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种子名称： GetFreeCourses.Co-Udemy-PyTorch for Deep Learning in 2023 Zero to Mastery
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文件数目： 325个文件
文件大小： 27.81 GB
收录时间： 2023-7-8 14:33
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资源热度： 150
最近下载： 2024-10-29 06:45

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GetFreeCourses.Co-Udemy-PyTorch for Deep Learning in 2023 Zero to Mastery.torrent

1. Introduction/1. PyTorch for Deep Learning.mp475.35MB

1. Introduction/2. Course Welcome and What Is Deep Learning.mp438.99MB

1. Introduction/3. Join Our Online Classroom!.mp475.35MB

10. PyTorch Paper Replicating/1. What Is a Machine Learning Research Paper.mp493.94MB

10. PyTorch Paper Replicating/10. Breaking Down Figure 1 of the ViT Paper.mp487.12MB

10. PyTorch Paper Replicating/11. Breaking Down the Four Equations Overview and a Trick for Reading Papers.mp4140.93MB

10. PyTorch Paper Replicating/12. Breaking Down Equation 1.mp4103.22MB

10. PyTorch Paper Replicating/13. Breaking Down Equation 2 and 3.mp4125.04MB

10. PyTorch Paper Replicating/14. Breaking Down Equation 4.mp492.44MB

10. PyTorch Paper Replicating/15. Breaking Down Table 1.mp4122.08MB

10. PyTorch Paper Replicating/16. Calculating the Input and Output Shape of the Embedding Layer by Hand.mp4160.6MB

10. PyTorch Paper Replicating/17. Turning a Single Image into Patches (Part 1 Patching the Top Row).mp4150.16MB

10. PyTorch Paper Replicating/18. Turning a Single Image into Patches (Part 2 Patching the Entire Image).mp4130.64MB

10. PyTorch Paper Replicating/19. Creating Patch Embeddings with a Convolutional Layer.mp4142.63MB

10. PyTorch Paper Replicating/2. Why Replicate a Machine Learning Research Paper.mp423.26MB

10. PyTorch Paper Replicating/20. Exploring the Outputs of Our Convolutional Patch Embedding Layer.mp4129.06MB

10. PyTorch Paper Replicating/21. Flattening Our Convolutional Feature Maps into a Sequence of Patch Embeddings.mp489.61MB

10. PyTorch Paper Replicating/22. Visualizing a Single Sequence Vector of Patch Embeddings.mp450.37MB

10. PyTorch Paper Replicating/23. Creating the Patch Embedding Layer with PyTorch.mp4170.03MB

10. PyTorch Paper Replicating/24. Creating the Class Token Embedding.mp4131.99MB

10. PyTorch Paper Replicating/25. Creating the Class Token Embedding - Less Birds.mp4131.91MB

10. PyTorch Paper Replicating/26. Creating the Position Embedding.mp4109.18MB

10. PyTorch Paper Replicating/27. Equation 1 Putting it All Together.mp4134.82MB

10. PyTorch Paper Replicating/28. Equation 2 Multihead Attention Overview.mp4144.11MB

10. PyTorch Paper Replicating/29. Equation 2 Layernorm Overview.mp4111.76MB

10. PyTorch Paper Replicating/3. Where Can You Find Machine Learning Research Papers and Code.mp4110.75MB

10. PyTorch Paper Replicating/30. Turning Equation 2 into Code.mp4163.87MB

10. PyTorch Paper Replicating/31. Checking the Inputs and Outputs of Equation.mp453.69MB

10. PyTorch Paper Replicating/32. Equation 3 Replication Overview.mp488.7MB

10. PyTorch Paper Replicating/33. Turning Equation 3 into Code.mp4107.07MB

10. PyTorch Paper Replicating/34. Transformer Encoder Overview.mp482.85MB

10. PyTorch Paper Replicating/35. Combining equation 2 and 3 to Create the Transformer Encoder.mp484.87MB

10. PyTorch Paper Replicating/36. Creating a Transformer Encoder Layer with In-Built PyTorch Layer.mp4188.75MB

10. PyTorch Paper Replicating/37. Bringing Our Own Vision Transformer to Life - Part 1 Gathering the Pieces.mp4190.82MB

10. PyTorch Paper Replicating/38. Bringing Our Own Vision Transformer to Life - Part 2 The Forward Method.mp4111.37MB

10. PyTorch Paper Replicating/39. Getting a Visual Summary of Our Custom Vision Transformer.mp484.89MB

10. PyTorch Paper Replicating/4. What We Are Going to Cover.mp487.76MB

10. PyTorch Paper Replicating/40. Creating a Loss Function and Optimizer from the ViT Paper.mp4118.33MB

10. PyTorch Paper Replicating/41. Training our Custom ViT on Food Vision Mini.mp453.48MB

10. PyTorch Paper Replicating/42. Discussing what Our Training Setup Is Missing.mp4101.2MB

10. PyTorch Paper Replicating/43. Plotting a Loss Curve for Our ViT Model.mp463.4MB

10. PyTorch Paper Replicating/44. Getting a Pretrained Vision Transformer from Torchvision and Setting it Up.mp4164.75MB

10. PyTorch Paper Replicating/45. Preparing Data to Be Used with a Pretrained ViT.mp457.22MB

10. PyTorch Paper Replicating/46. Training a Pretrained ViT Feature Extractor Model for Food Vision Mini.mp476.29MB

10. PyTorch Paper Replicating/47. Saving Our Pretrained ViT Model to File and Inspecting Its Size.mp440.36MB

10. PyTorch Paper Replicating/48. Discussing the Trade-Offs Between Using a Larger Model for Deployments.mp441.81MB

10. PyTorch Paper Replicating/49. Making Predictions on a Custom Image with Our Pretrained ViT.mp437.11MB

10. PyTorch Paper Replicating/5. Getting Setup for Coding in Google Colab.mp499.14MB

10. PyTorch Paper Replicating/50. PyTorch Paper Replicating Main Takeaways, Exercises and Extra-Curriculum.mp485.49MB

10. PyTorch Paper Replicating/6. Downloading Data for Food Vision Mini.mp443.83MB

10. PyTorch Paper Replicating/7. Turning Our Food Vision Mini Images into PyTorch DataLoaders.mp489.7MB

10. PyTorch Paper Replicating/8. Visualizing a Single Image.mp436.44MB

10. PyTorch Paper Replicating/9. Replicating a Vision Transformer - High Level Overview.mp477.84MB

11. PyTorch Model Deployment/1. What is Machine Learning Model Deployment - Why Deploy a Machine Learning Model.mp473.84MB

11. PyTorch Model Deployment/10. Creating an EffNetB2 Feature Extractor Model.mp492.12MB

11. PyTorch Model Deployment/11. Create a Function to Make an EffNetB2 Feature Extractor Model and Transforms.mp457.6MB

11. PyTorch Model Deployment/12. Creating DataLoaders for EffNetB2.mp431.38MB

11. PyTorch Model Deployment/13. Training Our EffNetB2 Feature Extractor and Inspecting the Loss Curves.mp497.04MB

11. PyTorch Model Deployment/14. Saving Our EffNetB2 Model to File.mp426.71MB

11. PyTorch Model Deployment/15. Getting the Size of Our EffNetB2 Model in Megabytes.mp455.48MB

11. PyTorch Model Deployment/16. Collecting Important Statistics and Performance Metrics for Our EffNetB2 Model.mp463.27MB

11. PyTorch Model Deployment/17. Creating a Vision Transformer Feature Extractor Model.mp478.51MB

11. PyTorch Model Deployment/18. Creating DataLoaders for Our ViT Feature Extractor Model.mp419.7MB

11. PyTorch Model Deployment/19. Training Our ViT Feature Extractor Model and Inspecting Its Loss Curves.mp462MB

11. PyTorch Model Deployment/2. Three Questions to Ask for Machine Learning Model Deployment.mp446.93MB

11. PyTorch Model Deployment/20. Saving Our ViT Feature Extractor and Inspecting Its Size.mp443.77MB

11. PyTorch Model Deployment/21. Collecting Stats About Our-ViT Feature Extractor.mp445.86MB

11. PyTorch Model Deployment/22. Outlining the Steps for Making and Timing Predictions for Our Models.mp493.42MB

11. PyTorch Model Deployment/23. Creating a Function to Make and Time Predictions with Our Models.mp4185.78MB

11. PyTorch Model Deployment/24. Making and Timing Predictions with EffNetB2.mp497.63MB

11. PyTorch Model Deployment/25. Making and Timing Predictions with ViT.mp472.47MB

11. PyTorch Model Deployment/26. Comparing EffNetB2 and ViT Model Statistics.mp489.62MB

11. PyTorch Model Deployment/27. Visualizing the Performance vs Speed Trade-off.mp4134.67MB

11. PyTorch Model Deployment/28. Gradio Overview and Installation.mp495.13MB

11. PyTorch Model Deployment/29. Gradio Function Outline.mp479.9MB

11. PyTorch Model Deployment/3. Where Is My Model Going to Go.mp4139.84MB

11. PyTorch Model Deployment/30. Creating a Predict Function to Map Our Food Vision Mini Inputs to Outputs.mp495.22MB

11. PyTorch Model Deployment/31. Creating a List of Examples to Pass to Our Gradio Demo.mp453.31MB

11. PyTorch Model Deployment/32. Bringing Food Vision Mini to Life in a Live Web Application.mp4135.39MB

11. PyTorch Model Deployment/33. Getting Ready to Deploy Our App Hugging Face Spaces Overview.mp464.81MB

11. PyTorch Model Deployment/34. Outlining the File Structure of Our Deployed App.mp489.54MB

11. PyTorch Model Deployment/35. Creating a Food Vision Mini Demo Directory to House Our App Files.mp439.14MB

11. PyTorch Model Deployment/36. Creating an Examples Directory with Example Food Vision Mini Images.mp492.41MB

11. PyTorch Model Deployment/37. Writing Code to Move Our Saved EffNetB2 Model File.mp471.91MB

11. PyTorch Model Deployment/38. Turning Our EffNetB2 Model Creation Function Into a Python Script.mp444.78MB

11. PyTorch Model Deployment/39. Turning Our Food Vision Mini Demo App Into a Python Script.mp4137.63MB

11. PyTorch Model Deployment/4. How Is My Model Going to Function.mp467.36MB

11. PyTorch Model Deployment/40. Creating a Requirements File for Our Food Vision Mini App.mp437.5MB

11. PyTorch Model Deployment/41. Downloading Our Food Vision Mini App Files from Google Colab.mp4112.22MB

11. PyTorch Model Deployment/42. Uploading Our Food Vision Mini App to Hugging Face Spaces Programmatically.mp4143.59MB

11. PyTorch Model Deployment/43. Running Food Vision Mini on Hugging Face Spaces and Trying it Out.mp491.61MB

11. PyTorch Model Deployment/44. Food Vision Big Project Outline.mp439.15MB

11. PyTorch Model Deployment/45. Preparing an EffNetB2 Feature Extractor Model for Food Vision Big.mp496.53MB

11. PyTorch Model Deployment/46. Downloading the Food 101 Dataset.mp471.67MB

11. PyTorch Model Deployment/47. Creating a Function to Split Our Food 101 Dataset into Smaller Portions.mp4119.74MB

11. PyTorch Model Deployment/48. Turning Our Food 101 Datasets into DataLoaders.mp461.5MB

11. PyTorch Model Deployment/49. Training Food Vision Big Our Biggest Model Yet!.mp4184.22MB

11. PyTorch Model Deployment/5. Some Tools and Places to Deploy Machine Learning Models.mp465.36MB

11. PyTorch Model Deployment/50. Outlining the File Structure for Our Food Vision Big.mp452.78MB

11. PyTorch Model Deployment/51. Downloading an Example Image and Moving Our Food Vision Big Model File.mp436.59MB

11. PyTorch Model Deployment/52. Saving Food 101 Class Names to a Text File and Reading them Back In.mp466.81MB

11. PyTorch Model Deployment/53. Turning Our EffNetB2 Feature Extractor Creation Function into a Python Script.mp423.9MB

11. PyTorch Model Deployment/54. Creating an App Script for Our Food Vision Big Model Gradio Demo.mp4104.81MB

11. PyTorch Model Deployment/55. Zipping and Downloading Our Food Vision Big App Files.mp439.76MB

11. PyTorch Model Deployment/56. Deploying Food Vision Big to Hugging Face Spaces.mp4162.53MB

11. PyTorch Model Deployment/57. PyTorch Mode Deployment Main Takeaways, Extra-Curriculum and Exercises.mp481.75MB

11. PyTorch Model Deployment/6. What We Are Going to Cover.mp440.83MB

11. PyTorch Model Deployment/7. Getting Setup to Code.mp462.88MB

11. PyTorch Model Deployment/8. Downloading a Dataset for Food Vision Mini.mp439.25MB

11. PyTorch Model Deployment/9. Outlining Our Food Vision Mini Deployment Goals and Modelling Experiments.mp458.56MB

12. Where To Go From Here/1. Thank You!.mp420.99MB

2. PyTorch Fundamentals/1. Why Use Machine Learning or Deep Learning.mp413.8MB

2. PyTorch Fundamentals/10. How To and How Not To Approach This Course.mp437.74MB

2. PyTorch Fundamentals/11. Important Resources For This Course.mp458.31MB

2. PyTorch Fundamentals/12. Getting Setup to Write PyTorch Code.mp470MB

2. PyTorch Fundamentals/13. Introduction to PyTorch Tensors.mp494MB

2. PyTorch Fundamentals/14. Creating Random Tensors in PyTorch.mp486.42MB

2. PyTorch Fundamentals/15. Creating Tensors With Zeros and Ones in PyTorch.mp424.56MB

2. PyTorch Fundamentals/16. Creating a Tensor Range and Tensors Like Other Tensors.mp432.59MB

2. PyTorch Fundamentals/17. Dealing With Tensor Data Types.mp481.4MB

2. PyTorch Fundamentals/18. Getting Tensor Attributes.mp466.44MB

2. PyTorch Fundamentals/19. Manipulating Tensors (Tensor Operations).mp439.7MB

2. PyTorch Fundamentals/2. The Number 1 Rule of Machine Learning and What Is Deep Learning Good For.mp435.34MB

2. PyTorch Fundamentals/20. Matrix Multiplication (Part 1).mp477.8MB

2. PyTorch Fundamentals/21. Matrix Multiplication (Part 2) The Two Main Rules of Matrix Multiplication.mp457.78MB

2. PyTorch Fundamentals/22. Matrix Multiplication (Part 3) Dealing With Tensor Shape Errors.mp497.35MB

2. PyTorch Fundamentals/23. Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation).mp448.14MB

2. PyTorch Fundamentals/24. Finding The Positional Min and Max of Tensors.mp424.5MB

2. PyTorch Fundamentals/25. Reshaping, Viewing and Stacking Tensors.mp4103.95MB

2. PyTorch Fundamentals/26. Squeezing, Unsqueezing and Permuting Tensors.mp488.41MB

2. PyTorch Fundamentals/27. Selecting Data From Tensors (Indexing).mp456.96MB

2. PyTorch Fundamentals/28. PyTorch Tensors and NumPy.mp459.78MB

2. PyTorch Fundamentals/29. PyTorch Reproducibility (Taking the Random Out of Random).mp495.11MB

2. PyTorch Fundamentals/3. Machine Learning vs. Deep Learning.mp455.3MB

2. PyTorch Fundamentals/30. Different Ways of Accessing a GPU in PyTorch.mp4113.01MB

2. PyTorch Fundamentals/31. Setting up Device-Agnostic Code and Putting Tensors On and Off the GPU.mp464.51MB

2. PyTorch Fundamentals/32. PyTorch Fundamentals Exercises and Extra-Curriculum.mp456.76MB

2. PyTorch Fundamentals/4. Anatomy of Neural Networks.mp470.32MB

2. PyTorch Fundamentals/5. Different Types of Learning Paradigms.mp427.05MB

2. PyTorch Fundamentals/6. What Can Deep Learning Be Used For.mp443.2MB

2. PyTorch Fundamentals/7. What Is and Why PyTorch.mp4113.56MB

2. PyTorch Fundamentals/8. What Are Tensors.mp424.99MB

2. PyTorch Fundamentals/9. What We Are Going To Cover With PyTorch.mp450.45MB

3. PyTorch Workflow/1. Introduction and Where You Can Get Help.mp428.6MB

3. PyTorch Workflow/10. Making Predictions With Our Random Model Using Inference Mode.mp4107.03MB

3. PyTorch Workflow/11. Training a Model Intuition (The Things We Need).mp469.5MB

3. PyTorch Workflow/12. Setting Up an Optimizer and a Loss Function.mp4116MB

3. PyTorch Workflow/13. PyTorch Training Loop Steps and Intuition.mp4128.78MB

3. PyTorch Workflow/14. Writing Code for a PyTorch Training Loop.mp483MB

3. PyTorch Workflow/15. Reviewing the Steps in a Training Loop Step by Step.mp4177.46MB

3. PyTorch Workflow/16. Running Our Training Loop Epoch by Epoch and Seeing What Happens.mp4101.7MB

3. PyTorch Workflow/17. Writing Testing Loop Code and Discussing What's Happening Step by Step.mp4135.03MB

3. PyTorch Workflow/18. Reviewing What Happens in a Testing Loop Step by Step.mp4161.56MB

3. PyTorch Workflow/19. Writing Code to Save a PyTorch Model.mp4129.82MB

3. PyTorch Workflow/2. Getting Setup and What We Are Covering.mp469.67MB

3. PyTorch Workflow/20. Writing Code to Load a PyTorch Model.mp479.58MB

3. PyTorch Workflow/21. Setting Up to Practice Everything We Have Done Using Device Agnostic code.mp445.8MB

3. PyTorch Workflow/22. Putting Everything Together (Part 1) Data.mp449.35MB

3. PyTorch Workflow/23. Putting Everything Together (Part 2) Building a Model.mp488.7MB

3. PyTorch Workflow/24. Putting Everything Together (Part 3) Training a Model.mp4103MB

3. PyTorch Workflow/25. Putting Everything Together (Part 4) Making Predictions With a Trained Model.mp450.63MB

3. PyTorch Workflow/26. Putting Everything Together (Part 5) Saving and Loading a Trained Model.mp472.52MB

3. PyTorch Workflow/27. Exercise Imposter Syndrome.mp439.25MB

3. PyTorch Workflow/28. PyTorch Workflow Exercises and Extra-Curriculum.mp449.32MB

3. PyTorch Workflow/3. Creating a Simple Dataset Using the Linear Regression Formula.mp468.65MB

3. PyTorch Workflow/4. Splitting Our Data Into Training and Test Sets.mp465.22MB

3. PyTorch Workflow/5. Building a function to Visualize Our Data.mp461.89MB

3. PyTorch Workflow/6. Creating Our First PyTorch Model for Linear Regression.mp4130.08MB

3. PyTorch Workflow/7. Breaking Down What's Happening in Our PyTorch Linear regression Model.mp462.18MB

3. PyTorch Workflow/8. Discussing Some of the Most Important PyTorch Model Building Classes.mp474.44MB

3. PyTorch Workflow/9. Checking Out the Internals of Our PyTorch Model.mp4102.71MB

4. PyTorch Neural Network Classification/1. Introduction to Machine Learning Classification With PyTorch.mp484.58MB

4. PyTorch Neural Network Classification/10. Loss Function Optimizer and Evaluation Function for Our Classification Network.mp4161.06MB

4. PyTorch Neural Network Classification/11. Going from Model Logits to Prediction Probabilities to Prediction Labels.mp4134.54MB

4. PyTorch Neural Network Classification/12. Coding a Training and Testing Optimization Loop for Our Classification Model.mp4126.75MB

4. PyTorch Neural Network Classification/13. Writing Code to Download a Helper Function to Visualize Our Models Predictions.mp4149.99MB

4. PyTorch Neural Network Classification/14. Discussing Options to Improve a Model.mp480.87MB

4. PyTorch Neural Network Classification/15. Creating a New Model with More Layers and Hidden Units.mp468.81MB

4. PyTorch Neural Network Classification/16. Writing Training and Testing Code to See if Our Upgraded Model Performs Better.mp4118.64MB

4. PyTorch Neural Network Classification/17. Creating a Straight Line Dataset to See if Our Model is Learning Anything.mp461.36MB

4. PyTorch Neural Network Classification/18. Building and Training a Model to Fit on Straight Line Data.mp471.67MB

4. PyTorch Neural Network Classification/19. Evaluating Our Models Predictions on Straight Line Data.mp450.8MB

4. PyTorch Neural Network Classification/2. Classification Problem Example Input and Output Shapes.mp449.97MB

4. PyTorch Neural Network Classification/20. Introducing the Missing Piece for Our Classification Model Non-Linearity.mp496.51MB

4. PyTorch Neural Network Classification/21. Building Our First Neural Network with Non-Linearity.mp492.59MB

4. PyTorch Neural Network Classification/22. Writing Training and Testing Code for Our First Non-Linear Model.mp4150.57MB

4. PyTorch Neural Network Classification/23. Making Predictions with and Evaluating Our First Non-Linear Model.mp453.05MB

4. PyTorch Neural Network Classification/24. Replicating Non-Linear Activation Functions with Pure PyTorch.mp480.74MB

4. PyTorch Neural Network Classification/25. Putting It All Together (Part 1) Building a Multiclass Dataset.mp497.46MB

4. PyTorch Neural Network Classification/26. Creating a Multi-Class Classification Model with PyTorch.mp4107.44MB

4. PyTorch Neural Network Classification/27. Setting Up a Loss Function and Optimizer for Our Multi-Class Model.mp465.06MB

4. PyTorch Neural Network Classification/28. Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model.mp497.05MB

4. PyTorch Neural Network Classification/29. Training a Multi-Class Classification Model and Troubleshooting Code on the Fly.mp4150.09MB

4. PyTorch Neural Network Classification/3. Typical Architecture of a Classification Neural Network (Overview).mp467.05MB

4. PyTorch Neural Network Classification/30. Making Predictions with and Evaluating Our Multi-Class Classification Model.mp477.05MB

4. PyTorch Neural Network Classification/31. Discussing a Few More Classification Metrics.mp497.54MB

4. PyTorch Neural Network Classification/32. PyTorch Classification Exercises and Extra-Curriculum.mp441.47MB

4. PyTorch Neural Network Classification/4. Making a Toy Classification Dataset.mp491.48MB

4. PyTorch Neural Network Classification/5. Turning Our Data into Tensors and Making a Training and Test Split.mp481.06MB

4. PyTorch Neural Network Classification/6. Laying Out Steps for Modelling and Setting Up Device-Agnostic Code.mp431.92MB

4. PyTorch Neural Network Classification/7. Coding a Small Neural Network to Handle Our Classification Data.mp486.85MB

4. PyTorch Neural Network Classification/8. Making Our Neural Network Visual.mp491.27MB

4. PyTorch Neural Network Classification/9. Recreating and Exploring the Insides of Our Model Using nn.Sequential.mp4123.24MB

5. PyTorch Computer Vision/1. What Is a Computer Vision Problem and What We Are Going to Cover.mp4113.67MB

5. PyTorch Computer Vision/10. Creating a Loss Function an Optimizer for Model 0.mp4110.54MB

5. PyTorch Computer Vision/11. Creating a Function to Time Our Modelling Code.mp445.61MB

5. PyTorch Computer Vision/12. Writing Training and Testing Loops for Our Batched Data.mp4157.56MB

5. PyTorch Computer Vision/13. Writing an Evaluation Function to Get Our Models Results.mp4106.79MB

5. PyTorch Computer Vision/14. Setup Device-Agnostic Code for Running Experiments on the GPU.mp444.32MB

5. PyTorch Computer Vision/15. Model 1 Creating a Model with Non-Linear Functions.mp486.39MB

5. PyTorch Computer Vision/16. Mode 1 Creating a Loss Function and Optimizer.mp431.34MB

5. PyTorch Computer Vision/17. Turing Our Training Loop into a Function.mp470.89MB

5. PyTorch Computer Vision/18. Turing Our Testing Loop into a Function.mp450.89MB

5. PyTorch Computer Vision/19. Training and Testing Model 1 with Our Training and Testing Functions.mp4108.44MB

5. PyTorch Computer Vision/2. Computer Vision Input and Output Shapes.mp485.02MB

5. PyTorch Computer Vision/20. Getting a Results Dictionary for Model 1.mp441.35MB

5. PyTorch Computer Vision/21. Model 2 Convolutional Neural Networks High Level Overview.mp494.63MB

5. PyTorch Computer Vision/22. Model 2 Coding Our First Convolutional Neural Network with PyTorch.mp4208.33MB

5. PyTorch Computer Vision/23. Model 2 Breaking Down Conv2D Step by Step.mp4162.72MB

5. PyTorch Computer Vision/24. Model 2 Breaking Down MaxPool2D Step by Step.mp4158.11MB

5. PyTorch Computer Vision/25. Mode 2 Using a Trick to Find the Input and Output Shapes of Each of Our Layers.mp4174.82MB

5. PyTorch Computer Vision/26. Model 2 Setting Up a Loss Function and Optimizer.mp427.88MB

5. PyTorch Computer Vision/27. Model 2 Training Our First CNN and Evaluating Its Results.mp476.79MB

5. PyTorch Computer Vision/28. Comparing the Results of Our Modelling Experiments.mp461.76MB

5. PyTorch Computer Vision/29. Making Predictions on Random Test Samples with the Best Trained Model.mp483.66MB

5. PyTorch Computer Vision/3. What Is a Convolutional Neural Network (CNN).mp455.4MB

5. PyTorch Computer Vision/30. Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them.mp463.49MB

5. PyTorch Computer Vision/31. Making Predictions and Importing Libraries to Plot a Confusion Matrix.mp4160.84MB

5. PyTorch Computer Vision/32. Evaluating Our Best Models Predictions with a Confusion Matrix.mp467.01MB

5. PyTorch Computer Vision/33. Saving and Loading Our Best Performing Model.mp498.16MB

5. PyTorch Computer Vision/34. Recapping What We Have Covered Plus Exercises and Extra-Curriculum.mp481.9MB

5. PyTorch Computer Vision/4. Discussing and Importing the Base Computer Vision Libraries in PyTorch.mp489.2MB

5. PyTorch Computer Vision/5. Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes.mp4154MB

5. PyTorch Computer Vision/6. Visualizing Random Samples of Data.mp468.11MB

5. PyTorch Computer Vision/7. DataLoader Overview Understanding Mini-Batches.mp460.21MB

5. PyTorch Computer Vision/8. Turning Our Datasets Into DataLoaders.mp4100.24MB

5. PyTorch Computer Vision/9. Model 0 Creating a Baseline Model with Two Linear Layers.mp4136.88MB

6. PyTorch Custom Datasets/1. What Is a Custom Dataset and What We Are Going to Cover.mp492.59MB

6. PyTorch Custom Datasets/10. Visualizing a Loaded Image From the Train Dataset.mp476.73MB

6. PyTorch Custom Datasets/11. Turning Our Image Datasets into PyTorch Dataloaders.mp484.33MB

6. PyTorch Custom Datasets/12. Creating a Custom Dataset Class in PyTorch High Level Overview.mp474.7MB

6. PyTorch Custom Datasets/13. Creating a Helper Function to Get Class Names From a Directory.mp479.09MB

6. PyTorch Custom Datasets/14. Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images.mp4176.28MB

6. PyTorch Custom Datasets/15. Compare Our Custom Dataset Class. to the Original Imagefolder Class.mp469.5MB

6. PyTorch Custom Datasets/16. Writing a Helper Function to Visualize Random Images from Our Custom Dataset.mp4131.22MB

6. PyTorch Custom Datasets/17. Turning Our Custom Datasets Into DataLoaders.mp480.62MB

6. PyTorch Custom Datasets/18. Exploring State of the Art Data Augmentation With Torchvision Transforms.mp4166.35MB

6. PyTorch Custom Datasets/19. Building a Baseline Model (Part 1) Loading and Transforming Data.mp477.93MB

6. PyTorch Custom Datasets/2. Importing PyTorch and Setting Up Device Agnostic Code.mp448.97MB

6. PyTorch Custom Datasets/20. Building a Baseline Model (Part 2) Replicating Tiny VGG from Scratch.mp4117.23MB

6. PyTorch Custom Datasets/21. Building a Baseline Model (Part 3)Doing a Forward Pass to Test Our Model Shapes.mp496.5MB

6. PyTorch Custom Datasets/22. Using the Torchinfo Package to Get a Summary of Our Model.mp464.97MB

6. PyTorch Custom Datasets/23. Creating Training and Testing loop Functions.mp4106.17MB

6. PyTorch Custom Datasets/24. Creating a Train Function to Train and Evaluate Our Models.mp4103.47MB

6. PyTorch Custom Datasets/25. Training and Evaluating Model 0 With Our Training Functions.mp489.28MB

6. PyTorch Custom Datasets/26. Plotting the Loss Curves of Model 0.mp489.45MB

6. PyTorch Custom Datasets/27. The Balance Between Overfitting and Underfitting and How to Deal With Each.mp4131.82MB

6. PyTorch Custom Datasets/28. Creating Augmented Training Datasets and DataLoaders for Model 1.mp498.83MB

6. PyTorch Custom Datasets/29. Constructing and Training Model 1.mp460.65MB

6. PyTorch Custom Datasets/3. Downloading a Custom Dataset of Pizza, Steak and Sushi Images.mp4150.96MB

6. PyTorch Custom Datasets/30. Plotting the Loss Curves of Model 1.mp431.69MB

6. PyTorch Custom Datasets/31. Plotting the Loss Curves of All of Our Models Against Each Other.mp489.27MB

6. PyTorch Custom Datasets/32. Predicting on Custom Data (Part 1) Downloading an Image.mp451.66MB

6. PyTorch Custom Datasets/33. Predicting on Custom Data (Part 2) Loading In a Custom Image With PyTorch.mp467.99MB

6. PyTorch Custom Datasets/34. Predicting on Custom Data (Part3)Getting Our Custom Image Into the Right Format.mp4127.06MB

6. PyTorch Custom Datasets/35. Predicting on Custom Data (Part4)Turning Our Models Raw Outputs Into Prediction.mp436.07MB

6. PyTorch Custom Datasets/36. Predicting on Custom Data (Part 5) Putting It All Together.mp4113.03MB

6. PyTorch Custom Datasets/37. Summary of What We Have Covered Plus Exercises and Extra-Curriculum.mp473.32MB

6. PyTorch Custom Datasets/4. Becoming One With the Data (Part 1) Exploring the Data Format.mp487.61MB

6. PyTorch Custom Datasets/5. Becoming One With the Data (Part 2) Visualizing a Random Image.mp4115.34MB

6. PyTorch Custom Datasets/6. Becoming One With the Data (Part 3) Visualizing a Random Image with Matplotlib.mp451.91MB

6. PyTorch Custom Datasets/7. Transforming Data (Part 1) Turning Images Into Tensors.mp481.72MB

6. PyTorch Custom Datasets/8. Transforming Data (Part 2) Visualizing Transformed Images.mp4127.58MB

6. PyTorch Custom Datasets/9. Loading All of Our Images and Turning Them Into Tensors With ImageFolder.mp498.17MB

7. PyTorch Going Modular/1. What Is Going Modular and What We Are Going to Cover.mp4100.12MB

7. PyTorch Going Modular/10. Going Modular Summary, Exercises and Extra-Curriculum.mp480.67MB

7. PyTorch Going Modular/2. Going Modular Notebook (Part 1) Running It End to End.mp4104.92MB

7. PyTorch Going Modular/3. Downloading a Dataset.mp467.64MB

7. PyTorch Going Modular/4. Writing the Outline for Our First Python Script to Setup the Data.mp4156.79MB

7. PyTorch Going Modular/5. Creating a Python Script to Create Our PyTorch DataLoaders.mp4135.14MB

7. PyTorch Going Modular/6. Turning Our Model Building Code into a Python Script.mp4115.13MB

7. PyTorch Going Modular/7. Turning Our Model Training Code into a Python Script.mp480MB

7. PyTorch Going Modular/8. Turning Our Utility Function to Save a Model into a Python Script.mp475.79MB

7. PyTorch Going Modular/9. Creating a Training Script to Train Our Model in One Line of Code.mp4165.52MB

8. PyTorch Transfer Learning/1. Introduction What is Transfer Learning and Why Use It.mp497.26MB

8. PyTorch Transfer Learning/10. Different Kinds of Transfer Learning.mp456.96MB

8. PyTorch Transfer Learning/11. Getting a Summary of the Different Layers of Our Model.mp476.04MB

8. PyTorch Transfer Learning/12. Freezing the Base Layers of Our Model and Updating the Classifier Head.mp4160.67MB

8. PyTorch Transfer Learning/13. Training Our First Transfer Learning Feature Extractor Model.mp474.81MB

8. PyTorch Transfer Learning/14. Plotting the Loss curves of Our Transfer Learning Model.mp458.93MB

8. PyTorch Transfer Learning/15. Outlining the Steps to Make Predictions on the Test Images.mp466.74MB

8. PyTorch Transfer Learning/16. Creating a Function Predict On and Plot Images.mp4101.67MB

8. PyTorch Transfer Learning/17. Making and Plotting Predictions on Test Images.mp478.14MB

8. PyTorch Transfer Learning/18. Making a Prediction on a Custom Image.mp467.83MB

8. PyTorch Transfer Learning/19. Main Takeaways, Exercises and Extra- Curriculum.mp444.43MB

8. PyTorch Transfer Learning/2. Where Can You Find Pretrained Models and What We Are Going to Cover.mp455.85MB

8. PyTorch Transfer Learning/3. Installing the Latest Versions of Torch and Torchvision.mp482.39MB

8. PyTorch Transfer Learning/4. Downloading Our Previously Written Code from Going Modular.mp483.75MB

8. PyTorch Transfer Learning/5. Downloading Pizza, Steak, Sushi Image Data from Github.mp472.17MB

8. PyTorch Transfer Learning/6. Turning Our Data into DataLoaders with Manually Created Transforms.mp4141.48MB

8. PyTorch Transfer Learning/7. Turning Our Data into DataLoaders with Automatic Created Transforms.mp4139.74MB

8. PyTorch Transfer Learning/8. Which Pretrained Model Should You Use.mp4128.78MB

8. PyTorch Transfer Learning/9. Setting Up a Pretrained Model with Torchvision.mp4113.15MB

9. PyTorch Experiment Tracking/1. What Is Experiment Tracking and Why Track Experiments.mp461.86MB

9. PyTorch Experiment Tracking/10. Creating a Function to Create SummaryWriter Instances.mp480.1MB

9. PyTorch Experiment Tracking/11. Adapting Our Train Function to Be Able to Track Multiple Experiments.mp466.54MB

9. PyTorch Experiment Tracking/12. What Experiments Should You Try.mp446.92MB

9. PyTorch Experiment Tracking/13. Discussing the Experiments We Are Going to Try.mp448.3MB

9. PyTorch Experiment Tracking/14. Downloading Datasets for Our Modelling Experiments.mp466.42MB

9. PyTorch Experiment Tracking/15. Turning Our Datasets into DataLoaders Ready for Experimentation.mp478.07MB

9. PyTorch Experiment Tracking/16. Creating Functions to Prepare Our Feature Extractor Models.mp4159.21MB

9. PyTorch Experiment Tracking/17. Coding Out the Steps to Run a Series of Modelling Experiments.mp4127.62MB

9. PyTorch Experiment Tracking/18. Running Eight Different Modelling Experiments in 5 Minutes.mp445.66MB

9. PyTorch Experiment Tracking/19. Viewing Our Modelling Experiments in TensorBoard.mp4140.3MB

9. PyTorch Experiment Tracking/2. Getting Setup by Importing Torch Libraries and Going Modular Code.mp493.39MB

9. PyTorch Experiment Tracking/20. Loading the Best Model and Making Predictions on Random Images from the Test Set.mp499.19MB

9. PyTorch Experiment Tracking/21. Making a Prediction on Our Own Custom Image with the Best Model.mp439.71MB

9. PyTorch Experiment Tracking/22. Main Takeaways, Exercises and Extra- Curriculum.mp443.59MB

9. PyTorch Experiment Tracking/3. Creating a Function to Download Data.mp495.23MB

9. PyTorch Experiment Tracking/4. Turning Our Data into DataLoaders Using Manual Transforms.mp492.72MB

9. PyTorch Experiment Tracking/5. Turning Our Data into DataLoaders Using Automatic Transforms.mp482.01MB

9. PyTorch Experiment Tracking/6. Preparing a Pretrained Model for Our Own Problem.mp4113.16MB

9. PyTorch Experiment Tracking/7. Setting Up a Way to Track a Single Model Experiment with TensorBoard.mp4150.28MB

9. PyTorch Experiment Tracking/8. Training a Single Model and Saving the Results to TensorBoard.mp441.79MB

9. PyTorch Experiment Tracking/9. Exploring Our Single Models Results with TensorBoard.mp4116.26MB