Images.mp4 | 127.58 MiB |
| 6. PyTorch Custom Datasets/9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.mp4 | 98.17 MiB |
| 6. PyTorch Custom Datasets/Download Paid Udemy Courses For Free.url | 116 B |
| 6. PyTorch Custom Datasets/GetFreeCourses.Co.url | 116 B |
| 7. PyTorch Going Modular/1. What Is Going Modular and What We Are Going to Cover.mp4 | 100.12 MiB |
| 7. PyTorch Going Modular/10. Going Modular Summary, Exercises and Extra-Curriculum.mp4 | 80.67 MiB |
| 7. PyTorch Going Modular/2. Going Modular Notebook (Part 1) Running It End to End.mp4 | 104.92 MiB |
| 7. PyTorch Going Modular/3. Downloading a Dataset.mp4 | 67.64 MiB |
| 7. PyTorch Going Modular/4. Writing the Outline for Our First Python Script to Setup the Data.mp4 | 156.79 MiB |
| 7. PyTorch Going Modular/5. Creating a Python Script to Create Our PyTorch DataLoaders.mp4 | 135.14 MiB |
| 7. PyTorch Going Modular/6. Turning Our Model Building Code into a Python Script.mp4 | 115.13 MiB |
| 7. PyTorch Going Modular/7. Turning Our Model Training Code into a Python Script.mp4 | 8 MiB |
| 7. PyTorch Going Modular/8. Turning Our Utility Function to Save a Model into a Python Script.mp4 | 75.79 MiB |
| 7. PyTorch Going Modular/9. Creating a Training Script to Train Our Model in One Line of Code.mp4 | 165.52 MiB |
| 8. PyTorch Transfer Learning/1. Introduction What is Transfer Learning and Why Use It.mp4 | 97.26 MiB |
| 8. PyTorch Transfer Learning/10. Different Kinds of Transfer Learning.mp4 | 56.96 MiB |
| 8. PyTorch Transfer Learning/11. Getting a Summary of the Different Layers of Our Model.mp4 | 76.04 MiB |
| 8. PyTorch Transfer Learning/12. Freezing the Base Layers of Our Model and Updating the Classifier Head.mp4 | 160.67 MiB |
| 8. PyTorch Transfer Learning/13. Training Our First Transfer Learning Feature Extractor Model.mp4 | 74.81 MiB |
| 8. PyTorch Transfer Learning/14. Plotting the Loss curves of Our Transfer Learning Model.mp4 | 58.93 MiB |
| 8. PyTorch Transfer Learning/15. Outlining the Steps to Make Predictions on the Test Images.mp4 | 66.74 MiB |
| 8. PyTorch Transfer Learning/16. Creating a Function Predict On and Plot Images.mp4 | 101.67 MiB |
| 8. PyTorch Transfer Learning/17. Making and Plotting Predictions on Test Images.mp4 | 78.14 MiB |
| 8. PyTorch Transfer Learning/18. Making a Prediction on a Custom Image.mp4 | 67.83 MiB |
| 8. PyTorch Transfer Learning/19. Main Takeaways, Exercises and Extra- Curriculum.mp4 | 44.43 MiB |
| 8. PyTorch Transfer Learning/2. Where Can You Find Pretrained Models and What We Are Going to Cover.mp4 | 55.85 MiB |
| 8. PyTorch Transfer Learning/3. Installing the Latest Versions of Torch and Torchvision.mp4 | 82.39 MiB |
| 8. PyTorch Transfer Learning/4. Downloading Our Previously Written Code from Going Modular.mp4 | 83.75 MiB |
| 8. PyTorch Transfer Learning/5. Downloading Pizza, Steak, Sushi Image Data from Github.mp4 | 72.17 MiB |
| 8. PyTorch Transfer Learning/6. Turning Our Data into DataLoaders with Manually Created Transforms.mp4 | 141.48 MiB |
| 8. PyTorch Transfer Learning/7. Turning Our Data into DataLoaders with Automatic Created Transforms.mp4 | 139.74 MiB |
| 8. PyTorch Transfer Learning/8. Which Pretrained Model Should You Use.mp4 | 128.78 MiB |
| 8. PyTorch Transfer Learning/9. Setting Up a Pretrained Model with Torchvision.mp4 | 113.15 MiB |
| 9. PyTorch Experiment Tracking/1. What Is Experiment Tracking and Why Track Experiments.mp4 | 61.86 MiB |
| 9. PyTorch Experiment Tracking/10. Creating a Function to Create SummaryWriter Instances.mp4 | 80.1 MiB |
| 9. PyTorch Experiment Tracking/11. Adapting Our Train Function to Be Able to Track Multiple Experiments.mp4 | 66.54 MiB |
| 9. PyTorch Experiment Tracking/12. What Experiments Should You Try.mp4 | 46.92 MiB |
| 9. PyTorch Experiment Tracking/13. Discussing the Experiments We Are Going to Try.mp4 | 48.3 MiB |
| 9. PyTorch Experiment Tracking/14. Downloading Datasets for Our Modelling Experiments.mp4 | 66.42 MiB |
| 9. PyTorch Experiment Tracking/15. Turning Our Datasets into DataLoaders Ready for Experimentation.mp4 | 78.07 MiB |
| 9. PyTorch Experiment Tracking/16. Creating Functions to Prepare Our Feature Extractor Models.mp4 | 159.21 MiB |
| 9. PyTorch Experiment Tracking/17. Coding Out the Steps to Run a Series of Modelling Experiments.mp4 | 127.62 MiB |
| 9. PyTorch Experiment Tracking/18. Running Eight Different Modelling Experiments in 5 Minutes.mp4 | 45.66 MiB |
| 9. PyTorch Experiment Tracking/19. Viewing Our Modelling Experiments in TensorBoard.mp4 | 140.3 MiB |
| 9. PyTorch Experiment Tracking/2. Getting Setup by Importing Torch Libraries and Going Modular Code.mp4 | 93.39 MiB |
| 9. PyTorch Experiment Tracking/20. Loading the Best Model and Making Predictions on Random Images from the Test Set.mp4 | 99.19 MiB |
| 9. PyTorch Experiment Tracking/21. Making a Prediction on Our Own Custom Image with the Best Model.mp4 | 39.71 MiB |
| 9. PyTorch Experiment Tracking/22. Main Takeaways, Exercises and Extra- Curriculum.mp4 | 43.59 MiB |
| 9. PyTorch Experiment Tracking/3. Creating a Function to Download Data.mp4 | 95.23 MiB |
| 9. PyTorch Experiment Tracking/4. Turning Our Data into DataLoaders Using Manual Transforms.mp4 | 92.72 MiB |
| 9. PyTorch Experiment Tracking/5. Turning Our Data into DataLoaders Using Automatic Transforms.mp4 | 82.01 MiB |
| 9. PyTorch Experiment Tracking/6. Preparing a Pretrained Model for Our Own Problem.mp4 | 113.16 MiB |
| 9. PyTorch Experiment Tracking/7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.mp4 | 150.28 MiB |
| 9. PyTorch Experiment Tracking/8. Training a Single Model and Saving the Results to TensorBoard.mp4 | 41.79 MiB |
| 9. PyTorch Experiment Tracking/9. Exploring Our Single Models Results with TensorBoard.mp4 | 116.26 MiB |
| Download Paid Udemy Courses For Free.url | 116 B |
| GetFreeCourses.Co.url | 116 B |
