[FreeCourseSite.com] Udemy - PyTorch for Deep Learning in 2023 Zero to Mastery

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[FreeCourseSite.com] Udemy - PyTorch for Deep Learning in 2023 Zero to Mastery

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文件列表

  • 5. PyTorch Computer Vision/22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.mp4 218.5 MB
  • 10. PyTorch Paper Replicating/37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces.mp4 200.1 MB
  • 10. PyTorch Paper Replicating/36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.mp4 197.9 MB
  • 11. PyTorch Model Deployment/23. Creating a Function to Make and Time Predictions with Our Models.mp4 194.8 MB
  • 11. PyTorch Model Deployment/49. Training Food Vision Big Our Biggest Model Yet!.mp4 193.2 MB
  • 3. PyTorch Workflow/15. Reviewing the Steps in a Training Loop Step by Step.mp4 186.1 MB
  • 6. PyTorch Custom Datasets/14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.mp4 184.8 MB
  • 5. PyTorch Computer Vision/25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.mp4 183.3 MB
  • 10. PyTorch Paper Replicating/23. Creating the Patch Embedding Layer with PyTorch.mp4 178.3 MB
  • 6. PyTorch Custom Datasets/18. Exploring State of the Art Data Augmentation With Torchvision Transforms.mp4 174.4 MB
  • 7. PyTorch Going Modular/9. Creating a Training Script to Train Our Model in One Line of Code.mp4 173.6 MB
  • 10. PyTorch Paper Replicating/44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.mp4 172.8 MB
  • 10. PyTorch Paper Replicating/30. Turning Equation 2 into Code.mp4 171.8 MB
  • 5. PyTorch Computer Vision/23. Model 2 Breaking Down Conv2D Step by Step.mp4 170.6 MB
  • 11. PyTorch Model Deployment/56. Deploying Food Vision Big to Hugging Face Spaces.mp4 170.4 MB
  • 3. PyTorch Workflow/18. Reviewing What Happens in a Testing Loop Step by Step.mp4 169.4 MB
  • 4. PyTorch Neural Network Classification/10. Loss Function Optimizer and Evaluation Function for Our Classification Network.mp4 168.9 MB
  • 5. PyTorch Computer Vision/31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.mp4 168.6 MB
  • 8. PyTorch Transfer Learning/12. Freezing the Base Layers of Our Model and Updating the Classifier Head.mp4 168.5 MB
  • 10. PyTorch Paper Replicating/16. Calculating the Input and Output Shape of the Embedding Layer by Hand.mp4 168.4 MB
  • 9. PyTorch Experiment Tracking/16. Creating Functions to Prepare Our Feature Extractor Models.mp4 166.9 MB
  • 5. PyTorch Computer Vision/24. Model 2 Breaking Down MaxPool2D Step by Step.mp4 165.8 MB
  • 5. PyTorch Computer Vision/12. Writing Training and Testing Loops for Our Batched Data.mp4 165.2 MB
  • 7. PyTorch Going Modular/4. Writing the Outline for Our First Python Script to Setup the Data.mp4 164.4 MB
  • 5. PyTorch Computer Vision/5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.mp4 161.5 MB
  • 6. PyTorch Custom Datasets/3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.mp4 158.3 MB
  • 4. PyTorch Neural Network Classification/22. Writing Training and Testing Code for Our First Non-Linear Model.mp4 157.9 MB
  • 9. PyTorch Experiment Tracking/7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.mp4 157.6 MB
  • 10. PyTorch Paper Replicating/17. Turning a Single Image into Patches (Part 1 Patching the Top Row).mp4 157.4 MB
  • 4. PyTorch Neural Network Classification/29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.mp4 157.4 MB
  • 4. PyTorch Neural Network Classification/13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.mp4 157.3 MB
  • 10. PyTorch Paper Replicating/28. Equation 2 Multihead Attention Overview.mp4 151.1 MB
  • 11. PyTorch Model Deployment/42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.mp4 150.6 MB
  • 10. PyTorch Paper Replicating/19. Creating Patch Embeddings with a Convolutional Layer.mp4 149.5 MB
  • 8. PyTorch Transfer Learning/6. Turning Our Data into DataLoaders with Manually Created Transforms.mp4 148.4 MB
  • 10. PyTorch Paper Replicating/11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.mp4 147.8 MB
  • 9. PyTorch Experiment Tracking/19. Viewing Our Modelling Experiments in TensorBoard.mp4 147.1 MB
  • 11. PyTorch Model Deployment/3. Where Is My Model Going to Go.mp4 146.6 MB
  • 8. PyTorch Transfer Learning/7. Turning Our Data into DataLoaders with Automatic Created Transforms.mp4 146.5 MB
  • 11. PyTorch Model Deployment/39. Turning Our Food Vision Mini Demo App Into a Python Script.mp4 144.3 MB
  • 5. PyTorch Computer Vision/9. Model 0 Creating a Baseline Model with Two Linear Layers.mp4 143.5 MB
  • 11. PyTorch Model Deployment/32. Bringing Food Vision Mini to Life in a Live Web Application.mp4 142.0 MB
  • 7. PyTorch Going Modular/5. Creating a Python Script to Create Our PyTorch DataLoaders.mp4 141.7 MB
  • 3. PyTorch Workflow/17. Writing Testing Loop Code and Discussing What's Happening Step by Step.mp4 141.6 MB
  • 10. PyTorch Paper Replicating/27. Equation 1 Putting it All Together.mp4 141.4 MB
  • 11. PyTorch Model Deployment/27. Visualizing the Performance vs Speed Trade-off.mp4 141.2 MB
  • 4. PyTorch Neural Network Classification/11. Going from Model Logits to Prediction Probabilities to Prediction Labels.mp4 141.1 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/22. Experiment 3 - Training a Non-Compiled Model for Multiple Runs.mp4 139.2 MB
  • 10. PyTorch Paper Replicating/24. Creating the Class Token Embedding.mp4 138.4 MB
  • 10. PyTorch Paper Replicating/25. Creating the Class Token Embedding - Less Birds.mp4 138.3 MB
  • 6. PyTorch Custom Datasets/27. The Balance Between Overfitting and Underfitting and How to Deal With Each.mp4 138.2 MB
  • 6. PyTorch Custom Datasets/16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.mp4 137.6 MB
  • 10. PyTorch Paper Replicating/18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).mp4 137.0 MB
  • 3. PyTorch Workflow/6. Creating Our First PyTorch Model for Linear Regression.mp4 136.4 MB
  • 3. PyTorch Workflow/19. Writing Code to Save a PyTorch Model.mp4 136.1 MB
  • 10. PyTorch Paper Replicating/20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.mp4 135.3 MB
  • 3. PyTorch Workflow/13. PyTorch Training Loop Steps and Intuition.mp4 135.0 MB
  • 8. PyTorch Transfer Learning/8. Which Pretrained Model Should You Use.mp4 135.0 MB
  • 9. PyTorch Experiment Tracking/17. Coding Out the Steps to Run a Series of Modelling Experiments.mp4 133.8 MB
  • 6. PyTorch Custom Datasets/8. Transforming Data (Part 2) Visualizing Transformed Images.mp4 133.8 MB
  • 6. PyTorch Custom Datasets/34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.mp4 133.2 MB
  • 4. PyTorch Neural Network Classification/12. Coding a Training and Testing Optimization Loop for Our Classification Model.mp4 132.9 MB
  • 10. PyTorch Paper Replicating/13. Breaking Down Equation 2 and 3.mp4 131.1 MB
  • 4. PyTorch Neural Network Classification/9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.mp4 129.2 MB
  • 10. PyTorch Paper Replicating/15. Breaking Down Table 1.mp4 128.0 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/19. Comparing the Results of Experiment 1 and 2.mp4 126.4 MB
  • 11. PyTorch Model Deployment/47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.mp4 125.5 MB
  • 4. PyTorch Neural Network Classification/16. Writing Training and Testing Code to See if Our Upgraded Model Performs Better.mp4 124.4 MB
  • 10. PyTorch Paper Replicating/40. Creating a Loss Function and Optimizer from the ViT Paper.mp4 124.1 MB
  • 6. PyTorch Custom Datasets/20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.mp4 122.9 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/21. Preparing Functions for Experiment 3 and 4.mp4 121.9 MB
  • 9. PyTorch Experiment Tracking/9. Exploring Our Single Models Results with TensorBoard.mp4 121.9 MB
  • 3. PyTorch Workflow/12. Setting Up an Optimizer and a Loss Function.mp4 121.6 MB
  • 6. PyTorch Custom Datasets/5. Becoming One With the Data (Part 2) Visualizing a Random Image.mp4 120.9 MB
  • 7. PyTorch Going Modular/6. Turning Our Model Building Code into a Python Script.mp4 120.7 MB
  • 5. PyTorch Computer Vision/1. What Is a Computer Vision Problem and What We Are Going to Cover.mp4 119.2 MB
  • 2. PyTorch Fundamentals/7. What Is and Why PyTorch.mp4 119.1 MB
  • 9. PyTorch Experiment Tracking/6. Preparing a Pretrained Model for Our Own Problem.mp4 118.7 MB
  • 8. PyTorch Transfer Learning/9. Setting Up a Pretrained Model with Torchvision.mp4 118.6 MB
  • 6. PyTorch Custom Datasets/36. Predicting on Custom Data (Part 5) Putting It All Together.mp4 118.5 MB
  • 2. PyTorch Fundamentals/30. Different Ways of Accessing a GPU in PyTorch.mp4 118.5 MB
  • 11. PyTorch Model Deployment/41. Downloading Our Food Vision Mini App Files from Google Colab.mp4 117.7 MB
  • 10. PyTorch Paper Replicating/29. Equation 2 Layernorm Overview.mp4 117.2 MB
  • 10. PyTorch Paper Replicating/38. Bringing Our Own Vision Transformer to Life - Part 2 The Forward Method.mp4 116.8 MB
  • 10. PyTorch Paper Replicating/3. Where Can You Find Machine Learning Research Papers and Code.mp4 116.1 MB
  • 5. PyTorch Computer Vision/10. Creating a Loss Function an Optimizer for Model 0.mp4 115.9 MB
  • 10. PyTorch Paper Replicating/26. Creating the Position Embedding.mp4 114.5 MB
  • 5. PyTorch Computer Vision/19. Training and Testing Model 1 with Our Training and Testing Functions.mp4 113.7 MB
  • 4. PyTorch Neural Network Classification/26. Creating a Multi-Class Classification Model with PyTorch.mp4 112.7 MB
  • 10. PyTorch Paper Replicating/33. Turning Equation 3 into Code.mp4 112.3 MB
  • 3. PyTorch Workflow/10. Making Predictions With Our Random Model Using Inference Mode.mp4 112.2 MB
  • 5. PyTorch Computer Vision/13. Writing an Evaluation Function to Get Our Models Results.mp4 112.0 MB
  • 6. PyTorch Custom Datasets/23. Creating Training and Testing loop Functions.mp4 111.3 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/18. Experiment 2 - Single Run with torch.compile.mp4 110.7 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/23. Experiment 4 - Training a Compiled Model for Multiple Runs.mp4 110.1 MB
  • 7. PyTorch Going Modular/2. Going Modular Notebook (Part 1) Running It End to End.mp4 110.0 MB
  • 11. PyTorch Model Deployment/54. Creating an App Script for Our Food Vision Big Model Gradio Demo.mp4 109.9 MB
  • 2. PyTorch Fundamentals/25. Reshaping, Viewing and Stacking Tensors.mp4 109.0 MB
  • 6. PyTorch Custom Datasets/24. Creating a Train Function to Train and Evaluate Our Models.mp4 108.5 MB
  • 10. PyTorch Paper Replicating/12. Breaking Down Equation 1.mp4 108.2 MB
  • 3. PyTorch Workflow/24. Putting Everything Together (Part 3) Training a Model.mp4 108.0 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/7. Setting the Default Device in PyTorch 2.mp4 108.0 MB
  • 3. PyTorch Workflow/9. Checking Out the Internals of Our PyTorch Model.mp4 107.7 MB
  • 3. PyTorch Workflow/16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.mp4 106.6 MB
  • 8. PyTorch Transfer Learning/16. Creating a Function Predict On and Plot Images.mp4 106.6 MB
  • 10. PyTorch Paper Replicating/42. Discussing what Our Training Setup Is Missing.mp4 106.1 MB
  • 5. PyTorch Computer Vision/8. Turning Our Datasets Into DataLoaders.mp4 105.1 MB
  • 7. PyTorch Going Modular/1. What Is Going Modular and What We Are Going to Cover.mp4 105.0 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/10. Creating a Function to Setup Our Model and Transforms.mp4 104.4 MB
  • 9. PyTorch Experiment Tracking/20. Loading the Best Model and Making Predictions on Random Images from the Test Set.mp4 104.0 MB
  • 10. PyTorch Paper Replicating/5. Getting Setup for Coding in Google Colab.mp4 103.9 MB
  • 6. PyTorch Custom Datasets/28. Creating Augmented Training Datasets and DataLoaders for Model 1.mp4 103.6 MB
  • 6. PyTorch Custom Datasets/9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.mp4 102.9 MB
  • 5. PyTorch Computer Vision/33. Saving and Loading Our Best Performing Model.mp4 102.9 MB
  • 11. PyTorch Model Deployment/24. Making and Timing Predictions with EffNetB2.mp4 102.4 MB
  • 4. PyTorch Neural Network Classification/31. Discussing a Few More Classification Metrics.mp4 102.3 MB
  • 4. PyTorch Neural Network Classification/25. Putting It All Together (Part 1) Building a Multiclass Dataset.mp4 102.2 MB
  • 2. PyTorch Fundamentals/22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.mp4 102.1 MB
  • 8. PyTorch Transfer Learning/1. Introduction What is Transfer Learning and Why Use It.mp4 102.0 MB
  • 4. PyTorch Neural Network Classification/28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.mp4 101.8 MB
  • 11. PyTorch Model Deployment/13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.mp4 101.7 MB
  • 4. PyTorch Neural Network Classification/20. Introducing the Missing Piece for Our Classification Model Non-Linearity.mp4 101.2 MB
  • 11. PyTorch Model Deployment/45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.mp4 101.2 MB
  • 6. PyTorch Custom Datasets/21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.mp4 101.2 MB
  • 11. PyTorch Model Deployment/30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.mp4 99.8 MB
  • 9. PyTorch Experiment Tracking/3. Creating a Function to Download Data.mp4 99.8 MB
  • 11. PyTorch Model Deployment/28. Gradio Overview and Installation.mp4 99.8 MB
  • 2. PyTorch Fundamentals/29. PyTorch Reproducibility (Taking the Random Out of Random).mp4 99.7 MB
  • 5. PyTorch Computer Vision/21. Model 2 Convolutional Neural Networks High Level Overview.mp4 99.2 MB
  • 2. PyTorch Fundamentals/13. Introduction to PyTorch Tensors.mp4 98.6 MB
  • 10. PyTorch Paper Replicating/1. What Is a Machine Learning Research Paper.mp4 98.5 MB
  • 11. PyTorch Model Deployment/22. Outlining the Steps for Making and Timing Predictions for Our Models.mp4 98.0 MB
  • 9. PyTorch Experiment Tracking/2. Getting Setup by Importing Torch Libraries and Going Modular Code.mp4 97.9 MB
  • 9. PyTorch Experiment Tracking/4. Turning Our Data into DataLoaders Using Manual Transforms.mp4 97.2 MB
  • 6. PyTorch Custom Datasets/1. What Is a Custom Dataset and What We Are Going to Cover.mp4 97.1 MB
  • 4. PyTorch Neural Network Classification/21. Building Our First Neural Network with Non-Linearity.mp4 97.1 MB
  • 10. PyTorch Paper Replicating/14. Breaking Down Equation 4.mp4 96.9 MB
  • 11. PyTorch Model Deployment/36. Creating an Examples Directory with Example Food Vision Mini Images.mp4 96.9 MB
  • 11. PyTorch Model Deployment/10. Creating an EffNetB2 Feature Extractor Model.mp4 96.6 MB
  • 11. PyTorch Model Deployment/43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.mp4 96.0 MB
  • 4. PyTorch Neural Network Classification/4. Making a Toy Classification Dataset.mp4 95.9 MB
  • 4. PyTorch Neural Network Classification/8. Making Our Neural Network Visual.mp4 95.7 MB
  • 10. PyTorch Paper Replicating/7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.mp4 94.1 MB
  • 11. PyTorch Model Deployment/26. Comparing EffNetB2 and ViT Model Statistics.mp4 94.0 MB
  • 10. PyTorch Paper Replicating/21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.mp4 94.0 MB
  • 11. PyTorch Model Deployment/34. Outlining the File Structure of Our Deployed App.mp4 93.9 MB
  • 6. PyTorch Custom Datasets/26. Plotting the Loss Curves of Model 0.mp4 93.8 MB
  • 6. PyTorch Custom Datasets/25. Training and Evaluating Model 0 With Our Training Functions.mp4 93.6 MB
  • 6. PyTorch Custom Datasets/31. Plotting the Loss Curves of All of Our Models Against Each Other.mp4 93.6 MB
  • 5. PyTorch Computer Vision/4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.mp4 93.5 MB
  • 10. PyTorch Paper Replicating/32. Equation 3 Replication Overview.mp4 93.0 MB
  • 3. PyTorch Workflow/23. Putting Everything Together (Part 2) Building a Model.mp4 93.0 MB
  • 2. PyTorch Fundamentals/26. Squeezing, Unsqueezing and Permuting Tensors.mp4 92.7 MB
  • 10. PyTorch Paper Replicating/4. What We Are Going to Cover.mp4 92.0 MB
  • 6. PyTorch Custom Datasets/4. Becoming One With the Data (Part 1) Exploring the Data Format.mp4 91.9 MB
  • 10. PyTorch Paper Replicating/10. Breaking Down Figure 1 of the ViT Paper.mp4 91.3 MB
  • 4. PyTorch Neural Network Classification/7. Coding a Small Neural Network to Handle Our Classification Data.mp4 91.1 MB
  • 2. PyTorch Fundamentals/14. Creating Random Tensors in PyTorch.mp4 90.6 MB
  • 5. PyTorch Computer Vision/15. Model 1 Creating a Model with Non-Linear Functions.mp4 90.6 MB
  • 10. PyTorch Paper Replicating/50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.mp4 89.6 MB
  • 5. PyTorch Computer Vision/2. Computer Vision Input and Output Shapes.mp4 89.1 MB
  • 10. PyTorch Paper Replicating/39. Getting a Visual Summary of Our Custom Vision Transformer.mp4 89.0 MB
  • 10. PyTorch Paper Replicating/35. Combining equation 2 and 3 to Create the Transformer Encoder.mp4 89.0 MB
  • 4. PyTorch Neural Network Classification/1. Introduction to Machine Learning Classification With PyTorch.mp4 88.7 MB
  • 6. PyTorch Custom Datasets/11. Turning Our Image Datasets into PyTorch Dataloaders.mp4 88.4 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/13. Getting More Potential Speedups with TensorFloat-32.mp4 87.9 MB
  • 8. PyTorch Transfer Learning/4. Downloading Our Previously Written Code from Going Modular.mp4 87.8 MB
  • 5. PyTorch Computer Vision/29. Making Predictions on Random Test Samples with the Best Trained Model.mp4 87.7 MB
  • 3. PyTorch Workflow/14. Writing Code for a PyTorch Training Loop.mp4 87.0 MB
  • 10. PyTorch Paper Replicating/34. Transformer Encoder Overview.mp4 86.9 MB
  • 8. PyTorch Transfer Learning/3. Installing the Latest Versions of Torch and Torchvision.mp4 86.4 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/1. Introduction to PyTorch 2.0.mp4 86.1 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/9. Introduction to PyTorch 2.mp4 86.1 MB
  • 9. PyTorch Experiment Tracking/5. Turning Our Data into DataLoaders Using Automatic Transforms.mp4 86.0 MB
  • 5. PyTorch Computer Vision/34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.mp4 85.9 MB
  • 11. PyTorch Model Deployment/57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.mp4 85.7 MB
  • 6. PyTorch Custom Datasets/7. Transforming Data (Part 1) Turning Images Into Tensors.mp4 85.7 MB
  • 2. PyTorch Fundamentals/17. Dealing With Tensor Data Types.mp4 85.4 MB
  • 4. PyTorch Neural Network Classification/5. Turning Our Data into Tensors and Making a Training and Test Split.mp4 85.0 MB
  • 4. PyTorch Neural Network Classification/14. Discussing Options to Improve a Model.mp4 84.8 MB
  • 4. PyTorch Neural Network Classification/24. Replicating Non-Linear Activation Functions with Pure PyTorch.mp4 84.7 MB
  • 7. PyTorch Going Modular/10. Going Modular Summary, Exercises and Extra-Curriculum.mp4 84.6 MB
  • 6. PyTorch Custom Datasets/17. Turning Our Custom Datasets Into DataLoaders.mp4 84.5 MB
  • 9. PyTorch Experiment Tracking/10. Creating a Function to Create SummaryWriter Instances.mp4 84.0 MB
  • 7. PyTorch Going Modular/7. Turning Our Model Training Code into a Python Script.mp4 83.9 MB
  • 11. PyTorch Model Deployment/29. Gradio Function Outline.mp4 83.8 MB
  • 3. PyTorch Workflow/20. Writing Code to Load a PyTorch Model.mp4 83.4 MB
  • 6. PyTorch Custom Datasets/13. Creating a Helper Function to Get Class Names From a Directory.mp4 82.9 MB
  • 11. PyTorch Model Deployment/17. Creating a Vision Transformer Feature Extractor Model.mp4 82.3 MB
  • 8. PyTorch Transfer Learning/17. Making and Plotting Predictions on Test Images.mp4 81.9 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/17. Experiment 1 - Single Run without torch.compile.mp4 81.9 MB
  • 9. PyTorch Experiment Tracking/15. Turning Our Datasets into DataLoaders Ready for Experimentation.mp4 81.8 MB
  • 6. PyTorch Custom Datasets/19. Building a Baseline Model (Part 1) Loading and Transforming Data.mp4 81.7 MB
  • 10. PyTorch Paper Replicating/9. Replicating a Vision Transformer - High Level Overview.mp4 81.6 MB
  • 2. PyTorch Fundamentals/20. Matrix Multiplication (Part 1).mp4 81.6 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/6. Getting Info from Our GPUs and Seeing if They're Capable of Using PyTorch 2.mp4 81.3 MB
  • 4. PyTorch Neural Network Classification/30. Making Predictions with and Evaluating Our Multi-Class Classification Model.mp4 80.8 MB
  • 5. PyTorch Computer Vision/27. Model 2 Training Our First CNN and Evaluating Its Results.mp4 80.5 MB
  • 6. PyTorch Custom Datasets/10. Visualizing a Loaded Image From the Train Dataset.mp4 80.4 MB
  • 10. PyTorch Paper Replicating/46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.mp4 80.0 MB
  • 8. PyTorch Transfer Learning/11. Getting a Summary of the Different Layers of Our Model.mp4 79.7 MB
  • 7. PyTorch Going Modular/8. Turning Our Utility Function to Save a Model into a Python Script.mp4 79.5 MB
  • 1. Introduction/1. PyTorch for Deep Learning.mp4 79.0 MB
  • 1. Introduction/3. Join Our Online Classroom!.mp4 79.0 MB
  • 8. PyTorch Transfer Learning/13. Training Our First Transfer Learning Feature Extractor Model.mp4 78.4 MB
  • 6. PyTorch Custom Datasets/12. Creating a Custom Dataset Class in PyTorch High Level Overview.mp4 78.3 MB
  • 3. PyTorch Workflow/8. Discussing Some of the Most Important PyTorch Model Building Classes.mp4 78.1 MB
  • 11. PyTorch Model Deployment/1. What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.mp4 77.4 MB
  • 6. PyTorch Custom Datasets/37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.mp4 76.9 MB
  • 3. PyTorch Workflow/26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.mp4 76.1 MB
  • 11. PyTorch Model Deployment/25. Making and Timing Predictions with ViT.mp4 76.0 MB
  • 8. PyTorch Transfer Learning/5. Downloading Pizza, Steak, Sushi Image Data from Github.mp4 75.7 MB
  • 11. PyTorch Model Deployment/37. Writing Code to Move Our Saved EffNetB2 Model File.mp4 75.4 MB
  • 4. PyTorch Neural Network Classification/18. Building and Training a Model to Fit on Straight Line Data.mp4 75.2 MB
  • 11. PyTorch Model Deployment/46. Downloading the Food 101 Dataset.mp4 75.1 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/12. Setting the Batch Size and Data Size Programmatically.mp4 74.4 MB
  • 5. PyTorch Computer Vision/17. Turing Our Training Loop into a Function.mp4 74.3 MB
  • 2. PyTorch Fundamentals/4. Anatomy of Neural Networks.mp4 73.7 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/11. Discussing How to Get Better Relative Speedups for Training Models.mp4 73.5 MB
  • 2. PyTorch Fundamentals/12. Getting Setup to Write PyTorch Code.mp4 73.4 MB
  • 3. PyTorch Workflow/2. Getting Setup and What We Are Covering.mp4 73.1 MB
  • 6. PyTorch Custom Datasets/15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.mp4 72.9 MB
  • 3. PyTorch Workflow/11. Training a Model Intuition (The Things We Need).mp4 72.9 MB
  • 4. PyTorch Neural Network Classification/15. Creating a New Model with More Layers and Hidden Units.mp4 72.2 MB
  • 3. PyTorch Workflow/3. Creating a Simple Dataset Using the Linear Regression Formula.mp4 72.0 MB
  • 5. PyTorch Computer Vision/6. Visualizing Random Samples of Data.mp4 71.4 MB
  • 6. PyTorch Custom Datasets/33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.mp4 71.3 MB
  • 8. PyTorch Transfer Learning/18. Making a Prediction on a Custom Image.mp4 71.1 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/15. Creating Training and Test DataLoaders.mp4 71.1 MB
  • 7. PyTorch Going Modular/3. Downloading a Dataset.mp4 70.9 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/14. Downloading the CIFAR10 Dataset.mp4 70.8 MB
  • 11. PyTorch Model Deployment/4. How Is My Model Going to Function.mp4 70.6 MB
  • 4. PyTorch Neural Network Classification/3. Typical Architecture of a Classification Neural Network (Overview).mp4 70.3 MB
  • 5. PyTorch Computer Vision/32. Evaluating Our Best Models Predictions with a Confusion Matrix.mp4 70.3 MB
  • 11. PyTorch Model Deployment/52. Saving Food 101 Class Names to a Text File and Reading them Back In.mp4 70.1 MB
  • 8. PyTorch Transfer Learning/15. Outlining the Steps to Make Predictions on the Test Images.mp4 70.0 MB
  • 9. PyTorch Experiment Tracking/11. Adapting Our Train Function to Be Able to Track Multiple Experiments.mp4 69.8 MB
  • 2. PyTorch Fundamentals/18. Getting Tensor Attributes.mp4 69.7 MB
  • 9. PyTorch Experiment Tracking/14. Downloading Datasets for Our Modelling Experiments.mp4 69.6 MB
  • 11. PyTorch Model Deployment/5. Some Tools and Places to Deploy Machine Learning Models.mp4 68.5 MB
  • 3. PyTorch Workflow/4. Splitting Our Data Into Training and Test Sets.mp4 68.4 MB
  • 4. PyTorch Neural Network Classification/27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.mp4 68.2 MB
  • 6. PyTorch Custom Datasets/22. Using the Torchinfo Package to Get a Summary of Our Model.mp4 68.1 MB
  • 11. PyTorch Model Deployment/33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.mp4 68.0 MB
  • 2. PyTorch Fundamentals/31. Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.mp4 67.6 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/25. Potential Extensions and Resources to Learn More.mp4 67.2 MB
  • 5. PyTorch Computer Vision/30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.mp4 66.6 MB
  • 10. PyTorch Paper Replicating/43. Plotting a Loss Curve for Our ViT Model.mp4 66.5 MB
  • 11. PyTorch Model Deployment/16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.mp4 66.3 MB
  • 11. PyTorch Model Deployment/7. Getting Setup to Code.mp4 65.9 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/24. Comparing the Results of Experiment 3 and 4.mp4 65.9 MB
  • 3. PyTorch Workflow/7. Breaking Down What's Happening in Our PyTorch Linear regression Model.mp4 65.2 MB
  • 11. PyTorch Model Deployment/19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.mp4 65.0 MB
  • 3. PyTorch Workflow/5. Building a function to Visualize Our Data.mp4 64.9 MB
  • 9. PyTorch Experiment Tracking/1. What Is Experiment Tracking and Why Track Experiments.mp4 64.9 MB
  • 5. PyTorch Computer Vision/28. Comparing the Results of Our Modelling Experiments.mp4 64.7 MB
  • 11. PyTorch Model Deployment/48. Turning Our Food 101 Datasets into DataLoaders.mp4 64.5 MB
  • 4. PyTorch Neural Network Classification/17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.mp4 64.3 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/16. Preparing Training and Testing Loops with Timing Steps for PyTorch 2.0 timing.mp4 63.7 MB
  • 6. PyTorch Custom Datasets/29. Constructing and Training Model 1.mp4 63.6 MB
  • 5. PyTorch Computer Vision/7. DataLoader Overview Understanding Mini-Batches.mp4 63.1 MB
  • 2. PyTorch Fundamentals/28. PyTorch Tensors and NumPy.mp4 62.7 MB
  • 8. PyTorch Transfer Learning/14. Plotting the Loss curves of Our Transfer Learning Model.mp4 61.8 MB
  • 11. PyTorch Model Deployment/9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.mp4 61.4 MB
  • 2. PyTorch Fundamentals/11. Important Resources For This Course.mp4 61.2 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/20. Saving the Results of Experiment 1 and 2.mp4 60.9 MB
  • 2. PyTorch Fundamentals/21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.mp4 60.6 MB
  • 11. PyTorch Model Deployment/11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.mp4 60.4 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/8. Discussing the Experiments We Are Going to Run for PyTorch 2.mp4 60.3 MB
  • 10. PyTorch Paper Replicating/45. Preparing Data to Be Used with a Pretrained ViT.mp4 60.0 MB
  • 8. PyTorch Transfer Learning/10. Different Kinds of Transfer Learning.mp4 59.7 MB
  • 2. PyTorch Fundamentals/27. Selecting Data From Tensors (Indexing).mp4 59.7 MB
  • 2. PyTorch Fundamentals/32. PyTorch Fundamentals Exercises and Extra-Curriculum.mp4 59.5 MB
  • 8. PyTorch Transfer Learning/2. Where Can You Find Pretrained Models and What We Are Going to Cover.mp4 58.6 MB
  • 11. PyTorch Model Deployment/15. Getting the Size of Our EffNetB2 Model in Megabytes.mp4 58.2 MB
  • 5. PyTorch Computer Vision/3. What Is a Convolutional Neural Network (CNN).mp4 58.1 MB
  • 2. PyTorch Fundamentals/3. Machine Learning vs. Deep Learning.mp4 58.0 MB
  • 10. PyTorch Paper Replicating/31. Checking the Inputs and Outputs of Equation.mp4 56.3 MB
  • 10. PyTorch Paper Replicating/41. Training our Custom ViT on Food Vision Mini.mp4 56.1 MB
  • 11. PyTorch Model Deployment/31. Creating a List of Examples to Pass to Our Gradio Demo.mp4 55.9 MB
  • 4. PyTorch Neural Network Classification/23. Making Predictions with and Evaluating Our First Non-Linear Model.mp4 55.6 MB
  • 11. PyTorch Model Deployment/50. Outlining the File Structure for Our Food Vision Big.mp4 55.3 MB
  • 6. PyTorch Custom Datasets/6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.mp4 54.4 MB
  • 6. PyTorch Custom Datasets/32. Predicting on Custom Data (Part 1) Downloading an Image.mp4 54.2 MB
  • 5. PyTorch Computer Vision/18. Turing Our Testing Loop into a Function.mp4 53.4 MB
  • 4. PyTorch Neural Network Classification/19. Evaluating Our Models Predictions on Straight Line Data.mp4 53.3 MB
  • 3. PyTorch Workflow/25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.mp4 53.1 MB
  • 2. PyTorch Fundamentals/9. What We Are Going To Cover With PyTorch.mp4 52.9 MB
  • 10. PyTorch Paper Replicating/22. Visualizing a Single Sequence Vector of Patch Embeddings.mp4 52.8 MB
  • 4. PyTorch Neural Network Classification/2. Classification Problem Example Input and Output Shapes.mp4 52.4 MB
  • 3. PyTorch Workflow/22. Putting Everything Together (Part 1) Data.mp4 51.7 MB
  • 3. PyTorch Workflow/28. PyTorch Workflow Exercises and Extra-Curriculum.mp4 51.7 MB
  • 6. PyTorch Custom Datasets/2. Importing PyTorch and Setting Up Device Agnostic Code.mp4 51.3 MB
  • 9. PyTorch Experiment Tracking/13. Discussing the Experiments We Are Going to Try.mp4 50.6 MB
  • 2. PyTorch Fundamentals/23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).mp4 50.5 MB
  • 11. PyTorch Model Deployment/2. Three Questions to Ask for Machine Learning Model Deployment.mp4 49.2 MB
  • 9. PyTorch Experiment Tracking/12. What Experiments Should You Try.mp4 49.2 MB
  • 11. PyTorch Model Deployment/21. Collecting Stats About Our-ViT Feature Extractor.mp4 48.1 MB
  • 3. PyTorch Workflow/21. Setting Up to Practice Everything We Have Done Using Device Agnostic code.mp4 48.0 MB
  • 9. PyTorch Experiment Tracking/18. Running Eight Different Modelling Experiments in 5 Minutes.mp4 47.9 MB
  • 5. PyTorch Computer Vision/11. Creating a Function to Time Our Modelling Code.mp4 47.8 MB
  • 11. PyTorch Model Deployment/38. Turning Our EffNetB2 Model Creation Function Into a Python Script.mp4 47.0 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/3. Getting Started with PyTorch 2 in Google Colab.mp4 46.7 MB
  • 1. Introduction/6. ZTM Resources.mp4 46.7 MB
  • 8. PyTorch Transfer Learning/19. Main Takeaways, Exercises and Extra- Curriculum.mp4 46.6 MB
  • 5. PyTorch Computer Vision/14. Setup Device-Agnostic Code for Running Experiments on the GPU.mp4 46.5 MB
  • 10. PyTorch Paper Replicating/6. Downloading Data for Food Vision Mini.mp4 46.0 MB
  • 11. PyTorch Model Deployment/20. Saving Our ViT Feature Extractor and Inspecting Its Size.mp4 45.9 MB
  • 9. PyTorch Experiment Tracking/22. Main Takeaways, Exercises and Extra- Curriculum.mp4 45.7 MB
  • 2. PyTorch Fundamentals/6. What Can Deep Learning Be Used For.mp4 45.3 MB
  • 10. PyTorch Paper Replicating/48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.mp4 43.8 MB
  • 9. PyTorch Experiment Tracking/8. Training a Single Model and Saving the Results to TensorBoard.mp4 43.8 MB
  • 4. PyTorch Neural Network Classification/32. PyTorch Classification Exercises and Extra-Curriculum.mp4 43.5 MB
  • 5. PyTorch Computer Vision/20. Getting a Results Dictionary for Model 1.mp4 43.4 MB
  • 11. PyTorch Model Deployment/6. What We Are Going to Cover.mp4 42.8 MB
  • 10. PyTorch Paper Replicating/47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.mp4 42.3 MB
  • 11. PyTorch Model Deployment/55. Zipping and Downloading Our Food Vision Big App Files.mp4 41.7 MB
  • 9. PyTorch Experiment Tracking/21. Making a Prediction on Our Own Custom Image with the Best Model.mp4 41.6 MB
  • 2. PyTorch Fundamentals/19. Manipulating Tensors (Tensor Operations).mp4 41.6 MB
  • 11. PyTorch Model Deployment/8. Downloading a Dataset for Food Vision Mini.mp4 41.2 MB
  • 3. PyTorch Workflow/27. Exercise Imposter Syndrome.mp4 41.2 MB
  • 11. PyTorch Model Deployment/35. Creating a Food Vision Mini Demo Directory to House Our App Files.mp4 41.0 MB
  • 11. PyTorch Model Deployment/44. Food Vision Big Project Outline.mp4 41.0 MB
  • 1. Introduction/2. Course Welcome and What Is Deep Learning.mp4 40.9 MB
  • 2. PyTorch Fundamentals/10. How To and How Not To Approach This Course.mp4 39.6 MB
  • 11. PyTorch Model Deployment/40. Creating a Requirements File for Our Food Vision Mini App.mp4 39.3 MB
  • 10. PyTorch Paper Replicating/49. Making Predictions on a Custom Image with Our Pretrained ViT.mp4 38.9 MB
  • 11. PyTorch Model Deployment/51. Downloading an Example Image and Moving Our Food Vision Big Model File.mp4 38.4 MB
  • 10. PyTorch Paper Replicating/8. Visualizing a Single Image.mp4 38.2 MB
  • 6. PyTorch Custom Datasets/35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.mp4 37.8 MB
  • 2. PyTorch Fundamentals/2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.mp4 37.0 MB
  • 2. PyTorch Fundamentals/16. Creating a Tensor Range and Tensors Like Other Tensors.mp4 34.2 MB
  • 4. PyTorch Neural Network Classification/6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.mp4 33.5 MB
  • 6. PyTorch Custom Datasets/30. Plotting the Loss Curves of Model 1.mp4 33.2 MB
  • 11. PyTorch Model Deployment/12. Creating DataLoaders for EffNetB2.mp4 32.9 MB
  • 5. PyTorch Computer Vision/16. Mode 1 Creating a Loss Function and Optimizer.mp4 32.9 MB
  • 3. PyTorch Workflow/1. Introduction and Where You Can Get Help.mp4 30.0 MB
  • 5. PyTorch Computer Vision/26. Model 2 Setting Up a Loss Function and Optimizer.mp4 29.2 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/5. Getting Setup for PyTorch 2.mp4 28.5 MB
  • 2. PyTorch Fundamentals/5. Different Types of Learning Paradigms.mp4 28.4 MB
  • 11. PyTorch Model Deployment/14. Saving Our EffNetB2 Model to File.mp4 28.0 MB
  • 2. PyTorch Fundamentals/8. What Are Tensors.mp4 26.2 MB
  • 2. PyTorch Fundamentals/15. Creating Tensors With Zeros and Ones in PyTorch.mp4 25.8 MB
  • 2. PyTorch Fundamentals/24. Finding The Positional Min and Max of Tensors.mp4 25.7 MB
  • 11. PyTorch Model Deployment/53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.mp4 25.1 MB
  • 10. PyTorch Paper Replicating/2. Why Replicate a Machine Learning Research Paper.mp4 24.4 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/4. PyTorch 2.0 - 30 Second Intro.mp4 23.5 MB
  • 14. Where To Go From Here/1. Thank You!.mp4 22.0 MB
  • 11. PyTorch Model Deployment/18. Creating DataLoaders for Our ViT Feature Extractor Model.mp4 20.7 MB
  • 12. Introduction to PyTorch 2.0 and torch.compile/2. What We Are Going to Cover and PyTorch 2 Reference Materials.mp4 15.8 MB
  • 2. PyTorch Fundamentals/1. Why Use Machine Learning or Deep Learning.mp4 14.5 MB
  • 5. PyTorch Computer Vision/12. Writing Training and Testing Loops for Our Batched Data.srt 31.9 kB
  • 5. PyTorch Computer Vision/22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.srt 31.7 kB
  • 11. PyTorch Model Deployment/49. Training Food Vision Big Our Biggest Model Yet!.srt 28.7 kB
  • 10. PyTorch Paper Replicating/37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces.srt 26.9 kB
  • 4. PyTorch Neural Network Classification/29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.srt 25.6 kB
  • 11. PyTorch Model Deployment/23. Creating a Function to Make and Time Predictions with Our Models.srt 24.7 kB
  • 5. PyTorch Computer Vision/5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.srt 24.4 kB
  • 5. PyTorch Computer Vision/23. Model 2 Breaking Down Conv2D Step by Step.srt 24.2 kB
  • 3. PyTorch Workflow/15. Reviewing the Steps in a Training Loop Step by Step.srt 23.7 kB
  • 4. PyTorch Neural Network Classification/10. Loss Function Optimizer and Evaluation Function for Our Classification Network.srt 23.7 kB
  • 6. PyTorch Custom Datasets/14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.srt 23.5 kB
  • 3. PyTorch Workflow/18. Reviewing What Happens in a Testing Loop Step by Step.srt 23.4 kB
  • 4. PyTorch Neural Network Classification/22. Writing Training and Testing Code for Our First Non-Linear Model.srt 23.4 kB
  • 4. PyTorch Neural Network Classification/13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.srt 23.4 kB
  • 10. PyTorch Paper Replicating/23. Creating the Patch Embedding Layer with PyTorch.srt 23.3 kB
  • 4. PyTorch Neural Network Classification/12. Coding a Training and Testing Optimization Loop for Our Classification Model.srt 23.3 kB
  • 5. PyTorch Computer Vision/24. Model 2 Breaking Down MaxPool2D Step by Step.srt 23.3 kB
  • 9. PyTorch Experiment Tracking/16. Creating Functions to Prepare Our Feature Extractor Models.srt 23.3 kB
  • 4. PyTorch Neural Network Classification/11. Going from Model Logits to Prediction Probabilities to Prediction Labels.srt 23.2 kB
  • 7. PyTorch Going Modular/9. Creating a Training Script to Train Our Model in One Line of Code.srt 22.4 kB
  • 3. PyTorch Workflow/13. PyTorch Training Loop Steps and Intuition.srt 22.2 kB
  • 5. PyTorch Computer Vision/9. Model 0 Creating a Baseline Model with Two Linear Layers.srt 22.2 kB
  • 3. PyTorch Workflow/19. Writing Code to Save a PyTorch Model.srt 22.1 kB
  • 11. PyTorch Model Deployment/27. Visualizing the Performance vs Speed Trade-off.srt 22.1 kB
  • 10. PyTorch Paper Replicating/28. Equation 2 Multihead Attention Overview.srt 22.1 kB
  • 5. PyTorch Computer Vision/31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.srt 22.1 kB
  • 6. PyTorch Custom Datasets/27. The Balance Between Overfitting and Underfitting and How to Deal With Each.srt 22.1 kB
  • 11. PyTorch Model Deployment/3. Where Is My Model Going to Go.srt 21.9 kB
  • 10. PyTorch Paper Replicating/36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.srt 21.8 kB
  • 11. PyTorch Model Deployment/42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.srt 21.3 kB
  • 10. PyTorch Paper Replicating/30. Turning Equation 2 into Code.srt 21.3 kB
  • 6. PyTorch Custom Datasets/18. Exploring State of the Art Data Augmentation With Torchvision Transforms.srt 21.2 kB
  • 4. PyTorch Neural Network Classification/9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.srt 21.2 kB
  • 10. PyTorch Paper Replicating/16. Calculating the Input and Output Shape of the Embedding Layer by Hand.srt 21.1 kB
  • 5. PyTorch Computer Vision/1. What Is a Computer Vision Problem and What We Are Going to Cover.srt 20.8 kB
  • 3. PyTorch Workflow/12. Setting Up an Optimizer and a Loss Function.srt 20.8 kB
  • 10. PyTorch Paper Replicating/17. Turning a Single Image into Patches (Part 1 Patching the Top Row).srt 20.8 kB
  • 2. PyTorch Fundamentals/25. Reshaping, Viewing and Stacking Tensors.srt 20.8 kB
  • 5. PyTorch Computer Vision/25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.srt 20.6 kB
  • 2. PyTorch Fundamentals/13. Introduction to PyTorch Tensors.srt 20.6 kB
  • 5. PyTorch Computer Vision/13. Writing an Evaluation Function to Get Our Models Results.srt 20.5 kB
  • 9. PyTorch Experiment Tracking/7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.srt 20.5 kB
  • 8. PyTorch Transfer Learning/12. Freezing the Base Layers of Our Model and Updating the Classifier Head.srt 20.4 kB
  • 10. PyTorch Paper Replicating/44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.srt 20.4 kB
  • 3. PyTorch Workflow/24. Putting Everything Together (Part 3) Training a Model.srt 20.3 kB
  • 11. PyTorch Model Deployment/56. Deploying Food Vision Big to Hugging Face Spaces.srt 20.2 kB
  • 6. PyTorch Custom Datasets/34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.srt 20.1 kB
  • 9. PyTorch Experiment Tracking/19. Viewing Our Modelling Experiments in TensorBoard.srt 20.1 kB
  • 9. PyTorch Experiment Tracking/17. Coding Out the Steps to Run a Series of Modelling Experiments.srt 20.1 kB
  • 3. PyTorch Workflow/17. Writing Testing Loop Code and Discussing What's Happening Step by Step.srt 20.0 kB
  • 8. PyTorch Transfer Learning/6. Turning Our Data into DataLoaders with Manually Created Transforms.srt 19.8 kB
  • 5. PyTorch Computer Vision/8. Turning Our Datasets Into DataLoaders.srt 19.8 kB
  • 6. PyTorch Custom Datasets/16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.srt 19.8 kB
  • 6. PyTorch Custom Datasets/3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.srt 19.6 kB
  • 4. PyTorch Neural Network Classification/16. Writing Training and Testing Code to See if Our Upgraded Model Performs Better.srt 19.6 kB
  • 7. PyTorch Going Modular/4. Writing the Outline for Our First Python Script to Setup the Data.srt 19.4 kB
  • 11. PyTorch Model Deployment/32. Bringing Food Vision Mini to Life in a Live Web Application.srt 19.2 kB
  • 11. PyTorch Model Deployment/39. Turning Our Food Vision Mini Demo App Into a Python Script.srt 19.2 kB
  • 10. PyTorch Paper Replicating/19. Creating Patch Embeddings with a Convolutional Layer.srt 19.1 kB
  • 10. PyTorch Paper Replicating/27. Equation 1 Putting it All Together.srt 18.9 kB
  • 8. PyTorch Transfer Learning/7. Turning Our Data into DataLoaders with Automatic Created Transforms.srt 18.9 kB
  • 6. PyTorch Custom Datasets/36. Predicting on Custom Data (Part 5) Putting It All Together.srt 18.8 kB
  • 3. PyTorch Workflow/6. Creating Our First PyTorch Model for Linear Regression.srt 18.7 kB
  • 4. PyTorch Neural Network Classification/26. Creating a Multi-Class Classification Model with PyTorch.srt 18.7 kB
  • 11. PyTorch Model Deployment/47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.srt 18.5 kB
  • 7. PyTorch Going Modular/1. What Is Going Modular and What We Are Going to Cover.srt 18.5 kB
  • 4. PyTorch Neural Network Classification/4. Making a Toy Classification Dataset.srt 18.4 kB
  • 10. PyTorch Paper Replicating/20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.srt 18.4 kB
  • 4. PyTorch Neural Network Classification/25. Putting It All Together (Part 1) Building a Multiclass Dataset.srt 18.3 kB
  • 5. PyTorch Computer Vision/19. Training and Testing Model 1 with Our Training and Testing Functions.srt 18.3 kB
  • 4. PyTorch Neural Network Classification/5. Turning Our Data into Tensors and Making a Training and Test Split.srt 18.2 kB
  • 8. PyTorch Transfer Learning/8. Which Pretrained Model Should You Use.srt 18.1 kB
  • 10. PyTorch Paper Replicating/25. Creating the Class Token Embedding - Less Birds.srt 18.1 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/21. Preparing Functions for Experiment 3 and 4.srt 18.1 kB
  • 2. PyTorch Fundamentals/22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.srt 18.0 kB
  • 10. PyTorch Paper Replicating/24. Creating the Class Token Embedding.srt 17.9 kB
  • 6. PyTorch Custom Datasets/23. Creating Training and Testing loop Functions.srt 17.9 kB
  • 6. PyTorch Custom Datasets/5. Becoming One With the Data (Part 2) Visualizing a Random Image.srt 17.6 kB
  • 5. PyTorch Computer Vision/33. Saving and Loading Our Best Performing Model.srt 17.6 kB
  • 10. PyTorch Paper Replicating/10. Breaking Down Figure 1 of the ViT Paper.srt 17.4 kB
  • 2. PyTorch Fundamentals/26. Squeezing, Unsqueezing and Permuting Tensors.srt 17.2 kB
  • 4. PyTorch Neural Network Classification/28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.srt 17.1 kB
  • 10. PyTorch Paper Replicating/26. Creating the Position Embedding.srt 17.1 kB
  • 9. PyTorch Experiment Tracking/9. Exploring Our Single Models Results with TensorBoard.srt 17.1 kB
  • 6. PyTorch Custom Datasets/8. Transforming Data (Part 2) Visualizing Transformed Images.srt 17.1 kB
  • 8. PyTorch Transfer Learning/9. Setting Up a Pretrained Model with Torchvision.srt 17.0 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/22. Experiment 3 - Training a Non-Compiled Model for Multiple Runs.srt 17.0 kB
  • 5. PyTorch Computer Vision/2. Computer Vision Input and Output Shapes.srt 16.9 kB
  • 10. PyTorch Paper Replicating/18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).srt 16.6 kB
  • 10. PyTorch Paper Replicating/40. Creating a Loss Function and Optimizer from the ViT Paper.srt 16.6 kB
  • 10. PyTorch Paper Replicating/11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.srt 16.6 kB
  • 5. PyTorch Computer Vision/29. Making Predictions on Random Test Samples with the Best Trained Model.srt 16.6 kB
  • 11. PyTorch Model Deployment/41. Downloading Our Food Vision Mini App Files from Google Colab.srt 16.5 kB
  • 3. PyTorch Workflow/10. Making Predictions With Our Random Model Using Inference Mode.srt 16.4 kB
  • 4. PyTorch Neural Network Classification/1. Introduction to Machine Learning Classification With PyTorch.srt 16.3 kB
  • 7. PyTorch Going Modular/5. Creating a Python Script to Create Our PyTorch DataLoaders.srt 16.3 kB
  • 4. PyTorch Neural Network Classification/7. Coding a Small Neural Network to Handle Our Classification Data.srt 16.2 kB
  • 6. PyTorch Custom Datasets/31. Plotting the Loss Curves of All of Our Models Against Each Other.srt 16.2 kB
  • 8. PyTorch Transfer Learning/1. Introduction What is Transfer Learning and Why Use It.srt 16.1 kB
  • 4. PyTorch Neural Network Classification/18. Building and Training a Model to Fit on Straight Line Data.srt 16.1 kB
  • 4. PyTorch Neural Network Classification/20. Introducing the Missing Piece for Our Classification Model Non-Linearity.srt 16.1 kB
  • 9. PyTorch Experiment Tracking/6. Preparing a Pretrained Model for Our Own Problem.srt 16.0 kB
  • 2. PyTorch Fundamentals/7. What Is and Why PyTorch.srt 16.0 kB
  • 6. PyTorch Custom Datasets/20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.srt 16.0 kB
  • 6. PyTorch Custom Datasets/24. Creating a Train Function to Train and Evaluate Our Models.srt 16.0 kB
  • 3. PyTorch Workflow/16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.srt 16.0 kB
  • 4. PyTorch Neural Network Classification/21. Building Our First Neural Network with Non-Linearity.srt 15.9 kB
  • 5. PyTorch Computer Vision/6. Visualizing Random Samples of Data.srt 15.9 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/19. Comparing the Results of Experiment 1 and 2.srt 15.8 kB
  • 5. PyTorch Computer Vision/10. Creating a Loss Function an Optimizer for Model 0.srt 15.7 kB
  • 10. PyTorch Paper Replicating/15. Breaking Down Table 1.srt 15.5 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/18. Experiment 2 - Single Run with torch.compile.srt 15.5 kB
  • 6. PyTorch Custom Datasets/28. Creating Augmented Training Datasets and DataLoaders for Model 1.srt 15.4 kB
  • 6. PyTorch Custom Datasets/1. What Is a Custom Dataset and What We Are Going to Cover.srt 15.3 kB
  • 2. PyTorch Fundamentals/29. PyTorch Reproducibility (Taking the Random Out of Random).srt 15.3 kB
  • 10. PyTorch Paper Replicating/33. Turning Equation 3 into Code.srt 15.2 kB
  • 10. PyTorch Paper Replicating/38. Bringing Our Own Vision Transformer to Life - Part 2 The Forward Method.srt 15.2 kB
  • 10. PyTorch Paper Replicating/13. Breaking Down Equation 2 and 3.srt 15.2 kB
  • 9. PyTorch Experiment Tracking/20. Loading the Best Model and Making Predictions on Random Images from the Test Set.srt 15.2 kB
  • 3. PyTorch Workflow/9. Checking Out the Internals of Our PyTorch Model.srt 15.1 kB
  • 5. PyTorch Computer Vision/4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.srt 15.0 kB
  • 4. PyTorch Neural Network Classification/24. Replicating Non-Linear Activation Functions with Pure PyTorch.srt 15.0 kB
  • 6. PyTorch Custom Datasets/25. Training and Evaluating Model 0 With Our Training Functions.srt 15.0 kB
  • 11. PyTorch Model Deployment/54. Creating an App Script for Our Food Vision Big Model Gradio Demo.srt 15.0 kB
  • 9. PyTorch Experiment Tracking/3. Creating a Function to Download Data.srt 14.9 kB
  • 4. PyTorch Neural Network Classification/2. Classification Problem Example Input and Output Shapes.srt 14.9 kB
  • 2. PyTorch Fundamentals/30. Different Ways of Accessing a GPU in PyTorch.srt 14.9 kB
  • 2. PyTorch Fundamentals/4. Anatomy of Neural Networks.srt 14.9 kB
  • 11. PyTorch Model Deployment/26. Comparing EffNetB2 and ViT Model Statistics.srt 14.7 kB
  • 2. PyTorch Fundamentals/14. Creating Random Tensors in PyTorch.srt 14.7 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/10. Creating a Function to Setup Our Model and Transforms.srt 14.7 kB
  • 11. PyTorch Model Deployment/1. What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.srt 14.6 kB
  • 9. PyTorch Experiment Tracking/10. Creating a Function to Create SummaryWriter Instances.srt 14.6 kB
  • 8. PyTorch Transfer Learning/16. Creating a Function Predict On and Plot Images.srt 14.5 kB
  • 11. PyTorch Model Deployment/22. Outlining the Steps for Making and Timing Predictions for Our Models.srt 14.3 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/23. Experiment 4 - Training a Compiled Model for Multiple Runs.srt 14.3 kB
  • 11. PyTorch Model Deployment/13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.srt 14.3 kB
  • 3. PyTorch Workflow/3. Creating a Simple Dataset Using the Linear Regression Formula.srt 14.3 kB
  • 3. PyTorch Workflow/26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.srt 14.2 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/7. Setting the Default Device in PyTorch 2.srt 14.2 kB
  • 11. PyTorch Model Deployment/30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.srt 14.1 kB
  • 10. PyTorch Paper Replicating/7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.srt 14.0 kB
  • 4. PyTorch Neural Network Classification/31. Discussing a Few More Classification Metrics.srt 14.0 kB
  • 11. PyTorch Model Deployment/24. Making and Timing Predictions with EffNetB2.srt 14.0 kB
  • 3. PyTorch Workflow/23. Putting Everything Together (Part 2) Building a Model.srt 13.9 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/13. Getting More Potential Speedups with TensorFloat-32.srt 13.9 kB
  • 10. PyTorch Paper Replicating/9. Replicating a Vision Transformer - High Level Overview.srt 13.9 kB
  • 5. PyTorch Computer Vision/15. Model 1 Creating a Model with Non-Linear Functions.srt 13.9 kB
  • 3. PyTorch Workflow/14. Writing Code for a PyTorch Training Loop.srt 13.8 kB
  • 11. PyTorch Model Deployment/45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.srt 13.8 kB
  • 7. PyTorch Going Modular/6. Turning Our Model Building Code into a Python Script.srt 13.7 kB
  • 10. PyTorch Paper Replicating/3. Where Can You Find Machine Learning Research Papers and Code.srt 13.6 kB
  • 5. PyTorch Computer Vision/21. Model 2 Convolutional Neural Networks High Level Overview.srt 13.6 kB
  • 6. PyTorch Custom Datasets/9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.srt 13.6 kB
  • 10. PyTorch Paper Replicating/21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.srt 13.5 kB
  • 4. PyTorch Neural Network Classification/14. Discussing Options to Improve a Model.srt 13.5 kB
  • 4. PyTorch Neural Network Classification/30. Making Predictions with and Evaluating Our Multi-Class Classification Model.srt 13.5 kB
  • 10. PyTorch Paper Replicating/4. What We Are Going to Cover.srt 13.4 kB
  • 2. PyTorch Fundamentals/27. Selecting Data From Tensors (Indexing).srt 13.4 kB
  • 11. PyTorch Model Deployment/10. Creating an EffNetB2 Feature Extractor Model.srt 13.4 kB
  • 11. PyTorch Model Deployment/28. Gradio Overview and Installation.srt 13.4 kB
  • 11. PyTorch Model Deployment/36. Creating an Examples Directory with Example Food Vision Mini Images.srt 13.2 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/17. Experiment 1 - Single Run without torch.compile.srt 13.1 kB
  • 10. PyTorch Paper Replicating/29. Equation 2 Layernorm Overview.srt 13.1 kB
  • 2. PyTorch Fundamentals/20. Matrix Multiplication (Part 1).srt 13.0 kB
  • 10. PyTorch Paper Replicating/35. Combining equation 2 and 3 to Create the Transformer Encoder.srt 13.0 kB
  • 2. PyTorch Fundamentals/17. Dealing With Tensor Data Types.srt 13.0 kB
  • 10. PyTorch Paper Replicating/42. Discussing what Our Training Setup Is Missing.srt 13.0 kB
  • 3. PyTorch Workflow/20. Writing Code to Load a PyTorch Model.srt 12.9 kB
  • 11. PyTorch Model Deployment/43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.srt 12.8 kB
  • 3. PyTorch Workflow/11. Training a Model Intuition (The Things We Need).srt 12.8 kB
  • 6. PyTorch Custom Datasets/26. Plotting the Loss Curves of Model 0.srt 12.8 kB
  • 9. PyTorch Experiment Tracking/2. Getting Setup by Importing Torch Libraries and Going Modular Code.srt 12.7 kB
  • 9. PyTorch Experiment Tracking/4. Turning Our Data into DataLoaders Using Manual Transforms.srt 12.6 kB
  • 6. PyTorch Custom Datasets/11. Turning Our Image Datasets into PyTorch Dataloaders.srt 12.6 kB
  • 4. PyTorch Neural Network Classification/15. Creating a New Model with More Layers and Hidden Units.srt 12.6 kB
  • 11. PyTorch Model Deployment/4. How Is My Model Going to Function.srt 12.5 kB
  • 5. PyTorch Computer Vision/30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.srt 12.5 kB
  • 3. PyTorch Workflow/5. Building a function to Visualize Our Data.srt 12.5 kB
  • 10. PyTorch Paper Replicating/32. Equation 3 Replication Overview.srt 12.5 kB
  • 6. PyTorch Custom Datasets/4. Becoming One With the Data (Part 1) Exploring the Data Format.srt 12.4 kB
  • 5. PyTorch Computer Vision/17. Turing Our Training Loop into a Function.srt 12.4 kB
  • 6. PyTorch Custom Datasets/21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.srt 12.3 kB
  • 10. PyTorch Paper Replicating/12. Breaking Down Equation 1.srt 12.2 kB
  • 3. PyTorch Workflow/4. Splitting Our Data Into Training and Test Sets.srt 12.2 kB
  • 10. PyTorch Paper Replicating/5. Getting Setup for Coding in Google Colab.srt 12.2 kB
  • 6. PyTorch Custom Datasets/13. Creating a Helper Function to Get Class Names From a Directory.srt 12.2 kB
  • 4. PyTorch Neural Network Classification/17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.srt 12.1 kB
  • 2. PyTorch Fundamentals/28. PyTorch Tensors and NumPy.srt 12.1 kB
  • 5. PyTorch Computer Vision/27. Model 2 Training Our First CNN and Evaluating Its Results.srt 12.1 kB
  • 2. PyTorch Fundamentals/12. Getting Setup to Write PyTorch Code.srt 12.0 kB
  • 10. PyTorch Paper Replicating/1. What Is a Machine Learning Research Paper.srt 12.0 kB
  • 6. PyTorch Custom Datasets/7. Transforming Data (Part 1) Turning Images Into Tensors.srt 12.0 kB
  • 2. PyTorch Fundamentals/18. Getting Tensor Attributes.srt 11.9 kB
  • 6. PyTorch Custom Datasets/19. Building a Baseline Model (Part 1) Loading and Transforming Data.srt 11.9 kB
  • 11. PyTorch Model Deployment/2. Three Questions to Ask for Machine Learning Model Deployment.srt 11.9 kB
  • 8. PyTorch Transfer Learning/13. Training Our First Transfer Learning Feature Extractor Model.srt 11.9 kB
  • 11. PyTorch Model Deployment/29. Gradio Function Outline.srt 11.8 kB
  • 7. PyTorch Going Modular/2. Going Modular Notebook (Part 1) Running It End to End.srt 11.8 kB
  • 2. PyTorch Fundamentals/21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.srt 11.7 kB
  • 3. PyTorch Workflow/2. Getting Setup and What We Are Covering.srt 11.6 kB
  • 9. PyTorch Experiment Tracking/15. Turning Our Datasets into DataLoaders Ready for Experimentation.srt 11.6 kB
  • 9. PyTorch Experiment Tracking/1. What Is Experiment Tracking and Why Track Experiments.srt 11.5 kB
  • 8. PyTorch Transfer Learning/5. Downloading Pizza, Steak, Sushi Image Data from Github.srt 11.5 kB
  • 8. PyTorch Transfer Learning/3. Installing the Latest Versions of Torch and Torchvision.srt 11.4 kB
  • 2. PyTorch Fundamentals/6. What Can Deep Learning Be Used For.srt 11.4 kB
  • 9. PyTorch Experiment Tracking/5. Turning Our Data into DataLoaders Using Automatic Transforms.srt 11.4 kB
  • 11. PyTorch Model Deployment/46. Downloading the Food 101 Dataset.srt 11.3 kB
  • 4. PyTorch Neural Network Classification/8. Making Our Neural Network Visual.srt 11.3 kB
  • 11. PyTorch Model Deployment/34. Outlining the File Structure of Our Deployed App.srt 11.3 kB
  • 5. PyTorch Computer Vision/28. Comparing the Results of Our Modelling Experiments.srt 11.3 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/15. Creating Training and Test DataLoaders.srt 11.2 kB
  • 10. PyTorch Paper Replicating/39. Getting a Visual Summary of Our Custom Vision Transformer.srt 11.1 kB
  • 11. PyTorch Model Deployment/9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.srt 11.1 kB
  • 10. PyTorch Paper Replicating/34. Transformer Encoder Overview.srt 11.1 kB
  • 8. PyTorch Transfer Learning/10. Different Kinds of Transfer Learning.srt 11.0 kB
  • 8. PyTorch Transfer Learning/17. Making and Plotting Predictions on Test Images.srt 11.0 kB
  • 6. PyTorch Custom Datasets/33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.srt 11.0 kB
  • 10. PyTorch Paper Replicating/50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.srt 10.9 kB
  • 2. PyTorch Fundamentals/9. What We Are Going To Cover With PyTorch.srt 10.9 kB
  • 11. PyTorch Model Deployment/17. Creating a Vision Transformer Feature Extractor Model.srt 10.7 kB
  • 8. PyTorch Transfer Learning/15. Outlining the Steps to Make Predictions on the Test Images.srt 10.7 kB
  • 2. PyTorch Fundamentals/31. Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.srt 10.7 kB
  • 5. PyTorch Computer Vision/7. DataLoader Overview Understanding Mini-Batches.srt 10.7 kB
  • 6. PyTorch Custom Datasets/12. Creating a Custom Dataset Class in PyTorch High Level Overview.srt 10.6 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/11. Discussing How to Get Better Relative Speedups for Training Models.srt 10.6 kB
  • 10. PyTorch Paper Replicating/46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.srt 10.6 kB
  • 8. PyTorch Transfer Learning/4. Downloading Our Previously Written Code from Going Modular.srt 10.5 kB
  • 6. PyTorch Custom Datasets/10. Visualizing a Loaded Image From the Train Dataset.srt 10.5 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/14. Downloading the CIFAR10 Dataset.srt 10.5 kB
  • 4. PyTorch Neural Network Classification/27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.srt 10.4 kB
  • 10. PyTorch Paper Replicating/14. Breaking Down Equation 4.srt 10.4 kB
  • 11. PyTorch Model Deployment/37. Writing Code to Move Our Saved EffNetB2 Model File.srt 10.3 kB
  • 8. PyTorch Transfer Learning/11. Getting a Summary of the Different Layers of Our Model.srt 10.3 kB
  • 4. PyTorch Neural Network Classification/3. Typical Architecture of a Classification Neural Network (Overview).srt 10.2 kB
  • 5. PyTorch Computer Vision/32. Evaluating Our Best Models Predictions with a Confusion Matrix.srt 10.2 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/12. Setting the Batch Size and Data Size Programmatically.srt 10.2 kB
  • 6. PyTorch Custom Datasets/15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.srt 10.0 kB
  • 6. PyTorch Custom Datasets/17. Turning Our Custom Datasets Into DataLoaders.srt 9.9 kB
  • 2. PyTorch Fundamentals/3. Machine Learning vs. Deep Learning.srt 9.9 kB
  • 11. PyTorch Model Deployment/25. Making and Timing Predictions with ViT.srt 9.9 kB
  • 5. PyTorch Computer Vision/18. Turing Our Testing Loop into a Function.srt 9.9 kB
  • 11. PyTorch Model Deployment/48. Turning Our Food 101 Datasets into DataLoaders.srt 9.8 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/8. Discussing the Experiments We Are Going to Run for PyTorch 2.srt 9.8 kB
  • 2. PyTorch Fundamentals/2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.srt 9.7 kB
  • 6. PyTorch Custom Datasets/29. Constructing and Training Model 1.srt 9.7 kB
  • 6. PyTorch Custom Datasets/22. Using the Torchinfo Package to Get a Summary of Our Model.srt 9.7 kB
  • 11. PyTorch Model Deployment/57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.srt 9.7 kB
  • 3. PyTorch Workflow/21. Setting Up to Practice Everything We Have Done Using Device Agnostic code.srt 9.7 kB
  • 11. PyTorch Model Deployment/19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.srt 9.7 kB
  • 5. PyTorch Computer Vision/34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.srt 9.6 kB
  • 8. PyTorch Transfer Learning/18. Making a Prediction on a Custom Image.srt 9.6 kB
  • 8. PyTorch Transfer Learning/14. Plotting the Loss curves of Our Transfer Learning Model.srt 9.6 kB
  • 11. PyTorch Model Deployment/52. Saving Food 101 Class Names to a Text File and Reading them Back In.srt 9.6 kB
  • 3. PyTorch Workflow/22. Putting Everything Together (Part 1) Data.srt 9.5 kB
  • 6. PyTorch Custom Datasets/37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.srt 9.5 kB
  • 7. PyTorch Going Modular/8. Turning Our Utility Function to Save a Model into a Python Script.srt 9.3 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/6. Getting Info from Our GPUs and Seeing if They're Capable of Using PyTorch 2.srt 9.1 kB
  • 9. PyTorch Experiment Tracking/14. Downloading Datasets for Our Modelling Experiments.srt 9.1 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/25. Potential Extensions and Resources to Learn More.srt 9.1 kB
  • 11. PyTorch Model Deployment/16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.srt 9.1 kB
  • 7. PyTorch Going Modular/10. Going Modular Summary, Exercises and Extra-Curriculum.srt 9.1 kB
  • 11. PyTorch Model Deployment/5. Some Tools and Places to Deploy Machine Learning Models.srt 9.1 kB
  • 11. PyTorch Model Deployment/11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.srt 9.0 kB
  • 11. PyTorch Model Deployment/7. Getting Setup to Code.srt 9.0 kB
  • 3. PyTorch Workflow/7. Breaking Down What's Happening in Our PyTorch Linear regression Model.srt 9.0 kB
  • 2. PyTorch Fundamentals/11. Important Resources For This Course.srt 8.9 kB
  • 10. PyTorch Paper Replicating/43. Plotting a Loss Curve for Our ViT Model.srt 8.9 kB
  • 3. PyTorch Workflow/8. Discussing Some of the Most Important PyTorch Model Building Classes.srt 8.9 kB
  • 4. PyTorch Neural Network Classification/23. Making Predictions with and Evaluating Our First Non-Linear Model.srt 8.9 kB
  • 7. PyTorch Going Modular/7. Turning Our Model Training Code into a Python Script.srt 8.9 kB
  • 4. PyTorch Neural Network Classification/19. Evaluating Our Models Predictions on Straight Line Data.srt 8.8 kB
  • 11. PyTorch Model Deployment/33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.srt 8.8 kB
  • 1. Introduction/2. Course Welcome and What Is Deep Learning.srt 8.8 kB
  • 2. PyTorch Fundamentals/10. How To and How Not To Approach This Course.srt 8.8 kB
  • 9. PyTorch Experiment Tracking/12. What Experiments Should You Try.srt 8.7 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/1. Introduction to PyTorch 2.0.srt 8.7 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/9. Introduction to PyTorch 2.srt 8.7 kB
  • 11. PyTorch Model Deployment/21. Collecting Stats About Our-ViT Feature Extractor.srt 8.7 kB
  • 2. PyTorch Fundamentals/23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).srt 8.6 kB
  • 8. PyTorch Transfer Learning/2. Where Can You Find Pretrained Models and What We Are Going to Cover.srt 8.5 kB
  • 11. PyTorch Model Deployment/50. Outlining the File Structure for Our Food Vision Big.srt 8.4 kB
  • 2. PyTorch Fundamentals/19. Manipulating Tensors (Tensor Operations).srt 8.4 kB
  • 9. PyTorch Experiment Tracking/13. Discussing the Experiments We Are Going to Try.srt 8.3 kB
  • 3. PyTorch Workflow/25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.srt 8.3 kB
  • 5. PyTorch Computer Vision/3. What Is a Convolutional Neural Network (CNN).srt 8.3 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/24. Comparing the Results of Experiment 3 and 4.srt 8.3 kB
  • 5. PyTorch Computer Vision/11. Creating a Function to Time Our Modelling Code.srt 8.3 kB
  • 6. PyTorch Custom Datasets/2. Importing PyTorch and Setting Up Device Agnostic Code.srt 8.0 kB
  • 10. PyTorch Paper Replicating/31. Checking the Inputs and Outputs of Equation.srt 8.0 kB
  • 6. PyTorch Custom Datasets/32. Predicting on Custom Data (Part 1) Downloading an Image.srt 7.9 kB
  • 2. PyTorch Fundamentals/32. PyTorch Fundamentals Exercises and Extra-Curriculum.srt 7.6 kB
  • 10. PyTorch Paper Replicating/45. Preparing Data to Be Used with a Pretrained ViT.srt 7.4 kB
  • 11. PyTorch Model Deployment/6. What We Are Going to Cover.srt 7.4 kB
  • 7. PyTorch Going Modular/3. Downloading a Dataset.srt 7.3 kB
  • 2. PyTorch Fundamentals/16. Creating a Tensor Range and Tensors Like Other Tensors.srt 7.3 kB
  • 11. PyTorch Model Deployment/15. Getting the Size of Our EffNetB2 Model in Megabytes.srt 7.3 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/16. Preparing Training and Testing Loops with Timing Steps for PyTorch 2.0 timing.srt 7.2 kB
  • 6. PyTorch Custom Datasets/6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.srt 7.2 kB
  • 10. PyTorch Paper Replicating/41. Training our Custom ViT on Food Vision Mini.srt 7.2 kB
  • 10. PyTorch Paper Replicating/22. Visualizing a Single Sequence Vector of Patch Embeddings.srt 7.1 kB
  • 11. PyTorch Model Deployment/31. Creating a List of Examples to Pass to Our Gradio Demo.srt 7.0 kB
  • 2. PyTorch Fundamentals/5. Different Types of Learning Paradigms.srt 7.0 kB
  • 2. PyTorch Fundamentals/8. What Are Tensors.srt 6.9 kB
  • 9. PyTorch Experiment Tracking/8. Training a Single Model and Saving the Results to TensorBoard.srt 6.9 kB
  • 11. PyTorch Model Deployment/20. Saving Our ViT Feature Extractor and Inspecting Its Size.srt 6.9 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/20. Saving the Results of Experiment 1 and 2.srt 6.8 kB
  • 9. PyTorch Experiment Tracking/11. Adapting Our Train Function to Be Able to Track Multiple Experiments.srt 6.8 kB
  • 9. PyTorch Experiment Tracking/22. Main Takeaways, Exercises and Extra- Curriculum.srt 6.7 kB
  • 4. PyTorch Neural Network Classification/6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.srt 6.7 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/3. Getting Started with PyTorch 2 in Google Colab.srt 6.6 kB
  • 10. PyTorch Paper Replicating/47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.srt 6.5 kB
  • 3. PyTorch Workflow/28. PyTorch Workflow Exercises and Extra-Curriculum.srt 6.5 kB
  • 1. Introduction/6. ZTM Resources.srt 6.5 kB
  • 9. PyTorch Experiment Tracking/18. Running Eight Different Modelling Experiments in 5 Minutes.srt 6.4 kB
  • 2. PyTorch Fundamentals/1. Why Use Machine Learning or Deep Learning.srt 6.4 kB
  • 11. PyTorch Model Deployment/40. Creating a Requirements File for Our Food Vision Mini App.srt 6.4 kB
  • 10. PyTorch Paper Replicating/6. Downloading Data for Food Vision Mini.srt 6.3 kB
  • 5. PyTorch Computer Vision/20. Getting a Results Dictionary for Model 1.srt 6.3 kB
  • 5. PyTorch Computer Vision/14. Setup Device-Agnostic Code for Running Experiments on the GPU.srt 6.2 kB
  • 1. Introduction/3. Join Our Online Classroom!.srt 6.1 kB
  • 6. PyTorch Custom Datasets/35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.srt 6.1 kB
  • 9. PyTorch Experiment Tracking/21. Making a Prediction on Our Own Custom Image with the Best Model.srt 6.0 kB
  • 11. PyTorch Model Deployment/35. Creating a Food Vision Mini Demo Directory to House Our App Files.srt 5.8 kB
  • 11. PyTorch Model Deployment/44. Food Vision Big Project Outline.srt 5.7 kB
  • 10. PyTorch Paper Replicating/48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.srt 5.6 kB
  • 10. PyTorch Paper Replicating/8. Visualizing a Single Image.srt 5.5 kB
  • 11. PyTorch Model Deployment/38. Turning Our EffNetB2 Model Creation Function Into a Python Script.srt 5.5 kB
  • 11. PyTorch Model Deployment/55. Zipping and Downloading Our Food Vision Big App Files.srt 5.4 kB
  • 8. PyTorch Transfer Learning/19. Main Takeaways, Exercises and Extra- Curriculum.srt 5.3 kB
  • 11. PyTorch Model Deployment/51. Downloading an Example Image and Moving Our Food Vision Big Model File.srt 5.3 kB
  • 1. Introduction/1. PyTorch for Deep Learning.srt 5.3 kB
  • 3. PyTorch Workflow/1. Introduction and Where You Can Get Help.srt 5.3 kB
  • 6. PyTorch Custom Datasets/30. Plotting the Loss Curves of Model 1.srt 5.3 kB
  • 10. PyTorch Paper Replicating/49. Making Predictions on a Custom Image with Our Pretrained ViT.srt 5.2 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/4. PyTorch 2.0 - 30 Second Intro.srt 5.0 kB
  • 10. PyTorch Paper Replicating/2. Why Replicate a Machine Learning Research Paper.srt 5.0 kB
  • 11. PyTorch Model Deployment/8. Downloading a Dataset for Food Vision Mini.srt 5.0 kB
  • 11. PyTorch Model Deployment/12. Creating DataLoaders for EffNetB2.srt 4.9 kB
  • 5. PyTorch Computer Vision/16. Mode 1 Creating a Loss Function and Optimizer.srt 4.7 kB
  • 2. PyTorch Fundamentals/15. Creating Tensors With Zeros and Ones in PyTorch.srt 4.6 kB
  • 3. PyTorch Workflow/27. Exercise Imposter Syndrome.srt 4.6 kB
  • 4. PyTorch Neural Network Classification/32. PyTorch Classification Exercises and Extra-Curriculum.srt 4.5 kB
  • 11. PyTorch Model Deployment/14. Saving Our EffNetB2 Model to File.srt 4.4 kB
  • 2. PyTorch Fundamentals/24. Finding The Positional Min and Max of Tensors.srt 4.0 kB
  • 1. Introduction/4. Exercise Meet Your Classmates + Instructor.html 3.9 kB
  • 11. PyTorch Model Deployment/18. Creating DataLoaders for Our ViT Feature Extractor Model.srt 3.9 kB
  • 5. PyTorch Computer Vision/26. Model 2 Setting Up a Loss Function and Optimizer.srt 3.7 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/5. Getting Setup for PyTorch 2.srt 3.4 kB
  • 11. PyTorch Model Deployment/53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.srt 3.3 kB
  • 1. Introduction/5. Free Course Book + Code Resources + Asking Questions + Getting Help.html 2.4 kB
  • 12. Introduction to PyTorch 2.0 and torch.compile/2. What We Are Going to Cover and PyTorch 2 Reference Materials.srt 2.4 kB
  • 1. Introduction/7. Machine Learning + Python Monthly Newsletters.html 2.0 kB
  • 14. Where To Go From Here/1. Thank You!.srt 1.9 kB
  • 14. Where To Go From Here/3. Endorsements on LinkedIn.html 1.4 kB
  • 13. Bonus Section/1. Special Bonus Lecture.html 1.3 kB
  • 14. Where To Go From Here/2. Become An Alumni.html 921 Bytes
  • 14. Where To Go From Here/4. Learning Guideline.html 353 Bytes
  • 0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 12. Introduction to PyTorch 2.0 and torch.compile/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 5. PyTorch Computer Vision/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 7. PyTorch Going Modular/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 9. PyTorch Experiment Tracking/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 12. Introduction to PyTorch 2.0 and torch.compile/0. Websites you may like/[CourseClub.Me].url 122 Bytes
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  • 7. PyTorch Going Modular/0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 9. PyTorch Experiment Tracking/0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 12. Introduction to PyTorch 2.0 and torch.compile/2.1 PyTorch 2.0 tutorial on learnpytorch.io.html 105 Bytes
  • 12. Introduction to PyTorch 2.0 and torch.compile/3.1 PyTorch 2.0 tutorial on learnpytorch.io.html 105 Bytes
  • 1. Introduction/6.1 LinkedIn Group.html 102 Bytes
  • 1. Introduction/6.3 ZTM Youtube.html 99 Bytes
  • 1. Introduction/6.2 zerotomastery.io.html 86 Bytes
  • 0. Websites you may like/[GigaCourse.Com].url 49 Bytes
  • 12. Introduction to PyTorch 2.0 and torch.compile/0. Websites you may like/[GigaCourse.Com].url 49 Bytes
  • 5. PyTorch Computer Vision/0. Websites you may like/[GigaCourse.Com].url 49 Bytes
  • 7. PyTorch Going Modular/0. Websites you may like/[GigaCourse.Com].url 49 Bytes
  • 9. PyTorch Experiment Tracking/0. Websites you may like/[GigaCourse.Com].url 49 Bytes

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